HMMT 2026 FebBrowse 284 families
HMMT 2026 Feb
Can the model solve difficult problems that require more than factual recall?
Version / measure
- Domain
- Reasoning
- Ranked models
- 7
- Reported values
- 11
- Reports
- 1 · lab
- Direction
- Higher is better
- Status
- Version not specified
Rankings
Best reported score per model across effort settings
| # | Model | Best setting | Score |
|---|---|---|---|
| 1 | GPT-5.4OpenAI | xhighcost n/a1 value · Apr 26 · source | 97.7% |
| 2 | Claude Opus 4.6Anthropic | maxcost n/a1 value · Apr 26 · source | 96.2% |
| 3 | DeepSeek V4 ProDeepSeekOW | maxcost n/a3 values · Apr 26 · source | 95.2% |
| 4 | DeepSeek-V4-FlashDeepSeekOW | maxcost n/a3 values · Apr 26 · source | 94.8% |
| 5 | Gemini 3.1 ProGoogle | highcost n/a1 value · Apr 26 · source | 94.7% |
| 6 | Kimi K2.6Moonshot AIOW | reportedcost n/a1 value · Apr 26 · source | 92.7% |
| 7 | GLM-5.1Z.aiOW | reportedcost n/a1 value · Apr 26 · source | 89.4% |
Effort curve
Score vs. reported cost. Lines connect complete sweeps only.
No cost-linked effort sweep for this version.
Definition and comparison boundarypublic methodology
Can the model solve difficult problems that require more than factual recall?
Multi-step reasoning on academic, mathematical, or abstract problems. Higher is better. The value is the percentage reported in this lab's table.
Harness, tools, prompts, attempt count, and benchmark version can materially change the result. Compare within one reporting context.
Frontier comparison and across-modes table.
Method / source ↗