Compare the best LLMs in 2026 by benchmark score, price, and access, with picks for reasoning, writing, coding, agents, and everyday work.
Updated July 12, 2026
LLMs are the general-purpose models behind chat, coding, research, and agents. The real decision isn’t which is smartest - it’s matching capability, price, context, and access, on a leaderboard that reshuffles monthly. We ranked the 15 that matter most.
This is the most capable model in this comparison, and it shows most on hard, long-horizon work - though you pay a real premium for it.
Score 100Price License ProprietaryContext 1M
It leads on the hardest reasoning, long autonomous agent runs, and messy multi-step tasks, staying coherent where lighter models drift. A 1M-token context holds an entire codebase or document set at once.
When the task is genuinely hard and the ceiling matters, this is the pick.
The price and latency are the catch: for everyday chat, summaries, or routine coding, you’re paying for headroom you won’t use.
Drop to Opus 4.8 or Sonnet 5 for most of the quality at far lower cost, and save Fable 5 for hard problems.
OpenAI’s newest flagship is a top-tier generalist that reasons cleanly across broad tasks, and it undercuts the very top on price.
Score 73Price License ProprietaryContext 1M
It’s a strong, well-rounded reasoner that handles analysis, writing, and multi-step problems with real polish, and it competes near the top of this list.
For frontier-level general work without paying the absolute premium, this is the sensible high-end default.
It’s very new, so its human-preference track record is thinner than the models just below it - worth a direct check on your own prompts before you commit.
And Claude Fable 5 still pulls ahead on the hardest, longest tasks.
The current Opus is a deep-reasoning workhorse for hard analysis and long agent runs, at a noticeably lower price than the top tier.
Score 70Price License ProprietaryContext 1M
Opus is built for sustained, careful reasoning: untangling ambiguous failures, working through large systems, and running long agent tasks without losing the thread.
A 1M-token context and steady long-horizon behavior make it a dependable default for heavy engineering and research, and it flags its own shaky work more readily than past versions.
If you rank models by raw human preference, note that older Opus releases like 4.7 still sit higher on those leaderboards - 4.8 is the current, supported version, but the shift is real.
For the very hardest work, Fable 5 remains a clear step up.
Grok 4.5 is xAI’s value play - genuinely strong reasoning at a price well below the frontier models, if you can live with a smaller context.
Score 61Price License ProprietaryContext 500k
The appeal is capability per dollar: it reasons well across analysis, coding, and general tasks and lands close to models that cost several times more.
For teams that want strong output without frontier pricing, it’s one of the better balances on this list.
Its context window is half what most rivals here offer, so very long documents or repo-wide runs can hit the wall sooner.
Its human-preference standing is also less established than Gemini 3.1 Pro or Claude Sonnet 5, so test it on your own workload first.
Gemini 3.5 Flash is the one to reach for when speed and volume matter more than squeezing out the last bit of reasoning quality.
Score 52Price License ProprietaryContext 1M
This is a fast, inexpensive model tuned for throughput: high-volume classification, extraction, summarization, and routine chat where latency and cost per call decide the winner.
A 1M-token context lets it chew through long inputs cheaply, which makes it a strong default for pipelines and user-facing features at scale.
It’s a Flash-tier model, so it trails the top picks on the hardest reasoning and multi-step agent work. For deep debugging, tricky analysis, or long autonomous runs, step up to Gemini 3.1 Pro, Claude Opus 4.8, or GPT-5.5.
Gemini 3.1 Pro is a balanced midrange reasoner with a big context and good real-world polish, though it still carries a preview label.
Score 49Price License ProprietaryContext 1M
It’s a dependable all-rounder that people tend to like in practice: clear writing, sound reasoning, and steady multi-step work across a 1M-token context.
It sits in the sweet spot where quality is high enough for most serious tasks but the price stays reasonable, which makes it an easy everyday recommendation.
It’s still a preview release, so behavior and pricing can shift before it’s final - pin versions for anything production-critical.
On the hardest reasoning it trails Claude Opus 4.8 and GPT-5.6 Sol, and Gemini 3.5 Flash is cheaper if you don’t need the depth.
Sonnet 5 is the balanced daily driver in the Claude line - most of the reasoning quality of Opus at a much friendlier price.
Score 47Price License ProprietaryContext 1M
This is the strongest everyday-work candidate here: strong reasoning, clean writing, and solid coding without the top tier’s premium. A 1M-token context handles long documents and codebases, and it stays fast in interactive use.
For most people, it’s the sensible default.
It’s not a top-preference winner, so on the hardest reasoning and longest agent runs it gives ground to Claude Opus 4.8 and Claude Fable 5.
If your work is routinely at that difficulty, pay up for one of those; otherwise Sonnet 5 is hard to beat.
GLM-5.2 is the strongest open-weight option here and priced like a budget model, but “open” doesn’t mean you’ll run it on your own laptop.
Score 46Price License Open weightContext 1M
It’s the highest-scoring open-weight model on this list, close to solid midrange proprietary picks while costing less. Open weights let you route it through whichever host is cheapest or fits your compliance needs, and a 1M-token context covers long inputs.
For frontier-adjacent capability without proprietary lock-in, this is the one.
The catch is what “open weight” actually buys you: the model is large enough that running it means real self-hosting infrastructure, not a workstation.
Most people will end up calling a hosted API, and on peak capability it trails frontier picks like Claude Fable 5.
Qwen3.7 Max is a capable, low-cost proprietary challenger - good general reasoning at a price that undercuts most Western flagships.
Score 40Price License ProprietaryContext 1M
It delivers respectable general-purpose reasoning and a large 1M-token context at a notably low price, which makes it a genuine value option for high-volume work.
If cost is a first-order constraint and you still want a proprietary, hosted model with a big context, it earns a look.
It sits mid-pack on capability, so for hard reasoning you’ll do better with Grok 4.5 or Gemini 3.1 Pro.
Access is mainly through Alibaba’s cloud or OpenRouter, which can mean regional and setup friction depending on where you operate.
MiniMax-M3 is an open-weight model built for cheap scale - very low cost per token, with capability that’s fine rather than frontier.
Score 39Price License Open weightContext 1M
The draw is price: it’s one of the cheapest models here, open weight, and backed by a 1M-token context, which makes it attractive for high-volume, cost-sensitive workloads.
For straightforward generation, extraction, and chat at scale, it does the job without much fuss.
Capability is mid-tier, so it’s not for hard reasoning or long agent runs. Despite open weights it’s really a hosted-API play: self-hosting means infrastructure, not a laptop.
For a little more capability, DeepSeek V4 Pro and GLM-5.2 are worth comparing.
DeepSeek V4 Flash is the price floor of this list - astonishingly cheap per token, best aimed at high-volume, lower-stakes work.
