The Inference Report

July 16, 2026

The SWE-rebench rankings show no movement from the previous cycle: OpenAI's gpt-5.5-2026-04-23-xhighModel holds at 62.7 percent, followed by Junie at 61.6 percent and OpenAI's CodexAgent at 60.4 percent, with confidence intervals wide enough that none of these differences exceed statistical significance given the stated uncertainties. The methodology underlying SWE-rebench itself remains opaque in the provided data, limiting assessment of whether these scores reflect genuine capability differences or measurement noise, though the consistency across runs suggests at minimum that the evaluation framework is stable. On the Artificial Analysis leaderboard, movement is more pronounced: Inkling enters at position 29 with a score of 40.7, displacing the prior entry at that rank, while subsequent entries shift downward accordingly through position 407. The scale of this leaderboard, spanning 407 models with scores ranging from 59.9 down to 1.0, makes granular interpretation difficult without knowing the underlying methodology, task distribution, or weighting scheme. Neither benchmark set demonstrates the kind of step change that would indicate a methodological breakthrough in the evaluated systems, and the Artificial Analysis data in particular lacks the confidence intervals present in SWE-rebench, making it impossible to assess whether observed differences between adjacent entries reflect genuine performance gaps or ranking artifacts.

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
1OpenAIgpt-5.5-2026-04-23-xhighModel62.7%± 0.91%
2JunieJunieAgent61.6%± 0.64%
3OpenAICodexAgent60.4%± 1.37%
4AnthropicClaude CodeAgent59.6%± 1.98%
5OpenAIgpt-5.5-2026-04-23-mediumModel58.9%± 0.78%
6AnthropicClaude Opus 4.8-xhighModel56.5%± 1.20%
7OpenAIgpt-5.4-2026-03-05-mediumModel54.9%± 1.02%
8AnthropicClaude Opus 4.7-highModel53.1%± 1.45%
9CursorCursorAgent53.0%± 0.53%
10AnthropicClaude Sonnet 4.6Model51.3%± 0.55%

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

#ModelScoretok/s$/1M
1Claude Fable 559.969$20.00
2GPT-5.6 Sol58.967$11.25
3Claude Opus 4.855.754$10.00
4GPT-5.6 Terra55165$5.63
5GPT-5.554.874$11.25
6Grok 4.553.8123$3.00
7Claude Opus 4.753.550$10.00
8Claude Sonnet 553.486$4.00
9GPT-5.451.4152$5.63
10GPT-5.6 Luna51.2244$2.25

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

#Modeltok/s
1GPT-5.6 Luna244
2Gemini 3.5 Flash241
3Qwen3.7 Max199
4GPT-5.6 Terra165
5GPT-5.4 mini163
6GPT-5.4152
7GLM-5.2149
8Nex-N2-Pro137
9Muse Spark 1.1126
10GPT-5.2 Codex126

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

#Model$/1M
1DeepSeek V4 Flash$0.175
2MiniMax-M3$0.525
3DeepSeek V4 Pro$0.544
4MiMo-V2.5-Pro$0.544
5Nex-N2-Pro$1.00
6GPT-5.4 mini$1.69
7Kimi K2.6$1.71
8Kimi K2.7 Code$1.71
9Muse Spark 1.1$2.00
10GLM-5.2$2.15