The Inference Report

October 14, 2026

The SWE-rebench rankings show no movement from the previous cycle, with AnthropicFable 5 holding the top position at 64.5%±1.41%, followed by GrokGrok 4.5 at 63.8%±0.60% and AnthropicOpus 5 at 63.4%±1.35%. The coding benchmark remains stable across its top seventeen entries, suggesting either that the evaluation window was too short to detect meaningful shifts or that these models have reached a plateau on this particular task distribution. The confidence intervals are tight enough that small variations would be noise rather than progress. Artificial Analysis shows considerably more churn, with Agnes 2.5 Pro Beta entering at position 42 as a new entry while the rest of the leaderboard shuffled downward by one position each, a pattern consistent with a single insertion rather than wholesale reordering. The median score in the Artificial Analysis top 100 hovers around 20 points, roughly one-third the performance of top SWE-rebench models, indicating the two benchmarks measure different problem classes or that Artificial Analysis weights harder instances more heavily. Without knowing the methodology behind Artificial Analysis or whether its test set was refreshed, it is difficult to assess whether the insertion of Agnes 2.5 Pro Beta represents genuine capability advancement or dataset drift. The SWE-rebench stability is more trustworthy given its explicit error bars, but it also invites the question of whether the benchmark is sensitive enough to detect incremental improvements, or whether the top performers have simply saturated the task space.

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 5.557.696$8.00
2Claude Sonnet 5.556133$4.00
3Muse Wonder Full55.80$0.00
4Claude Fable 5.153.465$20.00
5GPT-6 Astra52.752$20.00
6Gemini 4 Argon52.60$4.00
7Muse Wonder52.4113$8.00
8GPT-6.1 Sol51.856$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 5.5238
2Ling 3.1 Flash216
3Gemini 3.8 Flash134
4Claude Sonnet 5.5133
5Muse Wonder113
6Muse Spark 1.3112
7GPT-5.6 Terra106
8Claude Opus 5.596
9Step 5 Preview87
10GLM-5.368

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