> ## Documentation Index
> Fetch the complete documentation index at: https://usefulai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Best LLMs in 2026

export const models = [{
  rank: 1,
  name: "Claude Fable 5",
  dev: "Anthropic",
  icon: "/images/icons/anthropic.com.png",
  url: "https://www.anthropic.com/news/claude-fable-5-mythos-5",
  bestFor: "Most capable overall",
  score: "100",
  price: "$7.70 / 1M",
  license: "Proprietary",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 2,
  name: "GPT-5.6 Sol",
  dev: "OpenAI",
  icon: "/images/icons/openai.com.png",
  url: "https://openai.com/index/previewing-gpt-5-6-sol/",
  bestFor: "Broad frontier reasoning",
  score: "73",
  price: "$4.35 / 1M",
  license: "Proprietary",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 3,
  name: "Claude Opus 4.8",
  dev: "Anthropic",
  icon: "/images/icons/anthropic.com.png",
  url: "https://www.anthropic.com/news/claude-opus-4-8",
  bestFor: "Deep reasoning and agents",
  score: "70",
  price: "$3.85 / 1M",
  license: "Proprietary",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 4,
  name: "GPT-5.5",
  dev: "OpenAI",
  icon: "/images/icons/openai.com.png",
  url: "https://openai.com/index/introducing-gpt-5-5/",
  bestFor: "Proven all-round work",
  score: "65",
  price: "$4.35 / 1M",
  license: "Proprietary",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 5,
  name: "Grok 4.5",
  dev: "xAI",
  icon: "/images/icons/x.ai.png",
  url: "https://docs.x.ai/developers/grok-4-5",
  bestFor: "Strong reasoning, lower price",
  score: "61",
  price: "$1.35 / 1M",
  license: "Proprietary",
  custom: "500k",
  customLabel: "Context"
}, {
  rank: 6,
  name: "Gemini 3.5 Flash",
  dev: "Google",
  icon: "/images/icons/google.com.png",
  url: "https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-5/",
  bestFor: "Speed and high volume",
  score: "52",
  price: "$1.31 / 1M",
  license: "Proprietary",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 7,
  name: "Gemini 3.1 Pro",
  dev: "Google",
  icon: "/images/icons/google.com.png",
  url: "https://ai.google.dev/gemini-api/docs/models/gemini-3.1-pro-preview",
  bestFor: "Balanced everyday reasoning",
  score: "49",
  price: "$1.74 / 1M",
  license: "Proprietary",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 8,
  name: "Claude Sonnet 5",
  dev: "Anthropic",
  icon: "/images/icons/anthropic.com.png",
  url: "https://www.anthropic.com/news/claude-sonnet-5",
  bestFor: "Balanced daily driver",
  score: "47",
  price: "$1.54 / 1M",
  license: "Proprietary",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 9,
  name: "GLM-5.2",
  dev: "Z.ai",
  icon: "/images/icons/z.ai.png",
  url: "https://docs.z.ai/guides/llm/glm-5.2",
  bestFor: "Top open-weight capability",
  score: "46",
  price: "$0.90 / 1M",
  license: "Open weight",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 10,
  name: "Qwen3.7 Max",
  dev: "Alibaba",
  icon: "/images/icons/qwen.ai.png",
  url: "https://qwen.ai/blog?id=qwen3.7",
  bestFor: "Low-cost proprietary reasoning",
  score: "40",
  price: "$1.43 / 1M",
  license: "Proprietary",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 11,
  name: "MiniMax-M3",
  dev: "MiniMax",
  icon: "/images/icons/minimax.io.png",
  url: "https://www.minimax.io/blog/minimax-m3",
  bestFor: "Rock-bottom open-weight cost",
  score: "39",
  price: "$0.22 / 1M",
  license: "Open weight",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 12,
  name: "DeepSeek V4 Flash",
  dev: "DeepSeek",
  icon: "/images/icons/deepseek.com.png",
  url: "https://api-docs.deepseek.com/news/news260424",
  bestFor: "Cheapest high-volume API",
  score: "30",
  price: "$0.06 / 1M",
  license: "Open weight",
  custom: "1M",
  customLabel: "Context"
}, {
  rank: 13,
  name: "Gemma 4 31B",
  dev: "Google",
  icon: "/images/icons/google.com.png",
  url: "https://huggingface.co/google/gemma-4-31B-it",
  bestFor: "Local on a workstation",
  score: "27",
  price: "$0.17 / 1M",
  license: "Open weight",
  custom: "262k",
  customLabel: "Context"
}, {
  rank: 14,
  name: "Kimi K2.6",
  dev: "Moonshot AI",
  icon: "/images/icons/kimi.com.png",
  url: "https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart",
  bestFor: "Open-weight generalist value",
  score: "24",
  price: "$0.70 / 1M",
  license: "Open weight",
  custom: "256k",
  customLabel: "Context"
}, {
  rank: 15,
  name: "DeepSeek V4 Pro",
  dev: "DeepSeek",
  icon: "/images/icons/deepseek.com.png",
  url: "https://api-docs.deepseek.com/news/news260424",
  bestFor: "Budget quality reasoning",
  score: "20",
  price: "$0.18 / 1M",
  license: "Open weight",
  custom: "1M",
  customLabel: "Context"
}];

