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.
| # | Model | Score |
|---|---|---|
| 1 | OpenAIgpt-5.5-2026-04-23-xhighModel | 62.7%± 0.91% |
| 2 | JunieJunieAgent | 61.6%± 0.64% |
| 3 | OpenAICodexAgent | 60.4%± 1.37% |
| 4 | AnthropicClaude CodeAgent | 59.6%± 1.98% |
| 5 | OpenAIgpt-5.5-2026-04-23-mediumModel | 58.9%± 0.78% |
| 6 | AnthropicClaude Opus 4.8-xhighModel | 56.5%± 1.20% |
| 7 | OpenAIgpt-5.4-2026-03-05-mediumModel | 54.9%± 1.02% |
| 8 | AnthropicClaude Opus 4.7-highModel | 53.1%± 1.45% |
| 9 | CursorCursorAgent | 53.0%± 0.53% |
| 10 | AnthropicClaude Sonnet 4.6Model | 51.3%± 0.55% |
Artificial Analysis composite index across coding, math, and reasoning benchmarks.
| # | Model | Score | tok/s | $/1M |
|---|---|---|---|---|
| 1 | Claude Fable 5 | 59.9 | 69 | $20.00 |
| 2 | GPT-5.6 Sol | 58.9 | 67 | $11.25 |
| 3 | Claude Opus 4.8 | 55.7 | 54 | $10.00 |
| 4 | GPT-5.6 Terra | 55 | 165 | $5.63 |
| 5 | GPT-5.5 | 54.8 | 74 | $11.25 |
| 6 | Grok 4.5 | 53.8 | 123 | $3.00 |
| 7 | Claude Opus 4.7 | 53.5 | 50 | $10.00 |
| 8 | Claude Sonnet 5 | 53.4 | 86 | $4.00 |
| 9 | GPT-5.4 | 51.4 | 152 | $5.63 |
| 10 | GPT-5.6 Luna | 51.2 | 244 | $2.25 |
Output tokens per second — higher is faster. Minimum intelligence score of 40.
| # | Model | tok/s |
|---|---|---|
| 1 | GPT-5.6 Luna | 244 |
| 2 | Gemini 3.5 Flash | 241 |
| 3 | Qwen3.7 Max | 199 |
| 4 | GPT-5.6 Terra | 165 |
| 5 | GPT-5.4 mini | 163 |
| 6 | GPT-5.4 | 152 |
| 7 | GLM-5.2 | 149 |
| 8 | Nex-N2-Pro | 137 |
| 9 | Muse Spark 1.1 | 126 |
| 10 | GPT-5.2 Codex | 126 |
Blended cost per 1M tokens (3:1 input/output) — lower is cheaper. Minimum intelligence score of 40.
| # | Model | $/1M |
|---|---|---|
| 1 | DeepSeek V4 Flash | $0.175 |
| 2 | MiniMax-M3 | $0.525 |
| 3 | DeepSeek V4 Pro | $0.544 |
| 4 | MiMo-V2.5-Pro | $0.544 |
| 5 | Nex-N2-Pro | $1.00 |
| 6 | GPT-5.4 mini | $1.69 |
| 7 | Kimi K2.6 | $1.71 |
| 8 | Kimi K2.7 Code | $1.71 |
| 9 | Muse Spark 1.1 | $2.00 |
| 10 | GLM-5.2 | $2.15 |