LLM Reasoning Models Comparison (o3, Claude, Gemini)

The landscape of reasoning-capable LLMs has matured dramatically. OpenAI’s o-series, Anthropic’s Claude with extended thinking, and Google’s Gemini models represent the current frontier of chain-of-thought reasoning. Here’s how they stack up.

Architecture & Approach

OpenAI o3 uses a dedicated reasoning paradigm with explicit internal token generation during “thinking” phases. Unlike standard models that generate output directly, o3 produces intermediate reasoning steps before final answers. This approach trades speed for depth—o3 achieves 87.7% on GPQA (graduate-level science questions), the highest reasoning benchmark recorded.

Claude 3.7 Sonnet (with Extended Thinking) takes a different path—Anthropic added a “think” mode where the model can spend additional compute on complex problems. Claude excels at coding (70.3% on SWE-bench) and maintains strong factual accuracy. The thinking is more transparent and controllable than o3’s internal process.

Google Gemini 2.5 Pro offers reasoning through scaled context (1M tokens) combined with implicit chain-of-thought. Gemini 3.1 Pro currently ranks #2 on LLM leaderboards with strong multimodal capabilities and the most competitive pricing at $2.50/$15 per million tokens.

Benchmark Performance (2026)

Model	MMLU	GPQA	SWE-bench	Speed
Claude Opus 4.6	91.3%	80.8%	—	1,476 t/s
Gemini 3.1 Pro	94.3%	80.6%	—	1,222 t/s
GPT-5.4	92.8%	—	—	1,146 t/s
o3 (high)	84.2%	87.7%	69.1%	85 t/s

Note: o3’s lower MMLU score reflects its focus on reasoning over memorization—a tradeoff that often indicates better generalization.

Key Tradeoffs

Speed vs. Accuracy

• o3: Slower (85 tokens/sec) but excels at mathematical and logical problems
• Claude: Balanced speed, superior coding performance
• Gemini: Fast inference with competitive pricing, massive context window

Cost Considerations

• o3: Most expensive at $10/$40 per 1M tokens
• Claude Opus: Premium tier at $5/$25
• Gemini 3.1 Pro: Mid-range at $2.50/$15

Context Windows

• Gemini: Industry-leading 1M token context
• Claude: 200K standard, expandable
• o3: 200K context, optimized for reasoning within that window

When to Use Each

Choose o3 when:

• Solving complex multi-step mathematical proofs
• Tasks requiring verified chain-of-thought reasoning
• Budget allows for compute-intensive inference

Choose Claude when:

• Software engineering and code generation
• Factual content creation requiring accuracy
• Extended conversations with context preservation

Choose Gemini when:

• Processing extremely long documents
• Multimodal tasks (text + images + audio)
• Cost-efficiency is a primary constraint

Key Takeaways

• Reasoning models represent a paradigm shift from pattern-matching to actual problem-solving
• No single model dominates across all benchmarks—selection depends on use case
• Extended thinking modes are becoming standard across providers
• Cost scales with reasoning depth—evaluate whether your use case truly needs chain-of-thought
• The gap between reasoning and non-reasoning models continues to widen on complex tasks

The reasoning model space is evolving rapidly. For production systems, consider implementing model routing that selects the appropriate reasoning depth based on query complexity.