Score 30Price License Open weightContext 1M
Nothing here touches it on cost, and it comes with a 1M-token context and open weights.
For massive-volume tasks like bulk classification, extraction, and first-pass drafting, where throughput and spend matter more than peak quality, it’s the obvious budget workhorse.
You get what you pay for on capability: it’s low on this list and not built for hard reasoning, careful coding, or long agent runs.
Step up to DeepSeek V4 Pro, GLM-5.2, or a midrange proprietary model when quality matters more than raw cost.
Gemma 4 31B is the one model here you can realistically run yourself - if you have a high-end machine and accept a real capability drop.
Score 27Price License Open weightContext 262k
This is the genuinely local pick: with a strong workstation or ample GPU memory, you can run it fully offline, with no per-token cost and complete privacy.
Open weights and a 262k context make it a solid base for private, self-contained work.
It’s near the bottom on capability, so keep expectations modest: fine for well-scoped tasks, not for hard reasoning or serious agent work.
And “local” still means real hardware - without it you’re better off with a cheap hosted model like DeepSeek V4 Flash.
Run locally — If you have a high-end machine, you can run it with Ollama after downloading weights from Hugging Face.
Kimi K2.6 is a serviceable open-weight generalist - decent value through a hosted API, but not a top-capability pick.
Score 24Price License Open weightContext 256k
It’s a competent open-weight all-rounder available cheaply through hosted APIs, with a 256k context that covers most single-document and mid-length tasks.
If you want an open-weight model for general work and value matters more than topping the charts, it’s a sensible, low-drama choice.
It’s low on capability here, so it’s not for hard reasoning or long agent runs, and its context trails the 1M-token field. Despite open weights, self-hosting means infrastructure, not a laptop.
GLM-5.2 is the stronger open-weight pick if you can spend a little more.
DeepSeek V4 Pro aims for real reasoning quality at a rock-bottom price, and mostly gets there - just don’t expect frontier-level output.
Score 20Price License Open weightContext 1M
It’s the more capable DeepSeek tier: coherent reasoning, a 1M-token context, and a price that stays very low.
For budget-conscious work that still needs real reasoning and long-context handling, not just cheap bulk output, it’s a strong value pick, usable through a hosted app or API.
It ranks low on our combined score here, largely because human-preference results are softer than its raw reasoning suggests - so judge it on your own tasks.
Despite open weights it’s a hosted-API play in practice. For more capability, GLM-5.2 and midrange proprietary models pull ahead.
Access: First decide whether you want the model in an app, through an API, or running locally, because that choice drives cost, privacy, latency, and setup work more than small capability gaps do. Among the main picks, Gemma 4 31B is the cleanest run-it-yourself option, and only on a high-end machine. gpt-oss-120b and Llama 4 Scout are worth checking as open-weight alternatives, but they are not stronger overall recommendations here. The other highlighted open-weight models are “open” but need self-hosting infrastructure, so in practice you’re calling a hosted API just like a proprietary one.
Quality: Our score is a single 0-100 number that blends two respected public signals - the Artificial Analysis Intelligence Index, which measures reasoning and task benchmarks, and Arena Text Overall, which measures head-to-head human preference. It’s a good broad gauge of current capability, but it won’t predict every prompt, so use it to build a shortlist and then test the top two or three on your own work.
Price: We compare blended cost per million tokens, which folds input and output into one number for an apples-to-apples view. Prices here span more than a hundredfold, so once two models both clear your quality bar, cost usually decides.
Context window: This is how much text the model can weigh at once. A 1M-token window comfortably holds a large codebase or a stack of documents; the 256k-500k models are fine for most single-document and chat work but can force you to chunk very long inputs. Match the window to your longest realistic input, not the biggest number on the page.
GPT-5.6 Terra(OpenAI) — The mid-tier GPT-5.6 - good value, but Sol is the stronger flagship.GPT-5.6 Luna(OpenAI) — The cheapest GPT-5.6 tier - fine, but not a standout pick.Claude Opus 4.7(Anthropic) — Older Opus that still tops preference charts; 4.8 is the current version.Claude Opus 4.6(Anthropic) — Another strong older Opus, now superseded by newer Claude releases.Muse Spark(Meta) — Promising Meta benchmark signal, but access and pricing remain too unclear.GPT-5.4(OpenAI) — A capable earlier GPT, now superseded by GPT-5.5 and 5.6.Grok 4.20(xAI) — Strong on human preference, but those scores don’t transfer to Grok 4.5.GPT-5.3 Codex(OpenAI) — A coding-specialized GPT, better matched to a dedicated coding list.gpt-oss-120b(OpenAI) — OpenAI’s open-weight option, but unscored on the benchmarks used here.Llama 4 Scout(Meta) — A major open-weight baseline; realistic local use needs high-end hardware.
Claude Fable 5, on raw capability. It tops our combined score and pulls ahead most on hard, long-horizon work. But it’s the priciest option here, and for a lot of tasks you won’t notice the gap over Claude Opus 4.8, GPT-5.6 Sol, or Claude Sonnet 5 - each a fraction of the cost.
What is the best LLM for most people?
Claude Sonnet 5. It gives you most of the top tier’s quality - strong reasoning, clean writing, a 1M-token context - at a mainstream price, and it stays fast in interactive use. Gemini 3.1 Pro and GPT-5.5 are the close alternatives worth comparing.
What is the best open-weight LLM?
GLM-5.2 is the strongest open-weight pick here, close to solid midrange proprietary models while costing less. Just remember “open weight” means you can host it or use a provider, not that you’ll run it on a laptop - it needs real self-hosting infrastructure. For lower cost, DeepSeek V4 Pro is the next step down.
What is the best LLM you can run locally?
Among the main picks, Gemma 4 31B is the cleanest local choice, and only on a high-end machine with a strong GPU or ample memory. You trade capability for offline use, privacy, and zero per-token cost. gpt-oss-120b and Llama 4 Scout are also worth checking as open-weight alternatives, but the bigger open models here need self-hosting infrastructure rather than a normal machine.
Is Claude better than ChatGPT?
Those are apps, not models, and the honest answer depends on which model you run inside them. Claude Fable 5 leads our score, but GPT-5.6 Sol is right behind and strong across broad tasks. Pick by the specific model and your workload, not the brand, and test both on your own prompts.
Are LLM benchmarks reliable for real-world use?
They’re a good starting filter, not a verdict. Our score blends reasoning benchmarks with head-to-head human preference, which captures broad capability well but can’t predict how a model handles your exact prompts, domain, or tools. Use the score to shortlist, then test the top two or three on your real work.
Should you choose by score, price, context window, or access?
Access first: app, API, or local changes cost, privacy, and setup more than small score gaps. Then take the cheapest model that clears your quality bar - prices here vary more than a hundredfold. Treat context window as a gate, matching it to your longest realistic input.
