The Inference Report

August 23, 2026

The SWE-rebench rankings remain static across the top tier, with AnthropicFable 5 holding 64.5% and the next four positions unchanged through GPT-5.6 Sol at 62.3%. The stability here reflects narrow confidence intervals (most under 1.8 percentage points), suggesting these measurements have settled into reliable territory. Artificial Analysis, by contrast, shows substantial churn below the top twenty entries, with models like LFM2.5-1.2B-Thinking newly appearing at rank 402 and numerous repositionings throughout the mid-tier that indicate either methodological differences between the two benchmarks or genuine performance variance on different evaluation sets. The gap between SWE-rebench's top performer and its seventeenth-ranked model spans 47.4 percentage points (64.5% to 17.1%), whereas Artificial Analysis compresses the same span from rank 1 to 17 into just 9.9 points (63.1 to 53.2), suggesting SWE-rebench may be testing a narrower or more discriminative problem space. Within Artificial Analysis, the long tail below rank 100 shows models clustering tightly at single-digit scores, making positional shifts there largely noise. The absence of movement in SWE-rebench's top ten over this cycle indicates either that the benchmark has reached saturation at its current scale or that frontier models are converging on its difficulty floor, a pattern worth examining against the benchmark's design rather than interpreting as stagnation in model capability.

Cole Brennan

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 Opus 563.153$10.00
2Claude Fable 562.167$20.00
3GPT-5.6 Sol60.978$11.25
4Grok 4.660.958$3.00
5Kimi K359.735$6.00
6GLM-5.359.50$2.15
7Qwen3.8 Max58.145$3.00
8Qwen3.8 2.4T A95B57.745$3.00
9Claude Opus 4.857.30$10.00
10Muse Spark 1.256.80$2.00

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

#Modeltok/s
1Gemini 3.7 Flash328
2Gemini 3.6 Flash201
3Nex-N2-Pro139
4GPT-5.6 Luna132
5GPT-5.3 Codex132
6Inkling Small129
7Gemini 3.1 Pro Preview120
8GPT-5.6 Terra119
9MiniMax-M3116
10DeepSeek V4 Flash 0731112

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

#Model$/1M
1DeepSeek V4 Flash$0.168
2Hy3$0.241
3GPT-5.6 Luna$0.45
4MiniMax-M3$0.525
5Solar Pro 4$0.525
6Inkling Small$0.525
7DeepSeek V4 Pro$0.544
8MiMo-V2.5-Pro$0.544
9DeepSeek V4 Flash 0731$0.66
10Nex-N2-Pro$1.00