Quick answer: what is the best AI model for reasoning?
Claude Fable 5 is our best overall reasoning model for the hardest long-horizon problems. It combines strong analytical work, coding, scientific research and sustained agentic execution. GPT 5.6 Sol is the closest alternative and the better fit when a team already builds on OpenAI’s Responses API and tool ecosystem. GPT 5.5 remains a strong professional model, Claude Opus 5 offers a better cost-to-capability balance than Fable, and Gemini 3.7 Flash is the speed and multimodal choice.
HOW WE TEST: We use a frozen 120-task suite covering mathematical reasoning, scientific analysis, logic, planning, evidence synthesis, adversarial instruction-following and tool-mediated problem solving. Each model receives identical information, tools, retry limits and scoring rubrics. We score final-answer correctness, reasoning robustness, calibration, recovery and cost. Vendor demonstrations never determine the ranking.
Rank | Model | Best for | Primary advantage | Main trade-off |
|---|---|---|---|---|
1 | Claude Fable 5 | Hardest long-horizon reasoning | Sustained work across coding, research and analysis | High price and stricter safeguards |
2 | GPT 5.6 Sol | Tool-using professional agents | Large context, adjustable reasoning and broad tools | Long-context pricing multipliers |
3 | GPT 5.5 | Complex professional analysis | Strong reasoning with stable snapshots | Higher output price than Sol promotion |
4 | Claude Opus 5 | Cost-conscious frontier work | Near-frontier capability at half Fable token price | Below Fable on the hardest tasks |
5 | Gemini 3.7 Flash | Fast multimodal reasoning | Text, image, video, audio and PDF input | Some agent features remain preview |
6 | Kimi K3 | Open-weight long-horizon work | One-million context and native vision | Extremely demanding self-hosting |
7 | Qwen 3.8 | Open-weight coding and agents | Strong long-running autonomous workflows | Exact Max checkpoint matters |
8 | Grok 4.6 | Lower-cost agent reasoning | Competitive API price and coding focus | Benchmark provenance needs careful review |
What reasoning means in this ranking
Reasoning is not a model printing a long chain of thought. We care about whether it reaches a correct, useful conclusion under constraints. A model can produce polished intermediate prose and still choose the wrong evidence, violate a condition or fail to notice that the problem is underspecified.
Our evaluation separates closed-form correctness from open-ended judgment. Mathematics and logic can often be checked against a known answer. Research and planning require rubrics for evidence quality, assumptions, uncertainty, completeness and whether the proposed action survives adversarial review.
Test family | Example task | What earns a high score |
|---|---|---|
Mathematics | Solve a multi-stage optimization problem with hidden edge cases | Correct answer, verified constraints and no unsupported shortcut |
Scientific reasoning | Compare competing explanations against a fixed evidence packet | Accurate evidence use, uncertainty and discriminating tests |
Logic | Resolve a rule system containing distractors and exceptions | Consistent application of every rule |
Planning | Build a dependency-aware project plan under resource limits | Feasible sequence, contingencies and explicit assumptions |
Evidence synthesis | Reconcile conflicting primary sources | Source fidelity, contradiction handling and calibrated conclusion |
Tool reasoning | Use search, code and files to answer a verifiable question | Correct tool choice, recovery and evidence-backed result |
How the testing works
Every task begins with a clean context. We lock the exact model snapshot when the provider allows it, record the reasoning-effort setting and keep tool permissions equal. Models receive one standard clarification opportunity. They cannot receive hints tailored to their failure mode.
Each response is scored independently by two reviewers or by executable checks when possible. Disagreements go to a third review. We rerun a balanced sample to measure variance because a single best attempt can hide an unreliable model.
Metric | Weight | Why it matters |
|---|---|---|
Final correctness | 35% | The conclusion must be right |
Robustness | 20% | The answer should survive paraphrases and distractors |
Evidence and verification | 15% | Claims need traceable support |
Instruction adherence | 10% | Constraints are part of the problem |
Calibration | 10% | The model should distinguish knowledge from uncertainty |
Efficiency | 10% | Reasoning must be affordable and timely |
The models in detail
1. Claude Fable 5
Anthropic describes Claude Fable 5 as its most capable generally available model, with its largest gains on long and complex work. The launch documentation highlights software engineering, scientific research, finance, vision and persistent memory. It costs $10 per million input tokens and $50 per million output tokens.
Choose Fable when the cost of a failed analysis is higher than the token bill and the workload benefits from sustained attention across many steps. Do not assume Anthropic’s launch evaluations transfer directly to your environment. Its cyber and biology classifiers can also materially change behavior on dual-use tasks.
2. GPT 5.6 Sol
OpenAI documents GPT 5.6 Sol with a 1,050,000-token context window, 128,000 maximum output tokens and reasoning effort from none through max. It accepts text and image input and supports structured outputs and tools in the Responses API.
Sol is the strongest alternative for organizations that want a frontier reasoner inside a mature tool platform. Measure the real bill on long contexts: requests above 272,000 input tokens receive higher input and output multipliers.
3. GPT 5.5
The GPT 5.5 model page positions it for complex professional work, with the same 1,050,000-token context ceiling and reasoning effort through xhigh. Its published token price is $5 input and $30 output per million tokens.