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depth.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-gemini-3-1-pro-how-to-access`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-gemini-3-1-pro-how-to-access`,children:[_jsx(`span`,{children:`How to access`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsxs(`li`,{children:[_jsx(`strong`,{children:`App`}),` — Available in `,_jsx(`a`,{href:`https://gemini.google.com`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Gemini`}),`.`]}),_jsxs(`li`,{children:[_jsx(`strong`,{children:`API`}),` — Accessible via `,_jsx(`a`,{href:`https://ai.google.dev/gemini-api/docs/models/gemini-3.1-pro-preview`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Gemini API`}),` and `,_jsx(`a`,{href:`https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/3-1-pro`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Gemini Enterprise Agent 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`,_jsx(`strong`,{children:`1M`})]})]}),_jsxs(`div`,{className:`uai-itemcard-details-group`,children:[_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-claude-sonnet-5-strengths`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-claude-sonnet-5-strengths`,children:[_jsx(`span`,{children:`Strengths`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`This is the strongest everyday-work candidate here: strong reasoning, clean writing, and solid coding without the top tier’s premium. A 1M-token context handles long documents and codebases, and it stays fast in interactive use.`}),_jsx(`li`,{children:`For most people, it’s the sensible default.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-claude-sonnet-5-tradeoffs`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-claude-sonnet-5-tradeoffs`,children:[_jsx(`span`,{children:`Tradeoffs`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It’s not a top-preference winner, so on the hardest reasoning and longest agent runs it gives ground to Claude Opus 4.8 and Claude Fable 5.`}),_jsx(`li`,{children:`If your work is routinely at that difficulty, pay up for one of those; otherwise Sonnet 5 is hard to 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`,_jsx(`a`,{href:`https://platform.claude.com/docs/en/about-claude/models/overview`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Claude API`}),`.`]})]})})]})]})]})]}),`\n`,_jsxs(`div`,{className:`uai-itemcard`,role:`article`,children:[_jsxs(`div`,{className:`uai-itemcard-head`,children:[_jsx(`span`,{className:`uai-itemcard-icon`,children:_jsx(`img`,{src:`https://mintcdn.com/usefulai/sV7VJe4pqO2Le0pu/images/icons/144/z.ai.png?fit=max\u0026auto=format\u0026n=sV7VJe4pqO2Le0pu\u0026q=85\u0026s=d720f43ee25f6663eb6cb5ad076ac3ad`,alt:``,noZoom:true,loading:`lazy`,\"data-og-width\":`144`,width:`144`,\"data-og-height\":`144`,height:`144`,\"data-path\":`images/icons/144/z.ai.png`,\"data-optimize\":`true`,\"data-opv\":`3`,srcset:`https://mintcdn.com/usefulai/sV7VJe4pqO2Le0pu/images/icons/144/z.ai.png?w=280\u0026fit=max\u0026auto=format\u0026n=sV7VJe4pqO2Le0pu\u0026q=85\u0026s=49a43a5b6f07873108c92d577b783c5b 280w, 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2500w`})}),_jsxs(`div`,{className:`uai-itemcard-identity`,children:[_jsxs(`div`,{className:`uai-itemcard-row uai-itemcard-row--title`,children:[_jsx(Heading,{level:`2`,id:`glm-5-2`,children:_jsx(_components.a,{href:`https://docs.z.ai/guides/llm/glm-5.2`,children:`GLM-5.2`})}),_jsx(`span`,{className:`uai-itemcard-byline`,children:`Z.ai`})]}),_jsx(`div`,{className:`uai-itemcard-row`,children:_jsx(`span`,{className:`uai-itemcard-note uai-itemcard-note--blue`,children:`Top open-weight capability`})})]}),_jsx(`div`,{className:`uai-itemcard-end`,children:_jsx(`a`,{href:`https://docs.z.ai/guides/llm/glm-5.2`,target:`_blank`,rel:`noreferrer`,\"aria-label\":`Visit GLM-5.2`,className:`uai-itemcard-cta uai-itemcard-cta--blue mint-no-underline`,children:`Visit Z.ai`})})]}),_jsxs(`div`,{className:`uai-itemcard-body`,children:[_jsx(`div`,{className:`uai-itemcard-summary`,children:_jsx(_components.p,{children:`GLM-5.2 is the strongest open-weight option here and priced like a budget model, but “open” doesn’t mean you’ll run it on your own laptop.`})}),_jsxs(`div`,{className:`uai-itemcard-facts`,\"aria-label\":`GLM-5.2 facts`,children:[_jsxs(`span`,{children:[`Score `,_jsx(`strong`,{children:`46`})]}),_jsxs(`span`,{children:[`Price `,_jsx(`strong`,{children:`$0.90 / 1M`})]}),_jsxs(`span`,{children:[`License `,_jsx(`span`,{className:`uai-badge uai-badge--emerald`,children:`Open weight`})]}),_jsxs(`span`,{children:[`Context `,_jsx(`strong`,{children:`1M`})]})]}),_jsxs(`div`,{className:`uai-itemcard-details-group`,children:[_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-glm-5-2-strengths`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-glm-5-2-strengths`,children:[_jsx(`span`,{children:`Strengths`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It’s the highest-scoring open-weight model on this list, close to solid midrange proprietary picks while costing less. Open weights let you route it through whichever host is cheapest or fits your compliance needs, and a 1M-token context covers long inputs.`}),_jsx(`li`,{children:`For frontier-adjacent capability without proprietary lock-in, this is the one.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-glm-5-2-tradeoffs`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-glm-5-2-tradeoffs`,children:[_jsx(`span`,{children:`Tradeoffs`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`The catch is what “open weight” actually buys you: the model is large enough that running it means real self-hosting infrastructure, not a workstation.`}),_jsx(`li`,{children:`Most people will end up calling a hosted API, and on peak capability it trails frontier picks like Claude Fable 5.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-glm-5-2-how-to-access`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-glm-5-2-how-to-access`,children:[_jsx(`span`,{children:`How to access`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsxs(`li`,{children:[_jsx(`strong`,{children:`API`}),` — Accessible via `,_jsx(`a`,{href:`https://docs.z.ai/guides/llm/glm-5.2`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Z.ai API`}),` and `,_jsx(`a`,{href:`https://openrouter.ai/z-ai/glm-5.2`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`OpenRouter`}),`.