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Code Generation on BigCodeBench Full

54.2Pass@1IQuest-Coder-V1-40B-Instruct
23.31231.33139.3547.369Mar 2, 2026Mar 19, 2026Apr 6, 2026Apr 24, 2026May 11, 2026May 29, 2026Jun 16, 2026

Evaluation results

MethodLinks
IQuest-Coder-V1-40B-InstructModel Scale=20B+2026.0354.2—
TOKEN CLEANINGBase model=Seed-Coder-8B, Eff. Tokens (%)=1.72026.0653.662.8
Claude-Opus-4.5Model Category=Closed-APIs2026.0353.3—
Claude-Opus-4.52026.0653.3—
CODEBLOCKBase model=Seed-Coder-8B, Eff. Tokens (%)=1.92026.0653.165.5
FULL TOKENSBase model=Seed-Coder-8B, Eff. Tokens (%)=100.02026.0652.865.1
DS2Base model=Seed-Coder-8B, Eff. Tokens (%)=4.62026.0652.766
CLAMBase model=Seed-Coder-8B, Eff. Tokens (%)=3.62026.0652.364.9
RANDOM SELECTIONBase model=Seed-Coder-8B, Eff. Tokens (%)=1.92026.0651.663.9
Claude-Sonnet-4.5Model Category=Closed-APIs2026.0351.4—
BASEBase model=Seed-Coder-8B2026.0651.362.5
IQuest-Coder-V1-40B-ThinkingModel Scale=20B+2026.0351.1—
IQuest-Coder-V1-40B-Loop-ThinkingModel Scale=20B+2026.0350.6—
IQuest-Coder-V1-40B-Loop-InstructModel Scale=20B+2026.0349.9—
Kimi-K2-Instruct-0905Model Scale=20B+2026.0349.8—
Kimi-K2-Instruct-09052026.0649.8—
Qwen3-Coder-480B-A35B-InstructModel Scale=20B+2026.0349.4—
Qwen3-Coder-480B-A35B-Instruct2026.0649.4—
KAT-Dev-72B-ExpModel Scale=20B+2026.0348.3—
Deepseek-V3.2Model Scale=20B+2026.0348.1—
DeepSeek-V3.22026.0648.1—
Qwen2.5-Coder-32B-InstructModel Scale=20B+2026.0348—
Qwen2.5-Coder-32B-Instruct2026.0648—
IQuest-Coder-V1-14B-ThinkingModel Scale=13B+2026.0347.7—
Qwen3-235B-A22B-Instruct-2507Model Scale=20B+2026.0347.4—
Qwen3-235B-A22B-Instruct-25072026.0647.4—
Gemini-3-Pro-previewModel Category=Closed-APIs2026.0347.1—
Gemini-3-Pro2026.0647.1—
Qwen2.5-Coder-14B-InstructModel Scale=13B+2026.0347—
Qwen2.5-Coder-14B-Instruct2026.0647—
Qwen3-Coder-30B-A3B-InstructModel Scale=13B+2026.0346.9—
Kimi-K2-ThinkingModel Scale=20B+2026.0346.8—
GPT-5.1Model Category=Closed-APIs2026.0346.8—
GPT-5.12026.0646.8—
IQuest-Coder-V1-14B-InstructModel Scale=13B+2026.0346.3—
KAT-DevModel Scale=20B+2026.0346.2—
LoopCoder-v2R (number of refinement loops)=22026.0646.1—
GLM-4.7Model Scale=20B+2026.0345.7—
GLM-4.72026.0645.7—
Kimi-Dev-72BModel Scale=20B+2026.0345.4—
Kimi-Dev-72B2026.0645.4—
LFPO (All Loss)Reinforcement Learning Strategy=+ LFPO (All Loss)2026.0344.8—
AGRPOReinforcement Learning Strategy=+ AGRPO2026.0344.6—
Seed-Coder-8B-InstructModel Scale=6B+2026.0344.6—
Seed-Coder-8B-Instruct2026.0644.6—
Gemini-3-Flash-previewModel Category=Closed-APIs2026.0344.5—
Qwen3-235B-A22B-Thinking-2507Model Scale=20B+2026.0344.1—
LFPO (Neg. Only)Reinforcement Learning Strategy=+ LFPO (Neg. Only)2026.0343.4—
LoopCoder-v2R (number of refinement loops)=32026.0643.3—
LFPO (Pos. Only)Reinforcement Learning Strategy=+ LFPO (Pos. Only)2026.0343.1—
