Reasoning is the capability that separates genuinely useful AI models from autocomplete systems. The ability to follow a logical chain, work through multi-step mathematics, and arrive at a correct and explainable answer is what makes a model valuable for engineering, finance, research, and legal analysis. In 2026, a new category of "thinking models" has emerged specifically targeting this capability.
The Benchmarks That Matter
Two benchmarks are most widely used to evaluate reasoning capability:
- MATH-500: 500 problems from prestigious math competitions (AMC, AIME, MATH). Problems range from algebra to number theory and require multi-step derivation, not just retrieval.
- GPQA Diamond: Graduate-level questions in physics, chemistry, and biology written by domain experts. Designed to be unsolvable by someone who hasn't deeply studied the field.
Tier 1: The Thinking Models
"Thinking models" (also called reasoning models) use an extended internal reasoning chain before producing a final answer. They're designed to "think before they speak," catching errors in their reasoning before presenting conclusions.
OpenAI o3 — The Overall Leader
o3 scores 96.7% on MATH-500 and 87.7% on GPQA Diamond — the highest scores of any model in both categories. For genuinely hard mathematical problems, competitive programming, and advanced scientific reasoning, o3 has no peer. It is also expensive ($20/M input tokens) and slow — it trades speed for accuracy by design. For time-sensitive applications, consider o3-mini instead.
Gemini 2.0 Flash Thinking — Best Value Reasoning Model
Gemini Flash Thinking achieves 92.0% on MATH-500 at a fraction of o3's cost. For most engineering and financial calculation use cases, the gap between o3 and Flash Thinking is imperceptible in practice — but the cost difference is enormous. If budget matters and you don't need absolute top-tier performance on graduate-level science, Flash Thinking is the optimal choice.
DeepSeek-R2 — Best Open-Source Reasoner
DeepSeek's R2 model is a specialised reasoning model built on the V3 architecture with extended chain-of-thought training. It achieves 94.9% on MATH-500 under the MIT license — meaning you can self-host a near-o3-level reasoning model for zero API cost. For organisations with regulatory constraints or high reasoning volume, this is exceptional value.
Tier 2: General Models with Strong Reasoning
If you need a general-purpose model that's also strong at reasoning (rather than a dedicated thinking model):
- Claude 3.5 Sonnet: 78.3% MATH-500. Best for reasoning tasks that require explanation and clarity alongside mathematical work.
- DeepSeek V3: 90.2% MATH-500. Exceptional for a general-purpose model, particularly impressive given its open-source, free-to-use status.
- GPT-4o: 76.6% MATH-500. Solid general reasoning, but dedicated thinking models have overtaken it for pure math.
Benchmark Summary
| Model | MATH-500 | GPQA Diamond | Cost/M Tokens |
|---|---|---|---|
| OpenAI o3 | 96.7% | 87.7% | $20 |
| DeepSeek-R2 | 94.9% | 79.8% | Free (OSS) |
| Gemini Flash Thinking | 92.0% | 70.7% | $0.15 |
| DeepSeek V3 | 90.2% | 59.1% | Free (OSS) |
| Claude 3.5 Sonnet | 78.3% | 65.0% | $3 |
| GPT-4o | 76.6% | 53.6% | $5 |
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