The SWE-rebench leaderboard shows no movement at the top tier, with AnthropicFable 5, GrokGrok 4.5, and AnthropicOpus 5 holding positions 1 through 3 at 64.5%, 63.8%, and 63.4% respectively, all within their reported confidence intervals. The Artificial Analysis benchmark tells a different story: Gemini 4 Carbon gained 0.6 points to reach 60.0, Claude Opus 5.5 improved 0.7 points to 59.1, and two new entries appeared (GPT-6 Sol at position 11 with 44.2 and GPT-5.6 Terra at position 18 with 32.3), suggesting either model releases or evaluation methodology adjustments not reflected in the SWE-rebench data. The gap between the two benchmarks is instructive: SWE-rebench clusters the top five models within 2.2 percentage points, a tight range that reflects the difficulty of the task and the convergence of high-capacity models on this specific evaluation, while Artificial Analysis spreads the same performers across a 3.1 point range, indicating different test construction or scoring criteria. Within SWE-rebench, the confidence intervals are narrow enough that single-point movements would be meaningful, yet none occurred, suggesting either genuine stability in model performance or evaluation variance that masks real differences. The lower-ranked models show marginal gains across Artificial Analysis (most under 0.3 points), consistent with noise rather than systematic improvement. Neither benchmark provides evidence of a qualitative shift in coding capability 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.
| # | Model | Score |
|---|---|---|
| 1 | AnthropicFable 5 [high]Model | 64.5%± 1.41% |
| 2 | GrokGrok 4.5 [high]Model | 63.8%± 0.60% |
| 3 | AnthropicOpus 5 [high]Model | 63.4%± 1.35% |
| 4 | Z.aiGLM-5.2 [high]Model | 62.9%± 1.19% |
| 5 | OpenAIGPT-5.6 Sol [medium]Model | 62.3%± 1.83% |
| 6 | JunieJunieAgent | 61.8%± 0.54% |
| 7 | AnthropicClaude CodeAgent | 60.4%± 1.03% |
| 8 | OpenAICodexAgent | 58.0%± 1.29% |
| 9 | AnthropicSonnet 5 [high]Model | 56.8%± 0.94% |
| 10 | CursorCursorAgent | 51.7%± 0.84% |
Artificial Analysis composite index across coding, math, and reasoning benchmarks.
| # | Model | Score | tok/s | $/1M |
|---|---|---|---|---|
| 1 | Gemini 4 Carbon | 60 | 89 | $8.00 |
| 2 | Claude Opus 5.5 | 59.1 | 96 | $8.00 |
| 3 | GPT-6 Astra | 56.9 | 51 | $20.00 |
| 4 | GPT-6.1 Sol | 54.6 | 61 | $4.00 |
| 5 | Claude Sonnet 5.5 | 54.5 | 133 | $4.00 |
| 6 | GPT-6.1 Terra | 51.3 | 108 | $2.00 |
| 7 | Gemini 4 Argon | 50.4 | 82 | $8.00 |
| 8 | Muse Wonder Full | 50.1 | 0 | $0.00 |
| 9 | Claude Fable 5.1 | 50 | 50 | $20.00 |
| 10 | Kimi K3.1 Pro | 46.5 | 28 | $4.00 |
Output tokens per second — higher is faster. Minimum intelligence score of 40.
| # | Model | tok/s |
|---|---|---|
| 1 | Claude Sonnet 5.5 | 133 |
| 2 | Muse Wonder | 123 |
| 3 | GPT-6.1 Terra | 108 |
| 4 | Claude Opus 5.5 | 96 |
| 5 | Gemini 4 Carbon | 89 |
| 6 | Gemini 4 Argon | 82 |
| 7 | GPT-6.1 Sol | 61 |
| 8 | GPT-6 Astra | 51 |
| 9 | Claude Fable 5.1 | 50 |
| 10 | Kimi K3.1 Pro | 28 |
Blended cost per 1M tokens (3:1 input/output) — lower is cheaper. Minimum intelligence score of 40.
| # | Model | $/1M |
|---|---|---|
| 1 | GPT-6.1 Terra | $2.00 |
| 2 | GPT-6.1 Sol | $4.00 |
| 3 | Claude Sonnet 5.5 | $4.00 |
| 4 | Kimi K3.1 Pro | $4.00 |
| 5 | GPT-6 Sol | $4.00 |
| 6 | Gemini 4 Carbon | $8.00 |
| 7 | Claude Opus 5.5 | $8.00 |
| 8 | Gemini 4 Argon | $8.00 |
| 9 | Muse Wonder | $8.00 |
| 10 | GPT-6 Astra | $20.00 |