benchgap
Calibration

Vibe Code Bench → Vals SWE-bench

Vals SWE-bench is estimated from Vibe Code Bench with a offset logistic curve fitted on 19 models measured on both: y = 0.0000 + (0.8323 − 0.0000) / (1 + exp(−4.17·(x − -0.2546))), R² = 0.57, cross-validated error 6.3 pp. It is used for 18 estimates.

Estimated modelVibe Code BenchVals SWE-benchSource
Mistral Large 478.4%82.1%estimated ± 6.3 pp, low confidence
GPT-5.253.5%80.2%estimated ± 6.3 pp, medium confidence
Claude Opus 4.6 (Adaptive)53.5%80.2%estimated ± 6.3 pp, medium confidence
GLM-5 (Reasoning)23.4%73.6%estimated ± 6.3 pp, medium confidence
Claude Sonnet 4.5 Thinking22.6%73.3%estimated ± 6.3 pp, medium confidence
GPT-5.1-Codex-Max22.2%73.2%estimated ± 6.3 pp, medium confidence
Claude Opus 4.5 Thinking20.6%72.6%estimated ± 6.3 pp, medium confidence
Qwen3.5 Plus15.7%70.5%estimated ± 6.3 pp, medium confidence
MiniMax M2.514.9%70.1%estimated ± 6.3 pp, medium confidence
Gemini 3 Pro14.3%69.9%estimated ± 6.3 pp, medium confidence
GPT-5 mini14.2%69.8%estimated ± 6.3 pp, medium confidence
GPT-5.1-Codex13.1%69.3%estimated ± 6.3 pp, medium confidence
Claude Haiku 4.5 Thinking11.4%68.5%estimated ± 6.3 pp, medium confidence
DeepSeek V3.2 (Thinking)5.1%65.0%estimated ± 6.3 pp, medium confidence
Qwen3 Max3.5%64.1%estimated ± 6.3 pp, medium confidence
GLM-4.63.1%63.8%estimated ± 6.3 pp, medium confidence
Grok 4.1 Fast (Reasoning)1.2%62.6%estimated ± 6.3 pp, medium confidence
Grok 4 Fast (Reasoning)0.0%61.8%estimated ± 6.3 pp, medium confidence

Fitted on

ModelVibe Code BenchVals SWE-bench
Claude Opus 4.771.0%82.0%
Claude Sonnet 4.651.5%77.4%
DeepSeek V4 Pro 081349.9%96.4%
Gemini 2.5 Pro0.4%54.4%
Gemini 3.1 Flash-Lite0.0%62.8%
Gemini 3.1 Pro32.0%78.8%
Gemini 3.5 Flash48.7%78.8%
Gemini 3 Flash20.2%75.0%
GLM-5.131.5%76.4%
GPT-5.2-Codex37.9%72.4%
GPT-5.3 Codex61.8%78.0%
GPT-5.4 mini48.0%73.0%
GPT-5.4 nano26.1%69.8%
GPT-5.569.8%82.6%
Grok 4.204.1%72.2%
Kimi K2.637.9%76.2%
MiniMax M2.727.0%73.8%
Muse Spark19.7%74.4%
Qwen3.6 Plus25.6%73.4%