export const Fav = ({icon, size = "h-4 w-4"}) => <img src={icon} alt="" noZoom className={"relative -top-px mr-1 inline rounded-sm object-contain " + size} />;

export const LicenseBadge = ({license}) => license === "Open weight" ? <span className="inline-flex items-center rounded bg-emerald-50 px-1.5 py-[2px] text-xs font-medium leading-4 text-emerald-700 dark:bg-emerald-500/10 dark:text-emerald-400">Open weight</span> : <span className="inline-flex items-center rounded bg-zinc-100 px-1.5 py-[2px] text-xs font-medium leading-4 text-zinc-600 dark:bg-white/10 dark:text-zinc-300">Proprietary</span>;

export const BestForChip = ({children}) => <span className="inline-flex min-w-0 max-w-full items-center rounded bg-sky-50 px-1.5 py-[2px] dark:bg-sky-500/10">
    <span className="truncate text-xs font-medium leading-4 text-sky-700 dark:text-sky-400">{children}</span>
  </span>;

export const HeaderTip = ({label, tip}) => <span className="inline-flex items-center gap-1.5">
    <span>{label}</span>
    <Tooltip tip={tip}>
      <span className="relative top-0.5 inline-flex cursor-pointer text-zinc-500 transition-opacity hover:opacity-70 dark:text-zinc-400">
        <Icon icon="circle-info" size={12} color="currentColor" />
        <span className="sr-only">About {label.toLowerCase()}</span>
      </span>
    </Tooltip>
  </span>;

export const ModelCard = ({model, children}) => <section className="not-prose my-5 rounded-lg border border-zinc-200 bg-white p-5 shadow-sm dark:border-white/10 dark:bg-white/[0.03]">
    <div className="mb-3"><BestForChip>{model.bestFor}</BestForChip></div>
    <div className="flex items-center gap-3">
      <img src={model.icon} alt={model.dev + " logo"} noZoom className="h-10 w-10 shrink-0 rounded-lg object-contain" />
      <div className="min-w-0 flex-1">
        <h2 className="m-0 max-w-full text-xl font-semibold leading-7 tracking-normal">
          <a href={model.url} target="_blank" rel="noreferrer" className="group inline-flex min-w-0 max-w-full items-center gap-1.5 no-underline text-zinc-950 dark:text-white">
            <span className="min-w-0 break-words"><span className="underline-offset-4 group-hover:underline group-focus-visible:underline">{model.name}</span> <span className="font-normal text-zinc-500 dark:text-zinc-400">({model.dev})</span></span>
            <span aria-hidden="true" className="flex shrink-0 text-zinc-400 transition-colors group-hover:text-zinc-900 group-focus-visible:text-zinc-900 dark:text-zinc-500 dark:group-hover:text-white dark:group-focus-visible:text-white"><Icon icon="arrow-up-right" size={12} color="currentColor" /></span>
          </a>
        </h2>
        <div className="mt-1 flex flex-wrap items-center gap-x-2 gap-y-1 text-[13px] leading-5 uai-ink-muted">
          <LicenseBadge license={model.license} />
          <span aria-hidden="true">∙</span>
          <span>Score <span className="tabular-nums text-zinc-950 dark:text-white">{model.score}</span></span>
          <span aria-hidden="true">∙</span>
          <span>Price <span className="tabular-nums text-zinc-950 dark:text-white">{model.price}</span></span>
          {model.custom && <><span aria-hidden="true">∙</span><span>{model.customLabel} <span className="tabular-nums text-zinc-950 dark:text-white">{model.custom}</span></span></>}
        </div>
      </div>
    </div>
    {children}
  </section>;