`]}),_jsxs(`li`,{children:[_jsx(`strong`,{children:`Run locally`}),` — Open weights are available from `,_jsx(`a`,{href:`https://huggingface.co/zai-org/GLM-5.2`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Hugging Face`}),`, but in practice this needs self-hosting infrastructure, not a local machine.`]})]})})]})]})]})]}),`\n`,_jsxs(`div`,{className:`uai-itemcard`,role:`article`,children:[_jsxs(`div`,{className:`uai-itemcard-head`,children:[_jsx(`span`,{className:`uai-itemcard-icon`,children:_jsx(`img`,{src:`https://mintcdn.com/usefulai/Te6KzZ86-OxPuEC2/images/icons/144/qwen.ai.png?fit=max\u0026auto=format\u0026n=Te6KzZ86-OxPuEC2\u0026q=85\u0026s=77ec239e207f3d7865895d11af129b39`,alt:``,noZoom:true,loading:`lazy`,\"data-og-width\":`144`,width:`144`,\"data-og-height\":`144`,height:`144`,\"data-path\":`images/icons/144/qwen.ai.png`,\"data-optimize\":`true`,\"data-opv\":`3`,srcset:`https://mintcdn.com/usefulai/Te6KzZ86-OxPuEC2/images/icons/144/qwen.ai.png?w=280\u0026fit=max\u0026auto=format\u0026n=Te6KzZ86-OxPuEC2\u0026q=85\u0026s=32e94dba961bed1b459b8b7308211294 280w, https://mintcdn.com/usefulai/Te6KzZ86-OxPuEC2/images/icons/144/qwen.ai.png?w=560\u0026fit=max\u0026auto=format\u0026n=Te6KzZ86-OxPuEC2\u0026q=85\u0026s=a07b881a2f4e86a1eb1b23d76779b135 560w, 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uai-itemcard-row--title`,children:[_jsx(Heading,{level:`2`,id:`qwen3-7-max`,children:_jsx(_components.a,{href:`https://qwen.ai/blog?id=qwen3.7`,children:`Qwen3.7 Max`})}),_jsx(`span`,{className:`uai-itemcard-byline`,children:`Alibaba`})]}),_jsx(`div`,{className:`uai-itemcard-row`,children:_jsx(`span`,{className:`uai-itemcard-note uai-itemcard-note--blue`,children:`Low-cost proprietary reasoning`})})]}),_jsx(`div`,{className:`uai-itemcard-end`,children:_jsx(`a`,{href:`https://qwen.ai/blog?id=qwen3.7`,target:`_blank`,rel:`noreferrer`,\"aria-label\":`Visit Qwen3.7 Max`,className:`uai-itemcard-cta uai-itemcard-cta--blue mint-no-underline`,children:`Visit Alibaba`})})]}),_jsxs(`div`,{className:`uai-itemcard-body`,children:[_jsx(`div`,{className:`uai-itemcard-summary`,children:_jsx(_components.p,{children:`Qwen3.7 Max is a capable, low-cost proprietary challenger - good general reasoning at a price that undercuts most Western flagships.`})}),_jsxs(`div`,{className:`uai-itemcard-facts`,\"aria-label\":`Qwen3.7 Max facts`,children:[_jsxs(`span`,{children:[`Score `,_jsx(`strong`,{children:`40`})]}),_jsxs(`span`,{children:[`Price `,_jsx(`strong`,{children:`$1.43 / 1M`})]}),_jsxs(`span`,{children:[`License `,_jsx(`span`,{className:`uai-badge uai-badge--zinc`,children:`Proprietary`})]}),_jsxs(`span`,{children:[`Context `,_jsx(`strong`,{children:`1M`})]})]}),_jsxs(`div`,{className:`uai-itemcard-details-group`,children:[_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-qwen3-7-max-strengths`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-qwen3-7-max-strengths`,children:[_jsx(`span`,{children:`Strengths`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It delivers respectable general-purpose reasoning and a large 1M-token context at a notably low price, which makes it a genuine value option for high-volume work.`}),_jsx(`li`,{children:`If cost is a first-order constraint and you still want a proprietary, hosted model with a big context, it earns a look.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-qwen3-7-max-tradeoffs`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-qwen3-7-max-tradeoffs`,children:[_jsx(`span`,{children:`Tradeoffs`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It sits mid-pack on capability, so for hard reasoning you’ll do better with Grok 4.5 or Gemini 3.1 Pro.`}),_jsx(`li`,{children:`Access is mainly through Alibaba’s cloud or OpenRouter, which can mean regional and setup friction depending on where you operate.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-qwen3-7-max-how-to-access`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-qwen3-7-max-how-to-access`,children:[_jsx(`span`,{children:`How to access`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsx(`ul`,{children:_jsxs(`li`,{children:[_jsx(`strong`,{children:`API`}),` — Accessible via `,_jsx(`a`,{href:`https://www.alibabacloud.com/help/en/model-studio/models`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Alibaba Cloud Model Studio`}),` and `,_jsx(`a`,{href:`https://openrouter.ai/qwen/qwen3.7-max`,target:`_blank`,rel:`noreferrer`,className:`mint-underline 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2500w`})}),_jsxs(`div`,{className:`uai-itemcard-identity`,children:[_jsxs(`div`,{className:`uai-itemcard-row uai-itemcard-row--title`,children:[_jsx(Heading,{level:`2`,id:`minimax-m3`,children:_jsx(_components.a,{href:`https://www.minimax.io/blog/minimax-m3`,children:`MiniMax-M3`})}),_jsx(`span`,{className:`uai-itemcard-byline`,children:`MiniMax`})]}),_jsx(`div`,{className:`uai-itemcard-row`,children:_jsx(`span`,{className:`uai-itemcard-note uai-itemcard-note--blue`,children:`Rock-bottom open-weight cost`})})]}),_jsx(`div`,{className:`uai-itemcard-end`,children:_jsx(`a`,{href:`https://www.minimax.io/blog/minimax-m3`,target:`_blank`,rel:`noreferrer`,\"aria-label\":`Visit MiniMax-M3`,className:`uai-itemcard-cta uai-itemcard-cta--blue mint-no-underline`,children:`Visit MiniMax`})})]}),_jsxs(`div`,{className:`uai-itemcard-body`,children:[_jsx(`div`,{className:`uai-itemcard-summary`,children:_jsx(_components.p,{children:`MiniMax-M3 is an open-weight model built for cheap scale - very low cost per token, with capability that’s fine rather than frontier.`})}),_jsxs(`div`,{className:`uai-itemcard-facts`,\"aria-label\":`MiniMax-M3 facts`,children:[_jsxs(`span`,{children:[`Score `,_jsx(`strong`,{children:`39`})]}),_jsxs(`span`,{children:[`Price `,_jsx(`strong`,{children:`$0.22 / 1M`})]}),_jsxs(`span`,{children:[`License `,_jsx(`span`,{className:`uai-badge uai-badge--emerald`,children:`Open weight`})]}),_jsxs(`span`,{children:[`Context `,_jsx(`strong`,{children:`1M`})]})]}),_jsxs(`div`,{className:`uai-itemcard-details-group`,children:[_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-minimax-m3-strengths`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-minimax-m3-strengths`,children:[_jsx(`span`,{children:`Strengths`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`The draw is price: it’s one of the cheapest models here, open weight, and backed by a 1M-token context, which makes it attractive for high-volume, cost-sensitive workloads.