GPT 5.5 remains useful where an existing tested snapshot matters more than the newest model. It is not automatically better value than Sol, especially under Sol’s documented promotional pricing.
4. Claude Opus 5
Anthropic positions Claude Opus 5 close to Fable capability at $5 per million input and $25 per million output tokens. Anthropic reports gains on automation, computer use, coding and scientific tasks.
Opus is the practical default when Fable is too expensive. It also offers a Fast mode, but that doubles the base token price. Compare at matched effort and latency rather than using one vendor headline.
5. Gemini 3.7 Flash
Google’s Gemini 3.7 Flash documentation lists text, image, video, audio and PDF input; a 1,048,576-token input limit; thinking levels of low, medium and high; code execution; search grounding; function calling; file search and structured output.
Gemini is the shortlist choice for multimodal inputs and throughput-sensitive reasoning. Computer use is still preview, and this model does not provide image or audio generation.
6. Kimi K3
Moonshot’s Kimi K3 technical launch describes a 2.8-trillion-parameter mixture-of-experts model with native vision and a one-million-token context window. The company plans open-weight availability and provides first-party Kimi applications and API access.
Kimi is the strongest open-weight option in this reasoning shortlist. The model’s scale makes local deployment unrealistic for most teams, so open weights should not be confused with inexpensive self-hosting.
7. Qwen 3.8
Alibaba’s Qwen 3.8 launch describes Qwen3.8-Max as a 2.4-trillion-parameter model with 95 billion active parameters, focused on coding, work, multimodal agents and long-horizon tasks.
Qwen is attractive for teams that need weights and deployment control. Reproduce results with the exact Max checkpoint, tool harness and reasoning setup instead of assuming every Qwen 3.8 derivative behaves identically.
8. Grok 4.6
xAI says Grok 4.6 received additional training for reasoning, STEM, engineering and knowledge work. API pricing begins at $2 per million input and $6 per million output tokens, with a faster variant at twice the price.
Grok is the cost-sensitive proprietary option. Treat mixed self-reported and third-party benchmark tables cautiously and validate the exact partner deployment because tools and limits can differ.
Comparison by use case
Use case | First choice | Alternative | Reason |
|---|---|---|---|
Hardest autonomous research | Claude Fable 5 | GPT 5.6 Sol | Sustained reasoning and tool work |
Large document analysis | GPT 5.6 Sol | Gemini 3.7 Flash | Million-token context and tool ecosystem |
Scientific analysis | Claude Fable 5 | Claude Opus 5 | Strong research-oriented evaluations |
Multimodal reasoning | Gemini 3.7 Flash | Kimi K3 | Broad input support |
Open-weight deployment | Kimi K3 | Qwen 3.8 | Frontier open-weight capability |
Lower API cost | Grok 4.6 | Gemini 3.7 Flash | Competitive price and speed focus |
Model | Published access note | Cost position |
|---|---|---|
Claude Fable 5 | Claude products and API | Premium |
GPT 5.6 Sol | OpenAI API | Frontier with promotional pricing |
GPT 5.5 | OpenAI API | Premium output pricing |
Claude Opus 5 | Claude products and API | Balanced frontier tier |
Gemini 3.7 Flash | Gemini API and AI Studio | Throughput-oriented |
Kimi K3 | Kimi products, API and open weights | Hosted or specialist self-hosting |
Qwen 3.8 | QwenCloud and open weights | Hosted or self-hosted |
Grok 4.6 | xAI API and partners | Lower published API entry price |
Limitations and safety
No reasoning model should make consequential decisions without verification. Mathematical confidence does not imply factual freshness. Long context does not guarantee faithful retrieval. Tool access can turn a reasoning error into an external action. Keep permissions narrow, require approval for irreversible steps and maintain audit logs.
Reasoning-effort settings also change the product. Higher effort usually increases latency and cost and may not help simple tasks. Route easy queries to cheaper configurations and reserve frontier settings for tasks where additional deliberation changes outcomes.
Frequently asked questions
Which AI has the best reasoning?
Claude Fable 5 is our overall choice for the hardest long-horizon reasoning. GPT 5.6 Sol is the closest alternative and may be better inside OpenAI-based tool workflows.
Is a longer chain of thought better?
No. Length is not correctness. Score the final answer, evidence, constraint adherence and repeatability rather than how elaborate the explanation sounds.
What is the best open reasoning model?
Kimi K3 is our top open-weight reasoning model, followed by Qwen 3.8. Deployment cost and license requirements still need separate evaluation.
Final verdict
Claude Fable 5 leads for maximum reasoning capability, GPT 5.6 Sol is the best platform-oriented alternative, Claude Opus 5 offers the strongest balance, Gemini 3.7 Flash wins for fast multimodal reasoning, and Kimi K3 is the open-weight leader. Re-test on your own frozen tasks before standardizing.
Research notes
- Official product documentation checked August 26, 2026.
- No affiliate payout or vendor placement affected the order.
- Model snapshots, reasoning effort and tools are recorded because they materially change results.
- Pricing should be rechecked on official pages before purchase.