Updated
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
#ModelBest for
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.
Other Models We Considered
Frequently Asked Questions
What is the best LLM for search and deep research right now?
What is the best LLM for search and deep research right now?
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.
What is the best option for most people?
What is the best option for most people?
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.
What is the cheapest capable option?
What is the cheapest capable option?
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.
What is the best open-weight model for research?
What is the best open-weight model for research?
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.
Can I run any of these locally?
Can I run any of these locally?
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.
Do these benchmarks match real research use?
Do these benchmarks match real research use?
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.
Is a bigger context window better for research?
Is a bigger context window better for research?
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.