export const Take = ({children}) => <div className="not-prose mt-4 text-sm leading-[22px] text-zinc-700 dark:text-zinc-300">{children}</div>;

export const ST = ({label, children}) => <div className="not-prose mt-4">
    <div className="text-sm font-semibold text-zinc-950 dark:text-white">{label}</div>
    <div className="mt-1 flex flex-col gap-2.5 text-sm leading-[22px] text-zinc-700 dark:text-zinc-300">{children}</div>
  </div>;

export const AccessBullets = ({rows}) => <div className="not-prose mt-4 text-sm leading-[22px] text-zinc-700 dark:text-zinc-300">
    <div className="font-semibold text-zinc-950 dark:text-white">How to access</div>
    <div className="mt-1.5 flex flex-col gap-1.5">
      {rows.map(([key, body]) => <span key={key} className="flex items-baseline gap-2.5"><span aria-hidden="true" className="relative -top-0.5 inline-block h-1.5 w-1.5 shrink-0 rounded-full bg-zinc-700 dark:bg-zinc-300" /><span><span className="font-semibold text-zinc-950 dark:text-white">{key}</span> — {body}</span></span>)}
    </div>
  </div>;

export const Alt = ({icon, name, dev, url, children}) => <span className="flex items-baseline gap-2.5">
    <span aria-hidden="true" className="relative -top-0.5 inline-block h-1.5 w-1.5 shrink-0 rounded-full bg-zinc-300 dark:bg-zinc-600" />
    <span><Fav icon={icon} /><a href={url} target="_blank" rel="noreferrer" className="font-medium text-zinc-950 underline underline-offset-2 dark:text-white">{name}</a> <span className="uai-ink-muted">({dev})</span> — {children}</span>
  </span>;

<div className="not-prose -mt-2 mb-8 flex flex-wrap items-center gap-x-2 text-sm text-zinc-800 dark:text-zinc-200 lg:-mt-5" style={{ paddingLeft: "2px" }}>
  <span className="inline-flex items-center gap-1.5"><Icon icon="clock-rotate-left" size={10} color="currentColor" /> Updated <time dateTime="2026-07-12">July 12, 2026</time></span>
</div>

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.

## Best LLMs

<div className="uai-overview-breakout not-prose my-5">
  <input type="checkbox" id="models-more" className="fy-more-toggle sr-only" aria-label={"Show all 15 llms models"} />

  <div className="fy-more-table overflow-x-auto rounded-lg border border-zinc-200 bg-white dark:border-white/10 dark:bg-white/[0.03]">
    <div className="table text-sm" style={{ width: "100%" }}>
      <div className="table-header-group bg-zinc-50/60 dark:bg-white/[0.02]">
        <div className="table-row">
          <span className="table-cell whitespace-nowrap px-4 py-2.5 text-left text-[13px] font-medium leading-5 text-zinc-500 dark:text-zinc-400">#</span>
          <span className="table-cell whitespace-nowrap px-4 py-2.5 text-left text-[13px] font-medium leading-5 text-zinc-500 dark:text-zinc-400">Model</span>
          <span className="table-cell whitespace-nowrap px-4 py-2.5 text-left text-[13px] font-medium leading-5 text-zinc-500 dark:text-zinc-400">Best for</span>

