The Inference Report

October 19, 2026

The SWE-rebench standings remain unchanged across the top tier, with AnthropicFable 5 holding 64.5%±1.41%, followed by GrokGrok 4.5 at 63.8%±0.60% and AnthropicOpus 5 at 63.4%±1.35%, indicating stability in the coding agent benchmark where the confidence intervals are tight enough to suggest genuine performance separation. On Artificial Analysis, one new entry appears: DeepSeek V4.1 Flash 1017 enters at rank 17 with 45.1, pushing prior entries down by one position, though the score falls within the established distribution and represents incremental rather than disruptive movement. The broader Artificial Analysis leaderboard shows typical churn in the 200-400 range where models cluster within narrow score bands (5-15 points), making positional shifts frequent but often meaningless; notably, the top 16 models remain identical to the prior snapshot, with Claude Opus 5.5 at 57.6 and Claude Sonnet 5.5 at 56 unchanged. SWE-rebench's methodology, evaluating code agents on real GitHub issues with structured evaluation, appears more discriminating than Artificial Analysis's broader assessment, as evidenced by the larger gaps between top performers (Fable 5 to Sonnet 5: 7.7 percentage points) versus the compressed middle sections of the general benchmark. The absence of movement in SWE-rebench's top 17 suggests either that these models have reached a performance plateau on this specific task or that the benchmark's variance estimates are sufficiently large to mask genuine improvements; the ±1.41% confidence interval around Fable 5's score, for instance, creates a band where competitors would need substantial gains to register as clear winners. Neither benchmark shows evidence of a category-defining shift, the data reflects incremental optimization within established model families rather than new architectural approaches breaking through.

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 5.556139$4.00
3Muse Wonder Full55.80$0.00
4Claude Fable 5.153.474$20.00
5GPT-6 Astra52.772$20.00
6Gemini 4 Argon52.60$4.00
7Muse Wonder52.4159$8.00
8GPT-6.1 Sol51.854$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 5.5241
2Ling 3.1 Flash216
3DeepSeek V4.1 Flash 1017215
4Muse Wonder159
5Gemini 3.8 Flash141
6GPT-5.6 Terra140
7Claude Sonnet 5.5139
8Muse Spark 1.3119
9Claude Opus 5.596
10Step 5 Preview87

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
4DeepSeek V4.1 Flash 1017$0.525
5MiMo-V2.6-Pro$0.544
6Step 5 Preview$1.43
7Gemini 3.8 Flash$1.50
8Muse Spark 1.3$2.00
9GLM-5.3$2.15
10Grok 4.7$3.00