- Context
- 33Ktokens
- Released
- 1y ago
- Size
- 8Bparameters
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Arena leaderboards and other sourced evaluations appear below; missing scores are not zero.
How it ranks
Against its predecessor, similarly priced models and the models ranked around it.
DeepSWE 1.1
Best pass@1 per model · Higher is better
Not evaluated in DeepSWE 1.1
Arena Text
Best Arena rating per model · Higher is better
Not ranked on Arena Text yet
Arena WebDev
Best Arena rating per model · Higher is better
Not ranked on Arena WebDev yet
Arena Search
Best Arena rating per model · Higher is better
Not ranked on Arena Search yet
Agent Arena
Best IPS score per model · Higher is better
Not ranked on Agent Arena yet
GPQA diamond
Best score per model · Higher is better
#125 of 161
- 81.8%
o3Similar release date - 79.2%
Claude Sonnet 4Similar release date - 63.8%
Qwen3 14BSimilar release date - 60.5%
GLM 4.7 FlashNearby rank - 59.5%
Mistral Medium 3Similar release date - 56.8%
Qwen3 8B - 56.1%Qwen MaxNearby rank
- 56.1%
Phi 4Nearby rank
Ahead of 0 of 4 models released around the same time
OTIS Mock AIME 2024-2025
Best score per model · Higher is better
#116 of 158
- 84.4%o3Similar release date
- 70.8%
Gemini 2.5 Flash Preview 05-20Similar release date - 66.4%Qwen3 14BSimilar release date
- 58.3%GLM 4.7 FlashNearby rank
- 57.8%Claude Sonnet 3.7Nearby rank
- 56.1%Qwen3 8B
- 55.8%Qwen3.5 4BNearby rank
- 55.6%
DeepSeek-R1-Distill-Qwen-32BNearby rank - 32.2%Mistral Medium 3Similar release date
Ahead of 1 of 4 models released around the same time
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
All published scores
- GPQA diamond#125 of 16156.8%
- none47.5%
- default56.8%
Source:Epoch AI evaluation - OTIS Mock AIME 2024-2025#116 of 15856.1%
- none22.2%
- default56.1%
Source:Epoch AI evaluation
Benchmark compilation: Epoch AI, Capabilities & benchmarking (CC BY attribution). Scores are converted to percentages and model names matched to the Vertical Tabs catalog. Individual evaluations are credited to their original sources. External results may carry separate licences and evaluation configurations; do not interpret scores from different harnesses as directly comparable.