          <span className="table-cell whitespace-nowrap px-4 py-2.5 text-right text-[13px] font-medium leading-5 text-zinc-500 dark:text-zinc-400">
            <HeaderTip label="Score" tip={"UsefulAI's 0-100 score combines normalized Artificial Analysis Intelligence Index and Arena Text Overall results. Higher is better."} />
          </span>

          <span className="table-cell whitespace-nowrap px-4 py-2.5 text-right text-[13px] font-medium leading-5 text-zinc-500 dark:text-zinc-400">
            <HeaderTip label="Price" tip={"Blended USD per 1M tokens for the scored variant. Split pricing, tiers, caching, and long-context charges can change the real cost."} />
          </span>

          <span className="table-cell whitespace-nowrap px-4 py-2.5 text-left text-[13px] font-medium leading-5 text-zinc-500 dark:text-zinc-400">
            <HeaderTip label="License" tip="Proprietary means no public model weights. Open weight means weights are available, though exact licenses and commercial-use terms vary." />
          </span>

          <span className="table-cell whitespace-nowrap px-4 py-2.5 text-left text-[13px] font-medium leading-5 text-zinc-500 dark:text-zinc-400">
            <HeaderTip label={"Context"} tip={"Maximum context window for the scored variant or closest official route. Larger context does not guarantee better answers."} />
          </span>
        </div>
      </div>

      <div className="table-row-group">
        {models.map((model, index) => (
                        <a key={model.name} href={model.url} target="_blank" rel="noreferrer" className={"table-row no-underline transition-colors hover:bg-zinc-50/60 dark:hover:bg-white/[0.02]" + (models.length > 9 && index >= 7 ? " fy-more-row" : "")}>
                          <span className="table-cell w-8 whitespace-nowrap border-t border-zinc-100 px-4 py-3 align-middle text-[13px] tabular-nums dark:border-white/5"><span className="sr-only">Rank </span><span className="uai-ink-muted">{model.rank}</span></span>
                          <span className="table-cell whitespace-nowrap border-t border-zinc-100 px-4 py-3 align-middle dark:border-white/5"><span className="flex items-center gap-2.5"><Fav icon={model.icon} /><span className="text-sm font-semibold leading-5 text-zinc-950 dark:text-white">{model.name}</span></span></span>
                          <span className="table-cell whitespace-nowrap border-t border-zinc-100 px-4 py-3 align-middle text-[13px] leading-5 text-zinc-950 dark:border-white/5 dark:text-white"><span className="sr-only">Best for: </span>{model.bestFor}</span>
                          <span className="table-cell whitespace-nowrap border-t border-zinc-100 px-4 py-3 text-right align-middle text-[13px] leading-5 tabular-nums text-zinc-950 dark:border-white/5 dark:text-white"><span className="sr-only">Score: </span>{model.score}</span>
                          <span className="table-cell whitespace-nowrap border-t border-zinc-100 px-4 py-3 text-right align-middle text-[13px] leading-5 tabular-nums dark:border-white/5"><span className="sr-only">Price: </span><span className="uai-ink-muted">{model.price}</span></span>
                          <span className="table-cell whitespace-nowrap border-t border-zinc-100 px-4 py-3 align-middle dark:border-white/5"><span className="sr-only">License: </span><LicenseBadge license={model.license} /></span>
                          <span className="table-cell whitespace-nowrap border-t border-zinc-100 px-4 py-3 align-middle text-[13px] leading-5 text-zinc-950 dark:border-white/5 dark:text-white"><span className="sr-only">Context: </span>{model.custom}</span>
                        </a>
                      ))}
      </div>
    </div>
  </div>

  {models.length > 9 && <div className="fy-more-pill mt-3 flex justify-center">
      <label htmlFor="models-more" className="inline-flex cursor-pointer items-center gap-1.5 rounded-full border border-zinc-200 px-4 py-1.5 text-[13px] font-medium text-zinc-600 transition-colors hover:text-zinc-900 dark:border-white/15 dark:text-zinc-300 dark:hover:text-white">
        <span className="fy-more-open inline-flex items-center gap-1.5">Show {models.length - 7} more <Icon icon="chevron-down" size={13} /></span>
        <span className="fy-more-close items-center gap-1.5">Show less <Icon icon="chevron-up" size={13} /></span>
      </label>
    </div>}
</div>

