Benchmarks / Math

ArXivMath 06/2026 (MathArena)

Research-level math problems with a checkable final answer, drawn from arXiv papers submitted in June 2026; each edition is restricted to papers published within the last month to reduce training-data contamination. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).

26models
–Open Index weight
–last updated

ArXivMath 06/2026 (MathArena)

Higher is better · best effort per model

Chart settings
26 of 26 selected

full table below

#ModelEffortScoreCost / taskHarness
1GPT-6.1 Solmax94.4%––
2GPT-6 Astramax94.4%––
3GPT-6 Solmax91.0%––
4Fable 5.1max91.0%––
5GPT-5.6 Solmax88.5%––
6Fable 5max85.4%––
7GPT-5.5xhigh83.6%––
8Opus 5.5high83.3%––
9Opus 5max82.6%––
10Muse Spark 1.3max76.7%––
11Qwen 3.8 Maxmax75.7%––
12Kimi K3max71.5%––
13Grok 4.7xhigh70.1%––
14Opus 4.8max70.0%––
15Gemini 3.8 Flashmax67.4%––
16Gemini 3.1 Prohigh66.7%––
17Grok 4.5max66.7%––
18Gemini 3.7 Flashmax62.5%––
19DeepSeek V4.1 Flashmax57.6%––
20Gemini 3.6 Flashxhigh56.9%––
21Muse Spark 1.1max56.9%––
22Gemini 3.5 Flashxhigh52.1%––
23GLM 5.2max46.5%––
24DeepSeek V4 Flashmax43.8%––
25Qwen 3.6 35B–27.1%––
26Qwen 3.5 2Blow4.2%––