LLM·Dex
Use caseTop 6 picks

Best LLM for Reasoning in 2026

Multi-step logical reasoning, planning, and inference.

Updated

How we ranked

  • GPQA Diamond performance
  • ARC-AGI public set scores
  • Chain-of-thought coherence on novel puzzles
  • Reasoning-token cost, expensive on flagship reasoning models
  • Latency budget, reasoning runs are slow by design

Read the full methodology for our sourcing and ranking standards.

The 2025, 2026 cycle's biggest model-quality jumps all came from "thinking", generating internal reasoning tokens before the visible answer. o3 was first; everyone else followed. The result is that GPQA and ARC-AGI scores nearly doubled in eighteen months.

This page ranks the models that actually use thinking mode well, not the ones that have it as a nominal feature. Watching for: does the chain-of-thought stay on-task, or does it spiral? Does the model recognize when it's stuck and try a different approach?

For most production workloads you don't want a reasoning model, they're slow and expensive. But for hard agent steps, planning, and multi-hop QA, the flagship reasoning tier is now mandatory.

The ranking

  1. #1OpenAI

    o3

    OpenAI's flagship reasoning model, set the bar for hard math, GPQA, and agent benchmarks in 2025.

    Context
    200K tokens
    Output · 1M
    $8.00 / 1M tokens
    Modalities
    text, vision

    Why it ranks here. Industry-leading reasoning depth at launch. Strong on math, science, and abstract puzzles. Tracked weakness: Slow first-token, unpredictable total latency.

  2. #2OpenAI

    GPT-5.5

    OpenAI's mid-cycle GPT-5 refresh, improved reasoning, tool use, and multimodal grounding over the 2025 launch.

    Context
    400K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision, audio

    Why it ranks here. Industry-leading tool-use and function-calling reliability. Strong end-to-end agent performance across SWE-bench and GAIA. Tracked weakness: Pricing premium vs. open-weight alternatives.

  3. #3Anthropic

    Claude Opus 4.7

    Anthropic's mid-2026 flagship, ahead on SWE-bench, agent reliability, and writing quality.

    Context
    500K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision

    Why it ranks here. Strongest published SWE-bench Verified scores in agent settings. Best-in-class writing quality and voice control. Tracked weakness: Premium pricing relative to GPT-5 line.

  4. #4DeepSeekOpen weights

    DeepSeek-R1

    First open-weight reasoning model to match o1, the release that proved RL-from-scratch reasoning training was reproducible.

    Context
    128K tokens
    Output · 1M
    $2.19 / 1M tokens
    Modalities
    text

    Why it ranks here. Open-weight reasoning model on par with o1. MIT license. Tracked weakness: Slow, reasoning is slow by design.

  5. #5Google

    Gemini 3 Pro

    Google's late-2025 flagship, set new benchmarks on long-context, vision, and reasoning at competitive pricing.

    Context
    1.0M tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision, audio, video

    Why it ranks here. Massive 1M-token context window. State-of-the-art vision and document understanding. Tracked weakness: Tool-use ergonomics still lag OpenAI / Anthropic in some setups.

  6. #6OpenAI

    o4

    OpenAI's late-2025 standalone reasoning model, an evolution of o3 with deeper chain-of-thought and stronger multimodal reasoning.

    Context
    200K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision

    Why it ranks here. Exceptional performance on hard math and reasoning benchmarks. Good at multi-step planning and verification. Tracked weakness: Slow, reasoning tokens take real wall-clock time.

How to choose

Don't pick on the headline ranking alone. Run your top two picks on a representative sample of your own workload and compare. The numbers in this list are sound, but task-specific quality varies in ways no benchmark fully captures. The criteria above are the right axes to evaluate on, but the weighting depends on your stack.

  • Cost-sensitive workloads, start with the cheapest of the top three; escalate only if quality is the bottleneck.
  • Privacy-sensitive workloads, filter to open-weight picks above. They're labeled with a green badge.
  • Latency-sensitive workloads, see the Fastest LLMs list, which can override task-specific picks.

Frequently asked

  • What is the best model for reasoning?
    Our #1 pick is o3 from OpenAI. OpenAI's flagship reasoning model, set the bar for hard math, GPQA, and agent benchmarks in 2025.
  • How are these rankings determined?
    We rank by the criteria listed at the top of this page: GPQA Diamond performance; ARC-AGI public set scores; Chain-of-thought coherence on novel puzzles. Where two models are close, we prefer the one with stronger production deployment evidence at the time of writing. Read the full methodology for our standards.
  • o3 or GPT-5.5?
    Both are top-tier picks. o3 edges ahead on the criteria most relevant to this task. GPT-5.5 is the strongest alternative, see the head-to-head comparison page for full deltas.
  • Are open-source models on this list?
    Yes where they're competitive. Each entry below shows whether the model ships open weights and under what license.
  • How often is this list updated?
    Weekly. New launches that affect the ranking get reflected within seven days. The "last updated" stamp at the top of the page reflects the most recent dataset commit.

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