***

<ModelCard model={models[0]}>
  <Take>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.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>When the task is genuinely hard and the ceiling matters, this is the pick.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>The price and latency are the catch: for everyday chat, summaries, or routine coding, you're paying for headroom you won't use.</div>
    <div>Drop to Opus 4.8 or Sonnet 5 for most of the quality at far lower cost, and save Fable 5 for hard problems.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://claude.ai" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude</a> and <a href="https://docs.anthropic.com/en/docs/claude-code/overview" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude Code</a>.</span>],
["API", <span>Accessible via <a href="https://platform.claude.com/docs/en/about-claude/models/overview" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude API</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[1]}>
  <Take>OpenAI's newest flagship is a top-tier generalist that reasons cleanly across broad tasks, and it undercuts the very top on price.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>For frontier-level general work without paying the absolute premium, this is the sensible high-end default.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>And Claude Fable 5 still pulls ahead on the hardest, longest tasks.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://chatgpt.com" target="_blank" rel="noreferrer" className="underline underline-offset-2">ChatGPT</a>.</span>],
["API", <span>Accessible via <a href="https://developers.openai.com/api/docs" target="_blank" rel="noreferrer" className="underline underline-offset-2">OpenAI API</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[2]}>
  <Take>The current Opus is a deep-reasoning workhorse for hard analysis and long agent runs, at a noticeably lower price than the top tier.</Take>

  <ST label="Strengths:">
    <div>Opus is built for sustained, careful reasoning: untangling ambiguous failures, working through large systems, and running long agent tasks without losing the thread.</div>
    <div>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.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>For the very hardest work, Fable 5 remains a clear step up.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://claude.ai" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude</a> and <a href="https://docs.anthropic.com/en/docs/claude-code/overview" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude Code</a>.</span>],
["API", <span>Accessible via <a href="https://platform.claude.com/docs/en/about-claude/models/overview" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude API</a> and <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-opus-4-8.html" target="_blank" rel="noreferrer" className="underline underline-offset-2">Amazon Bedrock</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[3]}>
  <Take>The previous GPT flagship is still a complete, reliable all-rounder - just outclassed now by GPT-5.6 Sol at a similar price.</Take>

