LLMs for search and deep research browse the live web, gather sources, and synthesize cited answers or full reports. The catch: raw browsing skill and report-writing quality rarely track together. We ranked 12 leading models on both to separate real research ability from demo polish.
## Best LLMs for Search & Deep Research
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The top scorer here and the strongest candidate for genuinely hard research questions, though you pay frontier prices for it.
It leads on both halves of research - digging out buried answers and turning them into accurate, well-cited reports - and it holds together across long, ambiguous, multi-step work.
When the question is hard and getting it right matters more than the bill, this is the pick.
It's the most expensive model here by a wide margin, and on some sensitive cybersecurity and biology queries it quietly hands off to Opus 4.8.
For everyday research, Opus 4.8 or Sonnet 5 give you most of the quality for far less.
It breaks a big research task into parts and runs subagents in parallel, so broad multi-source sweeps come back fast and still hang together.
Retrieval and report-writing are both strong, making it the closest challenger to Fable 5 at a lower price.
It launched days ago, so its reliability on long unattended runs is still being proven, and Ultra mode is token-hungry.
If you want a settled default, GPT-5.5 or Opus 4.8 are steadier bets today.
Available in ChatGPT and Codex.],
["API", Accessible via OpenAI API.],
]}
/>
The proven, everywhere-deployed default that tops raw web-browsing benchmarks and rarely surprises you - a safe pick when you don't need Fable's ceiling.
It's the strongest model here at digging out hard-to-find answers from the open web, and it's predictable under load.
If your research is mostly about locating specific facts fast and reliably, this is the dependable everyday workhorse.
On full report synthesis and presentation it sits just behind the very top, and it costs the same as the newer Sol without the parallel Ultra mode.
Want newest, pick Sol; want cheaper, Terra.
Available in ChatGPT.],
["API", Accessible via OpenAI API.],
]}
/>
The open-weight standout - it matches proprietary mid-tier research quality at a small fraction of the price, making it the clear value pick.
It delivers frontier-adjacent research quality with open weights and a very large context, at a price that undercuts every proprietary rival here.
Open weights also let you route it through whichever host fits your budget or compliance needs.
Despite open weights it's far too large for your own machine, so in practice you're calling a hosted API like any proprietary model.
Some organizations also limit China-origin models. For managed simplicity, GPT-5.5 or Sonnet 5.
Available in DeepSeek Chat.],
["API", Accessible via DeepSeek API.],
["Run locally", Open weights are available from Hugging Face, but in practice this needs self-hosting infrastructure, not a local machine.],
]}
/>
The steady, high-accuracy Claude that Fable 5 itself falls back to on sensitive queries - a safe heavy-duty default just below the top.
It's excellent at careful synthesis with reliable citations, and it stays on track across long, multi-step research without drifting.
When you want near-frontier quality you can trust unattended, minus Fable's price and preview-stage edges, this is the dependable choice.
It's a clear step below Fable 5 on the very hardest questions, and it costs more than Sonnet 5, which handles most everyday research for less.
For the outright ceiling go to Fable 5; to save money, Sonnet 5.
It handles general multi-source research well at a mid-tier price, with a solid balance of retrieval and clean synthesis.
When Sol is more than you need but you still want current-generation quality, Terra is the practical middle option.
It has no Ultra parallel-agent mode, so the broadest, hardest sweeps still favor Sol, and it trails Opus 4.8 and GPT-5.5 on the toughest questions.
Being days old, expect some early rough edges.
Available in ChatGPT Work and Codex.],
["API", Accessible via OpenAI API.],
]}
/>
Google's research workhorse, strongest when answers must stay tied to a defined set of sources with clean, checkable citations.
It's very good at grounded synthesis and citation discipline, especially over your own uploaded source packs, and its large context plus native web grounding make it strong for document-heavy research and notebook-style workflows.
Its native grounding stack doesn't slot into common browsing harnesses, so head-to-head comparisons get murkier, and on open-web needle-finding it trails Fable 5 and the GPT-5.6 line.
For general research, weigh Opus 4.8.
Available in Gemini, NotebookLM, and Perplexity Pro Search.],
["API", Accessible via Gemini API and Google Cloud.],
]}
/>
The value-for-quality sweet spot in the Claude line - most of Opus 4.8's research reliability at a much friendlier price.
It gives you strong, well-cited synthesis and dependable long-context behavior at a mid-tier price.
For people who want reliable research quality as an everyday default, without stepping up to Opus 4.8 or Fable 5 pricing, it's the sensible choice.
It gives up ceiling on the hardest, most ambiguous research to Opus 4.8 and Fable 5, and open-weight DeepSeek V4 Pro undercuts it on price.
It pairs rock-bottom pricing with open weights, a large context, and native image and video input, so multimodal source packs and screen-based research are in reach without frontier costs.
A strong fit for cheap, high-volume visual research.
It sits below the frontier on the hardest reasoning and open-web needle-finding, and its open weights are too large for a laptop, so you're on a hosted API anyway.
For higher research accuracy at a similar price, DeepSeek V4 Pro.
