The Inference Report

October 15, 2026

The SWE-rebench coding benchmark shows no movement at the top tier, with AnthropicFable 5 holding 64.5 percent, GrokGrok 4.5 at 63.8 percent, and AnthropicOpus 5 at 63.4 percent, their positions and confidence intervals unchanged from the previous cycle. The Artificial Analysis benchmark, by contrast, exhibits substantial churn across its 473-model ranking, though the shifts are almost entirely cosmetic: most models moved one to three positions while their scores remained identical to prior measurements, suggesting the list was simply resorted or expanded with new entries rather than re-evaluated. For example, MiMo-V2.5 dropped from position 86 to 116 despite maintaining a 25.2 score, while Mercury 2 fell from 177 to 228 with no score change, and Llama 4 Maverick moved from 235 to 253 at 10.0 points. A small number of models did show genuine score shifts: Qwen3 32B improved from 8.6 to 7.3, Qwen3 14B fell from 8.2 to 7.6, and Mistral Small 3.2 dropped from 8.2 to 7.0, though the direction and magnitude of these changes lack clear pattern. The methodology of Artificial Analysis remains opaque, with no published evaluation protocol or reproducibility details, making it impossible to assess whether score changes reflect model improvements, benchmark refinement, or measurement noise. The SWE-rebench data, by contrast, includes confidence intervals that permit statistical interpretation, though the absence of any top-tier movement over this period suggests either genuine performance saturation at the frontier or insufficient sensitivity in the benchmark to detect improvements when they occur.

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 5.557.698$8.00
2Claude Sonnet 5.556139$4.00
3Muse Wonder Full55.80$0.00
4Claude Fable 5.153.466$20.00
5GPT-6 Astra52.746$20.00
6Gemini 4 Argon52.60$4.00
7Muse Wonder52.4117$8.00
8GPT-6.1 Sol51.849$4.00
9Claude Opus 550.80$10.00
10Claude Fable 549.60$20.00

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

#Modeltok/s
1Claude Haiku 5.5237
2Ling 3.1 Flash210
3Claude Sonnet 5.5139
4Gemini 3.8 Flash129
5Muse Spark 1.3121
6Muse Wonder117
7GPT-5.6 Terra106
8Claude Opus 5.598
9Step 5 Preview87
10Grok 4.775

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

#Model$/1M
1Claude Haiku 5.5$0.20
2GLM-5.3-Flash$0.237
3Ling 3.1 Flash$0.45
4MiMo-V2.6-Pro$0.544
5Step 5 Preview$1.43
6Gemini 3.8 Flash$1.50
7Muse Spark 1.3$2.00
8GLM-5.3$2.15
9Grok 4.7$3.00
10Qwen3.8 Max$3.00