FULL TOKENSBase model=OpenCoder-8B-Base, Eff. Tokens (%)=100.02026.0642.854.5
UniGRPOReinforcement Learning Strategy=+ UniGRPO2026.0342.6—
SPGReinforcement Learning Strategy=+ SPG2026.0342.1—
DS2Base model=Qwen2.5-Coder-7B-Instruct, Eff. Tokens (%)=4.62026.0642—
DS2Base model=OpenCoder-8B-Base, Eff. Tokens (%)=4.62026.0641.454.6
CODEBLOCKBase model=Qwen2.5-Coder-7B-Instruct, Eff. Tokens (%)=1.92026.0641.4—
RANDOM SELECTIONBase model=OpenCoder-8B-Base, Eff. Tokens (%)=1.92026.0640.954.6
LoopCoder-v2R (number of refinement loops)=42026.0640.8—
IQuest-Coder-V1-7B-ThinkingModel Scale=6B+2026.0340.5—
BASEBase model=OpenCoder-8B-Base2026.0640.554.4
coupled-GRPOReinforcement Learning Strategy=+ coupled-GRPO2026.0340.4—
RANDOM SELECTIONBase model=Qwen2.5-Coder-7B-Instruct, Eff. Tokens (%)=1.92026.0640.4—
LoopCoder-v2R (number of refinement loops)=12026.0640.1—
FULL TOKENSBase model=Qwen2.5-Coder-7B-Instruct, Eff. Tokens (%)=100.02026.0639.9—
CLAMBase model=OpenCoder-8B-Base, Eff. Tokens (%)=3.52026.0639.854.6
CLAMBase model=Qwen2.5-Coder-7B-Instruct, Eff. Tokens (%)=3.52026.0639.8—
BASEBase model=Qwen2.5-Coder-7B-Instruct2026.0639.7—
CODEBLOCKBase model=OpenCoder-8B-Base, Eff. Tokens (%)=1.92026.0639.357.1
diffu-GRPOReinforcement Learning Strategy=+ diffu-GRPO2026.0339.2—
IQuest-Coder-V1-7B-InstructModel Scale=6B+2026.0338.9—
DeepSeek-Coder-V2-Lite-InstructModel Scale=6B+2026.0337.8—
Qwen2.5-Coder-7B-InstructModel Scale=6B+2026.0337.8—
DeepSeek-Coder-V2-Lite-Instruct2026.0637.8—
Qwen2.5-Coder-7B-Instruct2026.0637.8—
FULL TOKENSBase model=Qwen2.5-Coder-3B-Instruct, Eff. Tokens (%)=100.02026.0637.2—
DS2Base model=Qwen2.5-Coder-3B-Instruct, Eff. Tokens (%)=4.62026.0636.7—
CLAMBase model=Qwen2.5-Coder-3B-Instruct, Eff. Tokens (%)=3.52026.0636.7—
CODEBLOCKBase model=Qwen2.5-Coder-3B-Instruct, Eff. Tokens (%)=1.92026.0636.7—
TOKEN CLEANINGBase model=OpenCoder-8B-Base, Eff. Tokens (%)=1.72026.0635.951.3
TOKEN CLEANINGBase model=Qwen2.5-Coder-7B-Instruct, Eff. Tokens (%)=1.72026.0635.9—
DiffuCoderReinforcement Learning Strategy=Base2026.0335.7—
RANDOM SELECTIONBase model=Qwen2.5-Coder-3B-Instruct, Eff. Tokens (%)=1.92026.0635.7—
BASEBase model=Qwen2.5-Coder-3B-Instruct2026.0634.8—
CODEBLOCKBase model=Qwen2.5-Coder-1.5B-Instruct, Eff. Tokens (%)=1.92026.0631.154.6
TOKEN CLEANINGBase model=Qwen2.5-Coder-3B-Instruct, Eff. Tokens (%)=1.72026.0630.9—
DS2Base model=Qwen2.5-Coder-1.5B-Instruct, Eff. Tokens (%)=4.62026.0630.553.6
CLAMBase model=Qwen2.5-Coder-1.5B-Instruct, Eff. Tokens (%)=3.52026.0630.451.8
RANDOM SELECTIONBase model=Qwen2.5-Coder-1.5B-Instruct, Eff. Tokens (%)=1.92026.0629.953.6
FULL TOKENSBase model=Qwen2.5-Coder-1.5B-Instruct, Eff. Tokens (%)=100.02026.0629.852.7
BASEBase model=Qwen2.5-Coder-1.5B-Instruct2026.0624.849.4
TOKEN CLEANINGBase model=Qwen2.5-Coder-1.5B-Instruct, Eff. Tokens (%)=1.82026.0624.549.1