Available in MiniMax Agent.],
["API", Accessible via MiniMax API.],
["Run locally", Open weights are available from Hugging Face, but in practice this needs self-hosting infrastructure, not a local machine.],
]}
/>
The budget tier of GPT-5.6, built for fast, cheap research at scale where you don't need Sol-level depth.
It offers strong capability for its low price, current-generation retrieval, and quick responses, making it a good fit for high-volume, latency-sensitive research pipelines and routine lookups where you don't want to pay for a heavier model.
As the lightweight tier, it trails Sol, Terra, and Opus 4.8 on hard multi-step research and dense report synthesis, and it's only days old.
For cheap-but-deeper research, DeepSeek V4 Pro is worth a look.
Available in ChatGPT Work and Codex.],
["API", Accessible via OpenAI API.],
]}
/>
An open-weight agent specialist tuned for long, many-step autonomous runs rather than chart-topping raw retrieval scores.
It's built for extended autonomous agent runs with many coordinated steps, so multi-stage research that unfolds over long tool sequences is its natural lane.
Open weights and a low price add routing and cost flexibility on top.
It has the lowest research score here and by far the smallest context of the frontier group, which hurts big source packs, and it's too large to self-host on a laptop.
For open-weight research, DeepSeek V4 Pro is stronger.
Available in Kimi.],
["API", Accessible via Kimi API Platform and OpenRouter.],
["Run locally", Open weights are available from Hugging Face, but in practice this needs self-hosting infrastructure, not a local machine.],
]}
/>
Perplexity's purpose-built research API that runs the whole search, read, and synthesize loop for you and returns a cited report.
It's a managed deep-research pipeline in a single API call - it searches, reads across many sources, and returns a structured, cited report - so you skip building and maintaining the agent loop yourself.
Handy when you want research output, not a model to orchestrate.
It's a packaged system, not a general model, with the smallest context here and no standalone app, and search fees stack on top of token costs.
If you want a raw model you fully control, Gemini 3.1 Pro or Opus 4.8.
Accessible via Perplexity API.],
]}
/>
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## How to Choose
When choosing between these models, consider:
* **Access:** First decide whether you'll use the model in an app, call it through an API, or self-host open weights, because that choice drives cost, privacy, latency, and setup work more than small score gaps do. Only three models here (DeepSeek V4 Pro, MiniMax M3, Kimi K2.6) ship open weights, and all need server-grade hardware - so "open" means routing flexibility and compliance control, not a laptop.
* **Quality:** We use a normalized composite of two benchmarks that measure different things. BrowseComp tests whether a model can dig out a hard-to-find answer through persistent browsing; DRACO grades full research reports on accuracy, completeness, and citations. A model can ace one and lag the other, so we blend them. (Gemini 3.1 Pro runs a native grounding stack the common DRACO harness doesn't fit, so its score leans on browsing.)
* **Price:** We use blended USD per 1M tokens at a 3:1 input-to-output ratio for the cleanest comparison. Watch the extras the sticker price hides: Sonar's per-search fees, deep-research modes that burn tokens across many steps, and subscription or caching quirks.
* **Context window:** A bigger window helps you load in more sources and synthesize across them, but it doesn't guarantee better retrieval or cleaner citations. Kimi K2.6 and Sonar carry the smallest windows here, which bites when your source pack is large.
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## Other Models We Considered
A multi-model research panel you call through one API, not a single model.Useful when your research leans on real-time X and web signals.The prior Opus - fine, but 4.8 and Fable 5 are better now.Open 35B agent model, but no managed app or hosted API.Cheap open-weight search agent with a genuine high-end local route.Faster, cheaper DeepSeek, but noticeably weaker on hard research.Low-cost Google option, but research results lag the leaders.Strong browsing results, but access is limited and largely regional.Perplexity's shorter-form search API, not a full deep-research system.
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## Frequently Asked Questions
Claude Fable 5, when the question is genuinely hard and budget isn't the constraint - it leads on both finding buried answers and writing well-cited reports. For most people, GPT-5.5, Claude Opus 4.8, or Claude Sonnet 5 deliver most of that quality for far less.
For a reliable everyday default, GPT-5.5 (strong at locating hard facts) or Claude Sonnet 5 (strong, well-cited synthesis at a friendlier price). Both handle the bulk of real research without frontier pricing.
DeepSeek V4 Pro and MiniMax M3 sit near the bottom on price while staying genuinely useful for research; among proprietary tiers, GPT-5.6 Luna is the budget pick. All three trade some ceiling for the low cost.
DeepSeek V4 Pro. It matches proprietary mid-tier research quality with open weights and a huge context. Just know it's too large to run on a personal machine - you're self-hosting on servers or paying a host.
Not really. The proprietary models are app- or API-only, and the three open-weight models (DeepSeek V4 Pro, MiniMax M3, Kimi K2.6) need server-grade GPUs. None is a realistic laptop model.
Roughly. BrowseComp reflects finding buried facts and DRACO reflects report quality, which together track real work better than either alone. Still, always spot-check citations - none of these models is immune to confident, wrong sourcing.
It helps when you're feeding in large source packs and synthesizing across them, but it doesn't guarantee better retrieval or citations. A model with a smaller window and sharper grounding can beat a bigger, sloppier one.