The Inference Report

November 4, 2026

The SWE-rebench leaderboard shows no movement at the top tier, with AnthropicFable 5, GrokGrok 4.5, and AnthropicOpus 5 holding positions 1 through 3 at 64.5%, 63.8%, and 63.4% respectively, all within their reported confidence intervals. The Artificial Analysis benchmark tells a different story: Gemini 4 Carbon gained 0.6 points to reach 60.0, Claude Opus 5.5 improved 0.7 points to 59.1, and two new entries appeared (GPT-6 Sol at position 11 with 44.2 and GPT-5.6 Terra at position 18 with 32.3), suggesting either model releases or evaluation methodology adjustments not reflected in the SWE-rebench data. The gap between the two benchmarks is instructive: SWE-rebench clusters the top five models within 2.2 percentage points, a tight range that reflects the difficulty of the task and the convergence of high-capacity models on this specific evaluation, while Artificial Analysis spreads the same performers across a 3.1 point range, indicating different test construction or scoring criteria. Within SWE-rebench, the confidence intervals are narrow enough that single-point movements would be meaningful, yet none occurred, suggesting either genuine stability in model performance or evaluation variance that masks real differences. The lower-ranked models show marginal gains across Artificial Analysis (most under 0.3 points), consistent with noise rather than systematic improvement. Neither benchmark provides evidence of a qualitative shift in coding capability this cycle.

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
1Gemini 4 Carbon6089$8.00
2Claude Opus 5.559.196$8.00
3GPT-6 Astra56.951$20.00
4GPT-6.1 Sol54.661$4.00
5Claude Sonnet 5.554.5133$4.00
6GPT-6.1 Terra51.3108$2.00
7Gemini 4 Argon50.482$8.00
8Muse Wonder Full50.10$0.00
9Claude Fable 5.15050$20.00
10Kimi K3.1 Pro46.528$4.00

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

#Modeltok/s
1Claude Sonnet 5.5133
2Muse Wonder123
3GPT-6.1 Terra108
4Claude Opus 5.596
5Gemini 4 Carbon89
6Gemini 4 Argon82
7GPT-6.1 Sol61
8GPT-6 Astra51
9Claude Fable 5.150
10Kimi K3.1 Pro28

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

#Model$/1M
1GPT-6.1 Terra$2.00
2GPT-6.1 Sol$4.00
3Claude Sonnet 5.5$4.00
4Kimi K3.1 Pro$4.00
5GPT-6 Sol$4.00
6Gemini 4 Carbon$8.00
7Claude Opus 5.5$8.00
8Gemini 4 Argon$8.00
9Muse Wonder$8.00
10GPT-6 Astra$20.00