The Inference Report

May 29, 2026

The SWE-rebench rankings show modest churn at the top tier but reveal significant instability in the middle and lower ranks on Artificial Analysis. On SWE-rebench, the top five remain locked: gpt-5.5-2026-04-23-xhigh holds 62.7%, followed by Codex (60.4%), Claude Code (59.6%), gpt-5.5-2026-04-23-medium (58.9%), and gpt-5.4-2026-03-05-medium (54.9%), with no score changes from prior results. Below that tier, Claude Opus 4.7 and Gemini 3.1 Pro Preview each dropped roughly 4 points (to 53.1% and 51.1% respectively), while Kimi K2.6 fell from 53.9% to 46.5%, a 7.4-point decline that suggests either methodology drift or model degradation rather than genuine progress. Across the Artificial Analysis leaderboard, the data is identical to the prior snapshot, indicating no new evaluations or score recalculations occurred. The absence of movement in a 383-model ranked list, combined with the stability of the top SWE-rebench performers, suggests these benchmarks may be operating on different evaluation cadences or that the SWE-rebench methodology itself is in flux. The meaningful signal here is negative: large score drops like Kimi K2.6's warrant investigation into whether the benchmark conditions changed, whether the model was updated, or whether previous scores were inflated. Without evidence of fresh evaluation runs, neither leaderboard documents actual progress 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
1gpt-5.5-2026-04-23-xhigh62.7%
2Codex60.4%
3Claude Code59.6%
4gpt-5.5-2026-04-23-medium58.9%
5gpt-5.4-2026-03-05-medium54.9%
6Claude Opus 4.753.1%
7Cursor53.0%
8Gemini 3.1 Pro Preview51.1%
9Claude Sonnet 4.651.1%
10GLM-5.150.7%

Artificial Analysis composite index across coding, math, and reasoning benchmarks.

#ModelScoretok/s$/1M
1Claude Opus 4.861.465$10.94
2GPT-5.560.278$11.25
3Claude Opus 4.757.353$10.94
4Gemini 3.1 Pro Preview57.2124$4.50
5GPT-5.456.889$5.63
6Qwen3.7 Max56.6199$3.75
7Gemini 3.5 Flash55.3222$3.38
8Kimi K2.653.932$1.71
9MiMo-V2.5-Pro53.851$0.544
10GPT-5.3 Codex53.672$4.81

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

#Modeltok/s
1Gemini 3.5 Flash222
2Grok 4.3213
3Grok 4.20 0309 v2200
4Qwen3.7 Max199
5Gemini 3 Flash Preview196
6Grok 4.20 0309182
7GPT-5.1 Codex180
8MiniMax-M2.5176
9GPT-5.4 mini167
10Qwen3.6 35B A3B167

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

#Model$/1M
1MiMo-V2-Flash$0.15
2MiMo-V2.5$0.175
3DeepSeek V4 Flash$0.175
4Hy3-preview$0.20
5DeepSeek V3.2$0.337
6GPT-5.4 nano$0.463
7MiniMax-M2.7$0.525
8KAT Coder Pro V2$0.525
9MiniMax-M2.5$0.525
10MiMo-V2.5-Pro$0.544