`}),_jsx(`li`,{children:`For straightforward generation, extraction, and chat at scale, it does the job without much fuss.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-minimax-m3-tradeoffs`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-minimax-m3-tradeoffs`,children:[_jsx(`span`,{children:`Tradeoffs`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`Capability is mid-tier, so it’s not for hard reasoning or long agent runs. Despite open weights it’s really a hosted-API play: self-hosting means infrastructure, not a laptop.`}),_jsx(`li`,{children:`For a little more capability, DeepSeek V4 Pro and GLM-5.2 are worth comparing.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-minimax-m3-how-to-access`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-minimax-m3-how-to-access`,children:[_jsx(`span`,{children:`How to access`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsxs(`li`,{children:[_jsx(`strong`,{children:`API`}),` — Accessible via `,_jsx(`a`,{href:`https://openrouter.ai/minimax/minimax-m3`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`OpenRouter`}),`.`]}),_jsxs(`li`,{children:[_jsx(`strong`,{children:`Run locally`}),` — Open weights are available from `,_jsx(`a`,{href:`https://huggingface.co/MiniMaxAI/MiniMax-M3`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Hugging Face`}),`, but in practice this needs self-hosting infrastructure, not a local machine.`]})]})})]})]})]})]}),`\n`,_jsxs(`div`,{className:`uai-itemcard`,role:`article`,children:[_jsxs(`div`,{className:`uai-itemcard-head`,children:[_jsx(`span`,{className:`uai-itemcard-icon`,children:_jsx(`img`,{src:`https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=f47aa3b25f028302bb1e83f032950b2d`,alt:``,noZoom:true,loading:`lazy`,\"data-og-width\":`144`,width:`144`,\"data-og-height\":`144`,height:`144`,\"data-path\":`images/icons/144/deepseek.com.png`,\"data-optimize\":`true`,\"data-opv\":`3`,srcset:`https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?w=280\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=23f376acd890ae1fe5c0a3826dbb4260 280w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?w=560\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=2ac6eabacb19ed36d878d9495af94bdc 560w, 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uai-itemcard-row--title`,children:[_jsx(Heading,{level:`2`,id:`deepseek-v4-flash`,children:_jsx(_components.a,{href:`https://api-docs.deepseek.com/news/news260424`,children:`DeepSeek V4 Flash`})}),_jsx(`span`,{className:`uai-itemcard-byline`,children:`DeepSeek`})]}),_jsx(`div`,{className:`uai-itemcard-row`,children:_jsx(`span`,{className:`uai-itemcard-note uai-itemcard-note--blue`,children:`Cheapest high-volume API`})})]}),_jsx(`div`,{className:`uai-itemcard-end`,children:_jsx(`a`,{href:`https://api-docs.deepseek.com/news/news260424`,target:`_blank`,rel:`noreferrer`,\"aria-label\":`Visit DeepSeek V4 Flash`,className:`uai-itemcard-cta uai-itemcard-cta--blue mint-no-underline`,children:`Visit DeepSeek`})})]}),_jsxs(`div`,{className:`uai-itemcard-body`,children:[_jsx(`div`,{className:`uai-itemcard-summary`,children:_jsx(_components.p,{children:`DeepSeek V4 Flash is the price floor of this list - astonishingly cheap per token, best aimed at high-volume, lower-stakes work.`})}),_jsxs(`div`,{className:`uai-itemcard-facts`,\"aria-label\":`DeepSeek V4 Flash facts`,children:[_jsxs(`span`,{children:[`Score `,_jsx(`strong`,{children:`30`})]}),_jsxs(`span`,{children:[`Price `,_jsx(`strong`,{children:`$0.06 / 1M`})]}),_jsxs(`span`,{children:[`License `,_jsx(`span`,{className:`uai-badge uai-badge--emerald`,children:`Open weight`})]}),_jsxs(`span`,{children:[`Context `,_jsx(`strong`,{children:`1M`})]})]}),_jsxs(`div`,{className:`uai-itemcard-details-group`,children:[_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-deepseek-v4-flash-strengths`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-deepseek-v4-flash-strengths`,children:[_jsx(`span`,{children:`Strengths`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`Nothing here touches it on cost, and it comes with a 1M-token context and open weights.`}),_jsx(`li`,{children:`For massive-volume tasks like bulk classification, extraction, and first-pass drafting, where throughput and spend matter more than peak quality, it’s the obvious budget workhorse.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-deepseek-v4-flash-tradeoffs`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-deepseek-v4-flash-tradeoffs`,children:[_jsx(`span`,{children:`Tradeoffs`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`You get what you pay for on capability: it’s low on this list and not built for hard reasoning, careful coding, or long agent runs.`}),_jsx(`li`,{children:`Step up to DeepSeek V4 Pro, GLM-5.2, or a midrange proprietary model when quality matters more than raw cost.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-deepseek-v4-flash-how-to-access`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-deepseek-v4-flash-how-to-access`,children:[_jsx(`span`,{children:`How to access`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsxs(`li`,{children:[_jsx(`strong`,{children:`App`}),` — Available in `,_jsx(`a`,{href:`https://chat.deepseek.com`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`DeepSeek Chat`}),`.`]}),_jsxs(`li`,{children:[_jsx(`strong`,{children:`API`}),` — Accessible via `,_jsx(`a`,{href:`https://api-docs.deepseek.com/news/news260424`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`DeepSeek API`}),` and `,_jsx(`a`,{href:`https://openrouter.ai/deepseek/deepseek-v4-flash`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`OpenRouter`}),`.`]}),_jsxs(`li`,{children:[_jsx(`strong`,{children:`Run locally`}),` — Open weights are available from `,_jsx(`a`,{href:`https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Hugging Face`}),`, but in practice this needs self-hosting infrastructure, not a local machine.