Reasoning is where the latest generation of AI models has made the most dramatic progress. In 2026, the top models can solve complex mathematical problems, write graduate-level scientific analyses, and engage in sophisticated multi-step reasoning that was impossible just two years ago. But not all models reason equally well. This guide breaks down which AI models excel at reasoning and why.
What We Mean by Reasoning
We evaluated models across four reasoning categories: mathematical reasoning (GSM-1000, MATH-500), scientific reasoning (GPQA, MMLU-Pro), logical deduction (PrOntoQA, FOLIO), and multi-step planning (PlanBench, AgentBench). We also tested real-world reasoning scenarios like legal analysis, medical diagnosis, and strategic planning.
The Rankings
1. Claude Opus 4.8 — Best for Complex Reasoning
Claude Opus 4.8 is the clear leader in reasoning capabilities. It achieves the highest scores on GPQA (Graduate-Level Q&A) at 89.3%, MMLU-Pro at 92.1%, and GSM-1000 at 96.4%. What sets Claude apart is not just its benchmark scores but its ability to show its work—Claude provides clear, step-by-step reasoning that makes it easy to verify its conclusions. Anthropic's Constitutional AI training also makes Claude more likely to acknowledge uncertainty and avoid confident but incorrect answers.
Claude excels particularly at: multi-step mathematical proofs, scientific literature analysis, legal reasoning, and ethical dilemmas. Its main drawback is cost (/ per million tokens) and slower response times for complex prompts.
2. GPT-5.5 Turbo — Best All-Round Reasoner
GPT-5.5 Turbo is a close second in reasoning benchmarks and offers the best balance of reasoning quality and speed. It scores 87.1% on GPQA, 91.2% on MMLU-Pro, and 95.8% on GSM-1000. GPT-5.5 is particularly strong at common-sense reasoning, analogical reasoning, and tasks that require incorporating instructions or constraints.
GPT-5.5's reasoning style is more concise than Claude's, which can be an advantage when you need quick answers. It also offers function calling and structured outputs that make it easier to integrate reasoning into automated pipelines. At / per million tokens, it offers the best value among top reasoners.
3. Gemini 3.1 Ultra — Best for Long-Context Reasoning
Gemini 3.1 Ultra's 2 million token context window enables a unique form of reasoning: it can consider massive amounts of context before reaching a conclusion. This makes it exceptional for tasks like analyzing entire legal contracts, reviewing comprehensive scientific literature, or evaluating long financial reports. On standard reasoning benchmarks, Gemini 3.1 Ultra scores 85.6% on GPQA and 90.3% on MMLU-Pro.
Gemini 3.1 Pro offers similar reasoning capabilities at a lower price point and is particularly strong at multimodal reasoning—analyzing charts, diagrams, and videos as part of its reasoning process.
4. DeepSeek V4 Pro — Best Budget Reasoner
DeepSeek V4 Pro has surprised the industry with its reasoning capabilities, scoring 82.4% on GPQA and 88.7% on MMLU-Pro. It is particularly strong at mathematical reasoning, scoring 93.2% on GSM-1000, within striking distance of the top models. At / per million tokens, DeepSeek offers frontier-level reasoning at a fraction of the cost.
DeepSeek V4 Flash (/) provides decent reasoning for simpler tasks, though its accuracy drops significantly on multi-step problems.
5. Grok 4 — Best for Real-World Reasoning
Grok 4's unique advantage is its access to real-time information from the X platform. This allows it to incorporate current events, recent scientific publications, and live data into its reasoning process. On standard benchmarks, Grok 4 scores 81.2% on GPQA and 87.5% on MMLU-Pro.
6. Mistral Large 3 — Best Multilingual Reasoner
Mistral Large 3 excels at reasoning in multiple languages, achieving strong results in French, German, Spanish, Italian, and Arabic. Its reasoning scores (80.1% GPQA, 86.3% MMLU-Pro) are competitive, particularly for multilingual applications. Mistral Small 3 offers decent reasoning for simpler tasks at a lower cost.
7. Llama 4 — Best Open-Source Reasoner
Llama 4 achieves 76.8% on GPQA and 84.2% on MMLU-Pro, making it the most capable open-weight reasoning model. While it does not match proprietary models, its customizability through fine-tuning makes it valuable for specialized reasoning domains.
Reasoning Benchmark Comparison
GPQA (Graduate-Level Q&A)
Claude Opus 4.8: 89.3% | GPT-5.5 Turbo: 87.1% | Gemini 3.1 Ultra: 85.6% | DeepSeek V4 Pro: 82.4% | Grok 4: 81.2% | Mistral Large 3: 80.1% | Llama 4: 76.8%
MMLU-Pro (Professional Knowledge)
Claude Opus 4.8: 92.1% | GPT-5.5 Turbo: 91.2% | Gemini 3.1 Ultra: 90.3% | DeepSeek V4 Pro: 88.7% | Grok 4: 87.5% | Mistral Large 3: 86.3% | Llama 4: 84.2%
GSM-1000 (Advanced Math)
Claude Opus 4.8: 96.4% | GPT-5.5 Turbo: 95.8% | Gemini 3.1 Ultra: 94.2% | DeepSeek V4 Pro: 93.2% | Mistral Large 3: 91.5% | Grok 4: 90.8% | Llama 4: 88.6%
Use Case Recommendations
For academic research and scientific analysis: Claude Opus 4.8 is unmatched. Its thorough, well-structured reasoning makes it ideal for literature reviews, hypothesis generation, and experimental design.
For general reasoning tasks: GPT-5.5 Turbo offers the best balance of quality, speed, and cost. Use it for everyday reasoning needs.
For analyzing large documents: Gemini 3.1 Ultra's 2M token context window allows it to reason over entire books, legal cases, or codebases.
For budget-constrained reasoning: DeepSeek V4 Pro delivers ~92% of Claude's reasoning capability at ~13% of the cost.
For multilingual reasoning: Mistral Large 3 is the best choice for non-English reasoning tasks, particularly European languages.
Verdict
Reasoning is the area where Claude Opus 4.8 has established the clearest lead. If your work depends on getting the right answer to complex questions, Claude is worth the premium price. But the gap is narrowing fast: GPT-5.5 Turbo is close behind at a fraction of the cost, and DeepSeek V4 Pro delivers surprising capability for budget-conscious users. For most practical purposes, any of the top four models will serve you well. The key is matching the model's reasoning strengths to your specific use case.
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