  <ST label="Strengths:">
    <div>This is a proven generalist with no obvious weak spots: dependable at reasoning, writing, coding, and tool use across a 1M-token context.</div>
    <div>It's the kind of model you can point at broad production work and trust to be consistent, even if newer releases now edge it out on peak capability.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>The problem is its own successor: GPT-5.6 Sol scores higher at a similar price, so for new work there's little reason to start here.</div>
    <div>If you want more capability, Sol or Claude Opus 4.8 both pull ahead.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://chatgpt.com" target="_blank" rel="noreferrer" className="underline underline-offset-2">ChatGPT</a>.</span>],
["API", <span>Accessible via <a href="https://developers.openai.com/api/docs/models/gpt-5.5" target="_blank" rel="noreferrer" className="underline underline-offset-2">OpenAI API</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[4]}>
  <Take>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.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>For teams that want strong output without frontier pricing, it's one of the better balances on this list.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>Its context window is half what most rivals here offer, so very long documents or repo-wide runs can hit the wall sooner.</div>
    <div>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.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://grok.com" target="_blank" rel="noreferrer" className="underline underline-offset-2">Grok</a>.</span>],
["API", <span>Accessible via <a href="https://docs.x.ai/developers/grok-4-5" target="_blank" rel="noreferrer" className="underline underline-offset-2">xAI API</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[5]}>
  <Take>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.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>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.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>Use Flash where volume beats peak capability.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://gemini.google.com" target="_blank" rel="noreferrer" className="underline underline-offset-2">Gemini</a>.</span>],
["API", <span>Accessible via <a href="https://ai.google.dev/gemini-api/docs/models" target="_blank" rel="noreferrer" className="underline underline-offset-2">Gemini API</a> and <a href="https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/3-5-flash" target="_blank" rel="noreferrer" className="underline underline-offset-2">Gemini Enterprise Agent Platform</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[6]}>
  <Take>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.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>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.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>It's still a preview release, so behavior and pricing can shift before it's final - pin versions for anything production-critical.</div>
    <div>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.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://gemini.google.com" target="_blank" rel="noreferrer" className="underline underline-offset-2">Gemini</a>.</span>],
["API", <span>Accessible via <a href="https://ai.google.dev/gemini-api/docs/models/gemini-3.1-pro-preview" target="_blank" rel="noreferrer" className="underline underline-offset-2">Gemini API</a> and <a href="https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/3-1-pro" target="_blank" rel="noreferrer" className="underline underline-offset-2">Gemini Enterprise Agent Platform</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[7]}>
  <Take>Sonnet 5 is the balanced daily driver in the Claude line - most of the reasoning quality of Opus at a much friendlier price.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>For most people, it's the sensible default.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>If your work is routinely at that difficulty, pay up for one of those; otherwise Sonnet 5 is hard to beat.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://claude.ai" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude</a> and <a href="https://docs.anthropic.com/en/docs/claude-code/overview" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude Code</a>.</span>],
["API", <span>Accessible via <a href="https://platform.claude.com/docs/en/about-claude/models/overview" target="_blank" rel="noreferrer" className="underline underline-offset-2">Claude API</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[8]}>
  <Take>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.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>For frontier-adjacent capability without proprietary lock-in, this is the one.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>Most people will end up calling a hosted API, and on peak capability it trails frontier picks like Claude Fable 5.</div>
  </ST>

  <AccessBullets
    rows={[
["API", <span>Accessible via <a href="https://docs.z.ai/guides/llm/glm-5.2" target="_blank" rel="noreferrer" className="underline underline-offset-2">Z.ai API</a> and <a href="https://openrouter.ai/z-ai/glm-5.2" target="_blank" rel="noreferrer" className="underline underline-offset-2">OpenRouter</a>.</span>],
["Run locally", <span>Open weights are available from <a href="https://huggingface.co/zai-org/GLM-5.2" target="_blank" rel="noreferrer" className="underline underline-offset-2">Hugging Face</a>, but in practice this needs self-hosting infrastructure, not a local machine.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[9]}>
  <Take>Qwen3.7 Max is a capable, low-cost proprietary challenger - good general reasoning at a price that undercuts most Western flagships.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>If cost is a first-order constraint and you still want a proprietary, hosted model with a big context, it earns a look.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>It sits mid-pack on capability, so for hard reasoning you'll do better with Grok 4.5 or Gemini 3.1 Pro.</div>
    <div>Access is mainly through Alibaba's cloud or OpenRouter, which can mean regional and setup friction depending on where you operate.</div>
  </ST>

  <AccessBullets
    rows={[
["API", <span>Accessible via <a href="https://www.alibabacloud.com/help/en/model-studio/models" target="_blank" rel="noreferrer" className="underline underline-offset-2">Alibaba Cloud Model Studio</a> and <a href="https://openrouter.ai/qwen/qwen3.7-max" target="_blank" rel="noreferrer" className="underline underline-offset-2">OpenRouter</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[10]}>
  <Take>MiniMax-M3 is an open-weight model built for cheap scale - very low cost per token, with capability that's fine rather than frontier.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>For straightforward generation, extraction, and chat at scale, it does the job without much fuss.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>For a little more capability, DeepSeek V4 Pro and GLM-5.2 are worth comparing.</div>
  </ST>