`]})]})})]})]})]})]}),`\n`,_jsxs(`div`,{className:`uai-itemcard`,role:`article`,children:[_jsxs(`div`,{className:`uai-itemcard-head`,children:[_jsx(`span`,{className:`uai-itemcard-icon`,children:_jsx(`img`,{src:`https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/google.com.png?fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=11e0c725f841c45443116a184b63beac`,alt:``,noZoom:true,loading:`lazy`,\"data-og-width\":`144`,width:`144`,\"data-og-height\":`144`,height:`144`,\"data-path\":`images/icons/144/google.com.png`,\"data-optimize\":`true`,\"data-opv\":`3`,srcset:`https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/google.com.png?w=280\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=394ab19e17b17900b398f20fb76e785e 280w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/google.com.png?w=560\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=468e165516e54d5f4206d48e53a62170 560w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/google.com.png?w=840\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=000df80339a683c15e6f5cb7acab45bd 840w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/google.com.png?w=1100\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=85e5984f09b272e973b117aea11a1baa 1100w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/google.com.png?w=1650\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=4cc454acd3bfd390f28bd0cb9c433b26 1650w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/google.com.png?w=2500\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=992661171eba0e955bee8ff361f0b971 2500w`})}),_jsxs(`div`,{className:`uai-itemcard-identity`,children:[_jsxs(`div`,{className:`uai-itemcard-row uai-itemcard-row--title`,children:[_jsx(Heading,{level:`2`,id:`gemma-4-31b`,children:_jsx(_components.a,{href:`https://huggingface.co/google/gemma-4-31B-it`,children:`Gemma 4 31B`})}),_jsx(`span`,{className:`uai-itemcard-byline`,children:`Google`})]}),_jsx(`div`,{className:`uai-itemcard-row`,children:_jsx(`span`,{className:`uai-itemcard-note uai-itemcard-note--blue`,children:`Local on a workstation`})})]}),_jsx(`div`,{className:`uai-itemcard-end`,children:_jsx(`a`,{href:`https://huggingface.co/google/gemma-4-31B-it`,target:`_blank`,rel:`noreferrer`,\"aria-label\":`View Gemma 4 31B on Hugging Face`,className:`uai-itemcard-cta uai-itemcard-cta--blue mint-no-underline`,children:`View on Hugging Face`})})]}),_jsxs(`div`,{className:`uai-itemcard-body`,children:[_jsx(`div`,{className:`uai-itemcard-summary`,children:_jsx(_components.p,{children:`Gemma 4 31B is the one model here you can realistically run yourself - if you have a high-end machine and accept a real capability drop.`})}),_jsxs(`div`,{className:`uai-itemcard-facts`,\"aria-label\":`Gemma 4 31B facts`,children:[_jsxs(`span`,{children:[`Score `,_jsx(`strong`,{children:`27`})]}),_jsxs(`span`,{children:[`Price `,_jsx(`strong`,{children:`$0.17 / 1M`})]}),_jsxs(`span`,{children:[`License `,_jsx(`span`,{className:`uai-badge uai-badge--emerald`,children:`Open weight`})]}),_jsxs(`span`,{children:[`Context `,_jsx(`strong`,{children:`262k`})]})]}),_jsxs(`div`,{className:`uai-itemcard-details-group`,children:[_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-gemma-4-31b-strengths`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-gemma-4-31b-strengths`,children:[_jsx(`span`,{children:`Strengths`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`This is the genuinely local pick: with a strong workstation or ample GPU memory, you can run it fully offline, with no per-token cost and complete privacy.`}),_jsx(`li`,{children:`Open weights and a 262k context make it a solid base for private, self-contained work.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-gemma-4-31b-tradeoffs`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-gemma-4-31b-tradeoffs`,children:[_jsx(`span`,{children:`Tradeoffs`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It’s near the bottom on capability, so keep expectations modest: fine for well-scoped tasks, not for hard reasoning or serious agent work.`}),_jsx(`li`,{children:`And “local” still means real hardware - without it you’re better off with a cheap hosted model like DeepSeek V4 Flash.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-gemma-4-31b-how-to-access`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-gemma-4-31b-how-to-access`,children:[_jsx(`span`,{children:`How to access`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsx(`ul`,{children:_jsxs(`li`,{children:[_jsx(`strong`,{children:`Run locally`}),` — If you have a high-end machine, you can run it with `,_jsx(`a`,{href:`https://ollama.com/library/gemma4:31b`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Ollama`}),` after downloading weights from `,_jsx(`a`,{href:`https://huggingface.co/google/gemma-4-31B-it`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Hugging Face`}),`.`]})})})]})]})]})]}),`\n`,_jsxs(`div`,{className:`uai-itemcard`,role:`article`,children:[_jsxs(`div`,{className:`uai-itemcard-head`,children:[_jsx(`span`,{className:`uai-itemcard-icon`,children:_jsx(`img`,{src:`https://mintcdn.com/usefulai/52KaILwddzz5TNZ_/images/icons/144/kimi.com.png?fit=max\u0026auto=format\u0026n=52KaILwddzz5TNZ_\u0026q=85\u0026s=8d6772935f3f8299f61c9820fd6d176d`,alt:``,noZoom:true,loading:`lazy`,\"data-og-width\":`144`,width:`144`,\"data-og-height\":`144`,height:`144`,\"data-path\":`images/icons/144/kimi.com.png`,\"data-optimize\":`true`,\"data-opv\":`3`,srcset:`https://mintcdn.com/usefulai/52KaILwddzz5TNZ_/images/icons/144/kimi.com.png?w=280\u0026fit=max\u0026auto=format\u0026n=52KaILwddzz5TNZ_\u0026q=85\u0026s=fdd2e4d4b91a6aba56ddb8bb40d01a3b 280w, https://mintcdn.com/usefulai/52KaILwddzz5TNZ_/images/icons/144/kimi.com.png?w=560\u0026fit=max\u0026auto=format\u0026n=52KaILwddzz5TNZ_\u0026q=85\u0026s=5d494c584b4a30c9a3c3d5257a76feb9 560w, https://mintcdn.com/usefulai/52KaILwddzz5TNZ_/images/icons/144/kimi.com.png?w=840\u0026fit=max\u0026auto=format\u0026n=52KaILwddzz5TNZ_\u0026q=85\u0026s=21831edde1bb3518749d8e176709a744 840w, https://mintcdn.com/usefulai/52KaILwddzz5TNZ_/images/icons/144/kimi.com.png?w=1100\u0026fit=max\u0026auto=format\u0026n=52KaILwddzz5TNZ_\u0026q=85\u0026s=31d9369c86c4e8a1d668a8931c35a05d 1100w, https://mintcdn.com/usefulai/52KaILwddzz5TNZ_/images/icons/144/kimi.com.png?w=1650\u0026fit=max\u0026auto=format\u0026n=52KaILwddzz5TNZ_\u0026q=85\u0026s=ac21bd7a4318f9bd715896fd84bc671d 1650w, https://mintcdn.com/usefulai/52KaILwddzz5TNZ_/images/icons/144/kimi.com.png?w=2500\u0026fit=max\u0026auto=format\u0026n=52KaILwddzz5TNZ_\u0026q=85\u0026s=7a6ec4125fea96a4712e13363ab999b1 2500w`})}),_jsxs(`div`,{className:`uai-itemcard-identity`,children:[_jsxs(`div`,{className:`uai-itemcard-row uai-itemcard-row--title`,children:[_jsx(Heading,{level:`2`,id:`kimi-k2-6`,children:_jsx(_components.a,{href:`https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart`,children:`Kimi K2.6`})}),_jsx(`span`,{className:`uai-itemcard-byline`,children:`Moonshot AI`})]}),_jsx(`div`,{className:`uai-itemcard-row`,children:_jsx(`span`,{className:`uai-itemcard-note uai-itemcard-note--blue`,children:`Open-weight generalist value`})})]}),_jsx(`div`,{className:`uai-itemcard-end`,children:_jsx(`a`,{href:`https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart`,target:`_blank`,rel:`noreferrer`,\"aria-label\":`Visit Kimi K2.6`,className:`uai-itemcard-cta uai-itemcard-cta--blue mint-no-underline`,children:`Visit Moonshot AI`})})]}),_jsxs(`div`,{className:`uai-itemcard-body`,children:[_jsx(`div`,{className:`uai-itemcard-summary`,children:_jsx(_components.p,{children:`Kimi K2.6 is a serviceable open-weight generalist - decent value through a hosted API, but not a top-capability pick.