Reasoning Model Rankings
Performance scores, pricing, and context windows for reasoning-focused tasks.
| Rank | Model | Score | Pricing ($/M) | Context | Key Strengths |
|---|---|---|---|---|---|
| #1 | Claude Opus 4.8 Anthropic | 9.4/10 | $5 / $25 | 1M | Best at nuanced analysis • Excellent tradeoff evaluation • Strong strategic thinking |
| #2 | Kimi K3 Moonshot | 9.2/10 | $3 / $15 | 1M | Configurable reasoning effort • Native visual understanding • 1M context |
| #3 | GLM-5 Zhipu AI | 8.6/10 | $0.5 / $0.5 | 128K | Strong analysis • Ultra-low cost • Enterprise ready |
| #4 | GPT-5.5 OpenAI | 8.5/10 | $5 / $30 | 1M | Good general reasoning • Fast responses • Strong ecosystem |
| #5 | Gemini 3.1 Pro Preview Google | 8.4/10 | $2 / $12 | 1M | Massive context • Research synthesis • Good multimodal |
| #6 | MiniMax M2.5 MiniMax | 8.3/10 | $0.5 / $2 | 245K | Budget option • Fast inference • Good for scale |
Benchmark Breakdown
How top models perform across specific reasoning tasks (scale: 1-10).
| Task | Description | Claude | Kimi | GLM-5 | GPT |
|---|---|---|---|---|---|
| Tradeoff Analysis | Evaluating pros/cons of technical decisions | 9.5 | 9.3 | 8.7 | 8.4 |
| Root Cause Analysis | Diagnosing problems from symptoms | 9.4 | 9.1 | 8.5 | 8.6 |
| Strategic Planning | Multi-step plans with dependencies | 9.3 | 9.2 | 8.4 | 8.3 |
| Logical Deduction | Following logical chains to conclusions | 9.2 | 9.4 | 8.8 | 8.5 |
| Risk Assessment | Identifying and prioritizing risks | 9.4 | 8.9 | 8.5 | 8.2 |
| Comparative Analysis | Comparing options against criteria | 9.5 | 9 | 8.6 | 8.5 |
When to Use Which Model
Decision guide for picking the right reasoning model for your situation.
Executive decision support
Best at nuanced analysis and presenting tradeoffs clearly. Excels at "it depends" scenarios.
Technical architecture decisions
Deep understanding of system design tradeoffs and can articulate long-term implications.
Long-context analysis tasks
Kimi K3 supports configurable reasoning effort and a 1M-token context window.
Budget-constrained projects
Solid reasoning capability at $0.50/$0.50 per million tokens. Best value for analysis tasks.
Research synthesis
1M context window lets you include extensive background material for comprehensive analysis.
Frequently Asked Questions
What is the best AI model for reasoning in 2026?
+The right choice depends on your task, evaluation setup, cost ceiling, and deployment constraints. Our displayed scores are editorial fit scores, not universal benchmark results; compare source-backed specifications and test finalists on representative work.
What tasks count as "reasoning" in your benchmarks?
+We test: tradeoff analysis (comparing options), root cause analysis (diagnosing problems), strategic planning (multi-step plans), logical deduction (following chains), risk assessment, and comparative analysis against criteria.
Why does Claude excel at reasoning?
+Claude tends to acknowledge uncertainty, present multiple perspectives, and avoid overconfident assertions. It excels at "it depends" scenarios where context matters more than binary answers.
Is reasoning capability worth paying more for?
+For critical decisions, yes. Better reasoning means fewer costly mistakes and more thorough analysis. For routine tasks, models like Kimi or GLM offer strong reasoning at lower cost.
How do I test reasoning quality myself?
+Give models ambiguous scenarios with no clear right answer. Better reasoning models will acknowledge tradeoffs, ask clarifying questions, and present balanced perspectives rather than confident wrong answers.
See Full Daily Scorecards
Get detailed task-level breakdowns, failure cases, and cost analysis for every model we test.
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