# Vals MMLU-Pro → HLE w/o tools

Calibration · https://benchgap.net/calibration/1565 · data retrieved 2026-10-29

HLE w/o tools is estimated from Vals MMLU-Pro with a offset logistic curve fitted on 19 models measured on both: y = 0.2510 + (0.5738 − 0.2510) / (1 + exp(−64.54·(x − 0.8764))), R² = 0.86, cross-validated error 5.1 pp. It is used for 9 estimates.

| Estimated model | Vals MMLU-Pro | HLE w/o tools | Source |
|---|---|---|---|
| [Gemini 3.1 Flash-Lite](https://benchgap.net/model/gemini-3-1-flash-lite.md) | 86.2% | 34.2% | estimated ± 5.1 pp, medium confidence |
| [GLM-5.3-Flash](https://benchgap.net/model/glm-5-3-flash.md) | 86.1% | 33.8% | estimated ± 5.1 pp, medium confidence |
| [Qwen3.5 Flash](https://benchgap.net/model/qwen3-5-flash.md) | 84.1% | 28.1% | estimated ± 5.1 pp, medium confidence |
| [MiMo-V2.5](https://benchgap.net/model/mimo-v2-5.md) | 82.9% | 26.5% | estimated ± 5.1 pp, medium confidence |
| [GLM-4.5](https://benchgap.net/model/glm-4-5.md) | 81.2% | 25.6% | estimated ± 5.1 pp, medium confidence |
| [Claude Haiku 4.5](https://benchgap.net/model/claude-haiku-4-5.md) | 78.7% | 25.2% | estimated ± 5.1 pp, medium confidence |
| [Mistral Medium 3.5 128B](https://benchgap.net/model/mistral-medium-3-5-128b.md) | 75.3% | 25.1% | estimated ± 5.1 pp, low confidence |
| [Laguna XS.2](https://benchgap.net/model/laguna-xs-2.md) | 69.1% | 25.1% | estimated ± 5.1 pp, low confidence |
| [Laguna M.1](https://benchgap.net/model/laguna-m-1.md) | 68.8% | 25.1% | estimated ± 5.1 pp, low confidence |

## Fitted on

| Model | Vals MMLU-Pro | HLE w/o tools |
|---|---|---|
| [Claude Fable 5.1](https://benchgap.net/model/claude-fable-5-1.md) | 92.4% | 60.9% |
| [Claude Opus 4.8](https://benchgap.net/model/claude-opus-4-8.md) | 89.6% | 49.8% |
| [Claude Opus 5](https://benchgap.net/model/claude-opus-5.md) | 91.6% | 56.3% |
| [Claude Sonnet 5](https://benchgap.net/model/claude-sonnet-5.md) | 87.5% | 43.2% |
| [Gemini 3.1 Pro](https://benchgap.net/model/gemini-3-1-pro.md) | 91.0% | 45.4% |
| [GLM-5.2](https://benchgap.net/model/glm-5-2.md) | 86.7% | 40.5% |
| [GPT-5.4 mini](https://benchgap.net/model/gpt-5-4-mini.md) | 84.6% | 28.2% |
| [GPT-5.4 nano](https://benchgap.net/model/gpt-5-4-nano.md) | 77.2% | 24.3% |
| [GPT-5.5](https://benchgap.net/model/gpt-5-5.md) | 88.1% | 41.4% |
| [Grok 4.20](https://benchgap.net/model/grok-4-20-beta.md) | 86.3% | 31.6% |
| [Inkling](https://benchgap.net/model/inkling.md) | 86.3% | 30.0% |
| [Inkling-Small](https://benchgap.net/model/inkling-small.md) | 85.6% | 31.6% |
| [Kimi K3](https://benchgap.net/model/kimi-k3.md) | 88.0% | 43.5% |
| [MiMo-V2.5-Pro](https://benchgap.net/model/mimo-v2-5-pro.md) | 84.6% | 34.0% |
| [Muse Spark](https://benchgap.net/model/muse-spark.md) | 87.3% | 42.8% |
| [Muse Spark 1.1](https://benchgap.net/model/muse-spark-1-1.md) | 88.7% | 52.2% |
| [Nemotron 3 Ultra](https://benchgap.net/model/nemotron-3-ultra.md) | 85.8% | 26.7% |
| [Qwen3.8-27B](https://benchgap.net/model/qwen3-8-27b.md) | 84.3% | 30.8% |
| [Qwen3.8 Max](https://benchgap.net/model/qwen3-8-max.md) | 88.6% | 43.6% |