`})}),_jsxs(`div`,{className:`uai-itemcard-facts`,\"aria-label\":`Kimi K2.6 facts`,children:[_jsxs(`span`,{children:[`Score `,_jsx(`strong`,{children:`24`})]}),_jsxs(`span`,{children:[`Price `,_jsx(`strong`,{children:`$0.70 / 1M`})]}),_jsxs(`span`,{children:[`License `,_jsx(`span`,{className:`uai-badge uai-badge--emerald`,children:`Open weight`})]}),_jsxs(`span`,{children:[`Context `,_jsx(`strong`,{children:`256k`})]})]}),_jsxs(`div`,{className:`uai-itemcard-details-group`,children:[_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-kimi-k2-6-strengths`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-kimi-k2-6-strengths`,children:[_jsx(`span`,{children:`Strengths`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It’s a competent open-weight all-rounder available cheaply through hosted APIs, with a 256k context that covers most single-document and mid-length tasks.`}),_jsx(`li`,{children:`If you want an open-weight model for general work and value matters more than topping the charts, it’s a sensible, low-drama choice.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-kimi-k2-6-tradeoffs`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-kimi-k2-6-tradeoffs`,children:[_jsx(`span`,{children:`Tradeoffs`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It’s low on capability here, so it’s not for hard reasoning or long agent runs, and its context trails the 1M-token field. Despite open weights, self-hosting means infrastructure, not a laptop.`}),_jsx(`li`,{children:`GLM-5.2 is the stronger open-weight pick if you can spend a little more.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-kimi-k2-6-how-to-access`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-kimi-k2-6-how-to-access`,children:[_jsx(`span`,{children:`How to access`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsxs(`li`,{children:[_jsx(`strong`,{children:`API`}),` — Accessible via `,_jsx(`a`,{href:`https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Kimi Platform`}),` and `,_jsx(`a`,{href:`https://openrouter.ai/moonshotai/kimi-k2.6`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`OpenRouter`}),`.`]}),_jsxs(`li`,{children:[_jsx(`strong`,{children:`Run locally`}),` — Open weights are available from `,_jsx(`a`,{href:`https://huggingface.co/moonshotai/Kimi-K2.6`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Hugging Face`}),`, but in practice this needs self-hosting infrastructure, not a local machine.`]})]})})]})]})]})]}),`\n`,_jsxs(`div`,{className:`uai-itemcard`,role:`article`,children:[_jsxs(`div`,{className:`uai-itemcard-head`,children:[_jsx(`span`,{className:`uai-itemcard-icon`,children:_jsx(`img`,{src:`https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=f47aa3b25f028302bb1e83f032950b2d`,alt:``,noZoom:true,loading:`lazy`,\"data-og-width\":`144`,width:`144`,\"data-og-height\":`144`,height:`144`,\"data-path\":`images/icons/144/deepseek.com.png`,\"data-optimize\":`true`,\"data-opv\":`3`,srcset:`https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?w=280\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=23f376acd890ae1fe5c0a3826dbb4260 280w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?w=560\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=2ac6eabacb19ed36d878d9495af94bdc 560w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?w=840\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=798309b06a67204274b1216c60d386e9 840w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?w=1100\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=cb7b78df30ac81e3a507d18034198d6c 1100w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?w=1650\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=fcc88a2b79d1927ac305dc547d5614bf 1650w, https://mintcdn.com/usefulai/Ez-pJDkPpztLE7Cr/images/icons/144/deepseek.com.png?w=2500\u0026fit=max\u0026auto=format\u0026n=Ez-pJDkPpztLE7Cr\u0026q=85\u0026s=bdc2feac30e3036c8cec18176526472a 2500w`})}),_jsxs(`div`,{className:`uai-itemcard-identity`,children:[_jsxs(`div`,{className:`uai-itemcard-row uai-itemcard-row--title`,children:[_jsx(Heading,{level:`2`,id:`deepseek-v4-pro`,children:_jsx(_components.a,{href:`https://api-docs.deepseek.com/news/news260424`,children:`DeepSeek V4 Pro`})}),_jsx(`span`,{className:`uai-itemcard-byline`,children:`DeepSeek`})]}),_jsx(`div`,{className:`uai-itemcard-row`,children:_jsx(`span`,{className:`uai-itemcard-note uai-itemcard-note--blue`,children:`Budget quality reasoning`})})]}),_jsx(`div`,{className:`uai-itemcard-end`,children:_jsx(`a`,{href:`https://api-docs.deepseek.com/news/news260424`,target:`_blank`,rel:`noreferrer`,\"aria-label\":`Visit DeepSeek V4 Pro`,className:`uai-itemcard-cta uai-itemcard-cta--blue mint-no-underline`,children:`Visit DeepSeek`})})]}),_jsxs(`div`,{className:`uai-itemcard-body`,children:[_jsx(`div`,{className:`uai-itemcard-summary`,children:_jsx(_components.p,{children:`DeepSeek V4 Pro aims for real reasoning quality at a rock-bottom price, and mostly gets there - just don’t expect frontier-level output.`})}),_jsxs(`div`,{className:`uai-itemcard-facts`,\"aria-label\":`DeepSeek V4 Pro facts`,children:[_jsxs(`span`,{children:[`Score `,_jsx(`strong`,{children:`20`})]}),_jsxs(`span`,{children:[`Price `,_jsx(`strong`,{children:`$0.18 / 1M`})]}),_jsxs(`span`,{children:[`License `,_jsx(`span`,{className:`uai-badge uai-badge--emerald`,children:`Open weight`})]}),_jsxs(`span`,{children:[`Context `,_jsx(`strong`,{children:`1M`})]})]}),_jsxs(`div`,{className:`uai-itemcard-details-group`,children:[_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-deepseek-v4-pro-strengths`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-deepseek-v4-pro-strengths`,children:[_jsx(`span`,{children:`Strengths`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It’s the more capable DeepSeek tier: coherent reasoning, a 1M-token context, and a price that stays very low.