The Inference Report

September 7, 2026

Daily rankings from SWE-rebench, a benchmark designed to fairly compare LLM capabilities on real-world software engineering tasks. Unlike other evaluations, it uses a standardized scaffolding for all models, continuously updates its dataset to prevent contamination, and runs each model five times to account for stochastic variance.

#ModelScore
1AnthropicFable 5 [high]Model64.5%± 1.41%
2GrokGrok 4.5 [high]Model63.8%± 0.60%
3AnthropicOpus 5 [high]Model63.4%± 1.35%
4Z.aiGLM-5.2 [high]Model62.9%± 1.19%
5OpenAIGPT-5.6 Sol [medium]Model62.3%± 1.83%
6JunieJunieAgent61.8%± 0.54%
7AnthropicClaude CodeAgent60.4%± 1.03%
8OpenAICodexAgent58.0%± 1.29%
9AnthropicSonnet 5 [high]Model56.8%± 0.94%
10CursorCursorAgent51.7%± 0.84%

Artificial Analysis composite index across coding, math, and reasoning benchmarks.

#ModelScoretok/s$/1M
1Claude Fable 5.156.870$20.00
2GPT-6 Astra54.761$20.00
3Claude Opus 554.149$10.00
4Claude Fable 553.259$20.00
5Muse Spark 1.353177$2.00
6GPT-5.6 Sol51.379$8.00
7Grok 4.650.657$3.00
8Kimi K350.239$6.00
9GLM-5.348.677$2.15
10Gemini 3.8 Flash47.1340$1.50

Output tokens per second — higher is faster. Minimum intelligence score of 40.

#Modeltok/s
1Gemini 3.8 Flash340
2Gemini 3.7 Flash284
3Muse Spark 1.2224
4Gemini 3.6 Flash186
5Muse Spark 1.3177
6DeepSeek V4 Flash Vision121
7DeepSeek V4 Flash 0731118
8GPT-5.6 Luna110
9GPT-5.6 Terra104
10GPT-5.6 Sol79

Blended cost per 1M tokens (3:1 input/output) — lower is cheaper. Minimum intelligence score of 40.

#Model$/1M
1Qwen3.8-Flash-Next$0.23
2GLM-5.3-Flash$0.237
3GPT-5.6 Luna$0.45
4DeepSeek V4 Flash Vision$0.66
5DeepSeek V4 Flash 0731$0.66
6Qwen3.8 27B$1.13
7Gemini 3.8 Flash$1.50
8Gemini 3.7 Flash$1.50
9Gemini 3.6 Flash$1.50
10DeepSeek V4 Pro 0813$1.98