  <AccessBullets
    rows={[
["API", <span>Accessible via <a href="https://openrouter.ai/minimax/minimax-m3" target="_blank" rel="noreferrer" className="underline underline-offset-2">OpenRouter</a>.</span>],
["Run locally", <span>Open weights are available from <a href="https://huggingface.co/MiniMaxAI/MiniMax-M3" target="_blank" rel="noreferrer" className="underline underline-offset-2">Hugging Face</a>, but in practice this needs self-hosting infrastructure, not a local machine.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[11]}>
  <Take>DeepSeek V4 Flash is the price floor of this list - astonishingly cheap per token, best aimed at high-volume, lower-stakes work.</Take>

  <ST label="Strengths:">
    <div>Nothing here touches it on cost, and it comes with a 1M-token context and open weights.</div>
    <div>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.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>Step up to DeepSeek V4 Pro, GLM-5.2, or a midrange proprietary model when quality matters more than raw cost.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://chat.deepseek.com" target="_blank" rel="noreferrer" className="underline underline-offset-2">DeepSeek Chat</a>.</span>],
["API", <span>Accessible via <a href="https://api-docs.deepseek.com/news/news260424" target="_blank" rel="noreferrer" className="underline underline-offset-2">DeepSeek API</a> and <a href="https://openrouter.ai/deepseek/deepseek-v4-flash" target="_blank" rel="noreferrer" className="underline underline-offset-2">OpenRouter</a>.</span>],
["Run locally", <span>Open weights are available from <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash" target="_blank" rel="noreferrer" className="underline underline-offset-2">Hugging Face</a>, but in practice this needs self-hosting infrastructure, not a local machine.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[12]}>
  <Take>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.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>Open weights and a 262k context make it a solid base for private, self-contained work.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>It's near the bottom on capability, so keep expectations modest: fine for well-scoped tasks, not for hard reasoning or serious agent work.</div>
    <div>And "local" still means real hardware - without it you're better off with a cheap hosted model like DeepSeek V4 Flash.</div>
  </ST>

  <AccessBullets
    rows={[
["Run locally", <span>If you have a high-end machine, you can run it with <a href="https://ollama.com/library/gemma4:31b" target="_blank" rel="noreferrer" className="underline underline-offset-2">Ollama</a> after downloading weights from <a href="https://huggingface.co/google/gemma-4-31B-it" target="_blank" rel="noreferrer" className="underline underline-offset-2">Hugging Face</a>.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[13]}>
  <Take>Kimi K2.6 is a serviceable open-weight generalist - decent value through a hosted API, but not a top-capability pick.</Take>

  <ST label="Strengths:">
    <div>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.</div>
    <div>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.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>GLM-5.2 is the stronger open-weight pick if you can spend a little more.</div>
  </ST>

  <AccessBullets
    rows={[
["API", <span>Accessible via <a href="https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart" target="_blank" rel="noreferrer" className="underline underline-offset-2">Kimi Platform</a> and <a href="https://openrouter.ai/moonshotai/kimi-k2.6" target="_blank" rel="noreferrer" className="underline underline-offset-2">OpenRouter</a>.</span>],
["Run locally", <span>Open weights are available from <a href="https://huggingface.co/moonshotai/Kimi-K2.6" target="_blank" rel="noreferrer" className="underline underline-offset-2">Hugging Face</a>, but in practice this needs self-hosting infrastructure, not a local machine.</span>],
]}
  />
</ModelCard>

<ModelCard model={models[14]}>
  <Take>DeepSeek V4 Pro aims for real reasoning quality at a rock-bottom price, and mostly gets there - just don't expect frontier-level output.</Take>

  <ST label="Strengths:">
    <div>It's the more capable DeepSeek tier: coherent reasoning, a 1M-token context, and a price that stays very low.</div>
    <div>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.</div>
  </ST>

  <ST label="Tradeoffs:">
    <div>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.</div>
    <div>Despite open weights it's a hosted-API play in practice. For more capability, GLM-5.2 and midrange proprietary models pull ahead.</div>
  </ST>