`}),_jsx(`li`,{children:`For budget-conscious work that still needs real reasoning and long-context handling, not just cheap bulk output, it’s a strong value pick, usable through a hosted app or API.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-deepseek-v4-pro-tradeoffs`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-deepseek-v4-pro-tradeoffs`,children:[_jsx(`span`,{children:`Tradeoffs`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsx(`li`,{children:`It ranks low on our combined score here, largely because human-preference results are softer than its raw reasoning suggests - so judge it on your own tasks.`}),_jsx(`li`,{children:`Despite open weights it’s a hosted-API play in practice. For more capability, GLM-5.2 and midrange proprietary models pull ahead.`})]})})]}),_jsxs(`div`,{className:`uai-itemcard-details`,children:[_jsx(`input`,{type:`checkbox`,id:`llms-deepseek-v4-pro-how-to-access`,className:`uai-itemcard-details-toggle`}),_jsxs(`label`,{htmlFor:`llms-deepseek-v4-pro-how-to-access`,children:[_jsx(`span`,{children:`How to access`}),_jsx(`span`,{className:`uai-itemcard-details-chevron`})]}),_jsx(`div`,{className:`uai-itemcard-details-body`,children:_jsxs(`ul`,{children:[_jsxs(`li`,{children:[_jsx(`strong`,{children:`App`}),` — Available in `,_jsx(`a`,{href:`https://chat.deepseek.com`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`DeepSeek Chat`}),`.`]}),_jsxs(`li`,{children:[_jsx(`strong`,{children:`API`}),` — Accessible via `,_jsx(`a`,{href:`https://api-docs.deepseek.com/news/news260424`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`DeepSeek API`}),` and `,_jsx(`a`,{href:`https://openrouter.ai/deepseek/deepseek-v4-pro`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`OpenRouter`}),`.`]}),_jsxs(`li`,{children:[_jsx(`strong`,{children:`Run locally`}),` — Open weights are available from `,_jsx(`a`,{href:`https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro`,target:`_blank`,rel:`noreferrer`,className:`mint-underline mint-underline-offset-2`,children:`Hugging Face`}),`, but in practice this needs self-hosting infrastructure, not a local machine.`]})]})})]})]})]})]}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`how-to-choose`,children:`How to Choose`}),`\n`,_jsx(_components.p,{children:`When choosing between these models, consider:`}),`\n`,_jsxs(_components.ul,{children:[`\n`,_jsxs(_components.li,{children:[_jsx(_components.strong,{children:`Access:`}),` First decide whether you want the model in an app, through an API, or running locally, because that choice drives cost, privacy, latency, and setup work more than small capability gaps do. Among the main picks, Gemma 4 31B is the cleanest run-it-yourself option, and only on a high-end machine. gpt-oss-120b and Llama 4 Scout are worth checking as open-weight alternatives, but they are not stronger overall recommendations here. The other highlighted open-weight models are “open” but need self-hosting infrastructure, so in practice you’re calling a hosted API just like a proprietary one.`]}),`\n`,_jsxs(_components.li,{children:[_jsx(_components.strong,{children:`Quality:`}),` Our score is a single 0-100 number that blends two respected public signals - the Artificial Analysis Intelligence Index, which measures reasoning and task benchmarks, and Arena Text Overall, which measures head-to-head human preference. It’s a good broad gauge of current capability, but it won’t predict every prompt, so use it to build a shortlist and then test the top two or three on your own work.`]}),`\n`,_jsxs(_components.li,{children:[_jsx(_components.strong,{children:`Price:`}),` We compare blended cost per million tokens, which folds input and output into one number for an apples-to-apples view. Prices here span more than a hundredfold, so once two models both clear your quality bar, cost usually decides.`]}),`\n`,_jsxs(_components.li,{children:[_jsx(_components.strong,{children:`Context window:`}),` This is how much text the model can weigh at once. A 1M-token window comfortably holds a large codebase or a stack of documents; the 256k-500k models are fine for most single-document and chat work but can force you to chunk very long inputs. Match the window to your longest realistic input, not the biggest number on the page.`]}),`\n`]}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`other-models-we-considered`,children:`Other Models We Considered`}),`\n`,_jsxs(`div`,{className:`not-prose mint-my-4 mint-flex mint-flex-col mint-gap-1.5 uai-article-prose mint-text-zinc-700 dark:mint-text-zinc-300`,children:[_jsxs(`span`,{className:`mint-flex mint-items-baseline mint-gap-2.5`,children:[_jsx(`span`,{\"aria-hidden\":`true`,className:`mint-relative mint--top-0.5 mint-inline-block mint-h-1.5 mint-w-1.5 mint-shrink-0 mint-rounded-full mint-bg-zinc-300 dark:mint-bg-zinc-600`}),_jsxs(`span`,{children:[_jsx(`img`,{src:`https://mintcdn.com/usefulai/C5xOaAf4Os-Vu41o/images/icons/48/openai.com.png?fit=max\u0026auto=format\u0026n=C5xOaAf4Os-Vu41o\u0026q=85\u0026s=21ac965dc6ee5751127e6d435043629b`,alt:``,noZoom:true,className:`mint-relative mint--top-px mint-mr-1 mint-inline mint-h-4 mint-w-4 mint-rounded-sm 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dark:mint-text-white`,children:`GPT-5.6 Luna`}),` `,_jsx(`span`,{className:`uai-ink-muted`,children:`(OpenAI)`}),` — The cheapest GPT-5.6 tier - fine, but not a standout pick.`]})]}),_jsxs(`span`,{className:`mint-flex mint-items-baseline mint-gap-2.5`,children:[_jsx(`span`,{\"aria-hidden\":`true`,className:`mint-relative mint--top-0.5 mint-inline-block mint-h-1.5 mint-w-1.5 mint-shrink-0 mint-rounded-full mint-bg-zinc-300 dark:mint-bg-zinc-600`}),_jsxs(`span`,{children:[_jsx(`img`,{src:`https://mintcdn.com/usefulai/82PG1Up2qz4DPkMj/images/icons/48/anthropic.com.png?fit=max\u0026auto=format\u0026n=82PG1Up2qz4DPkMj\u0026q=85\u0026s=f84edebba0edca3b7e75a740d0fc318e`,alt:``,noZoom:true,className:`mint-relative mint--top-px mint-mr-1 mint-inline mint-h-4 mint-w-4 mint-rounded-sm 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Claude Fable 5
GPT-5.6 Sol
Grok 4.5
Gemini 3.5 Flash
GLM-5.2
Qwen3.7 Max
MiniMax-M3
DeepSeek V4 Flash
Kimi K2.6