  <AccessBullets
    rows={[
["App", <span>Available in <a href="https://chat.deepseek.com" target="_blank" rel="noreferrer" className="underline underline-offset-2">DeepSeek Chat</a>.</span>],
["API", <span>Accessible via <a href="https://api-docs.deepseek.com/news/news260424" target="_blank" rel="noreferrer" className="underline underline-offset-2">DeepSeek API</a> and <a href="https://openrouter.ai/deepseek/deepseek-v4-pro" target="_blank" rel="noreferrer" className="underline underline-offset-2">OpenRouter</a>.</span>],
["Run locally", <span>Open weights are available from <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro" target="_blank" rel="noreferrer" className="underline underline-offset-2">Hugging Face</a>, but in practice this needs self-hosting infrastructure, not a local machine.</span>],
]}
  />
</ModelCard>

***

## How to Choose

When choosing between these models, consider:

* **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.

***

## Other Models We Considered

<div className="not-prose my-4 flex flex-col gap-1.5 text-sm text-zinc-700 dark:text-zinc-300">
  <Alt icon={"/images/icons/openai.com.png"} name={"GPT-5.6 Terra"} dev={"OpenAI"} url={"https://developers.openai.com/api/docs/models/gpt-5.6-terra"}>The mid-tier GPT-5.6 - good value, but Sol is the stronger flagship.</Alt>
  <Alt icon={"/images/icons/openai.com.png"} name={"GPT-5.6 Luna"} dev={"OpenAI"} url={"https://developers.openai.com/api/docs/models/gpt-5.6-luna"}>The cheapest GPT-5.6 tier - fine, but not a standout pick.</Alt>
  <Alt icon={"/images/icons/anthropic.com.png"} name={"Claude Opus 4.7"} dev={"Anthropic"} url={"https://www.anthropic.com/news/claude-opus-4-7"}>Older Opus that still tops preference charts; 4.8 is the current version.</Alt>
  <Alt icon={"/images/icons/anthropic.com.png"} name={"Claude Opus 4.6"} dev={"Anthropic"} url={"https://www.anthropic.com/news/claude-opus-4-6"}>Another strong older Opus, now superseded by newer Claude releases.</Alt>
  <Alt icon={"/images/icons/meta.ai.png"} name={"Muse Spark"} dev={"Meta"} url={"https://ai.meta.com/blog/introducing-muse-spark-msl/"}>Promising Meta benchmark signal, but access and pricing remain too unclear.</Alt>
  <Alt icon={"/images/icons/openai.com.png"} name={"GPT-5.4"} dev={"OpenAI"} url={"https://developers.openai.com/api/docs/models/gpt-5.4"}>A capable earlier GPT, now superseded by GPT-5.5 and 5.6.</Alt>
  <Alt icon={"/images/icons/x.ai.png"} name={"Grok 4.20"} dev={"xAI"} url={"https://docs.x.ai/developers/models"}>Strong on human preference, but those scores don't transfer to Grok 4.5.</Alt>
  <Alt icon={"/images/icons/openai.com.png"} name={"GPT-5.3 Codex"} dev={"OpenAI"} url={"https://developers.openai.com/api/docs/models/gpt-5.3-codex"}>A coding-specialized GPT, better matched to a dedicated coding list.</Alt>
  <Alt icon={"/images/icons/openai.com.png"} name={"gpt-oss-120b"} dev={"OpenAI"} url={"https://openai.com/index/introducing-gpt-oss/"}>OpenAI's open-weight option, but unscored on the benchmarks used here.</Alt>
  <Alt icon={"/images/icons/meta.ai.png"} name={"Llama 4 Scout"} dev={"Meta"} url={"https://ai.meta.com/blog/llama-4-multimodal-intelligence/"}>A major open-weight baseline; realistic local use needs high-end hardware.</Alt>
</div>

***

## Frequently Asked Questions

<AccordionGroup>
  <Accordion title={"What is the best LLM right now?"}>
    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.
  </Accordion>

  <Accordion title={"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.
  </Accordion>

  <Accordion title={"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.
  </Accordion>

  <Accordion title={"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.
  </Accordion>

  <Accordion title={"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.
  </Accordion>

  <Accordion title={"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.
  </Accordion>

  <Accordion title={"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.
  </Accordion>
</AccordionGroup>