The SWE-rebench rankings show minimal movement at the top tier, with Claude Code holding 52.9% and the next three positions separated by less than 1.2 percentage points, suggesting performance has plateaued in a narrow band where incremental gains demand substantial effort. Below the top tier, volatility emerges: Claude Opus 4.5 dropped from 49.7% on Artificial Analysis to 43.8% on SWE-rebench (a 5.9-point gap that flags a possible methodology divergence between the two benchmarks), while Gemini 3 Pro Preview fell from 48.4% to 46.7%, and Kimi K2.5 contracted from 46.8% to 37.9%, suggesting these models may perform differently on SWE-rebench's specific test distribution or problem types. Conversely, Kimi K2 Thinking climbed 2.9 points from 40.9% to 43.8%, and GLM-4.6 gained 4.6 points from 32.5% to 37.1%, indicating selective improvements in certain architectures. The Artificial Analysis leaderboard itself remained largely stable in its top 20, with new entry mimo-v2-omni appearing at rank 22 and Nanbeige4.1-3B at rank 169, but these additions occupy middle and lower positions where churn is expected. The key signal is not absolute ranking changes but the widening discrepancy between the two benchmarks: models ranking identically on both (like the top five) inspire confidence, while models showing 5+ point spreads warrant scrutiny into whether SWE-rebench is measuring a materially different capability or whether evaluation methodology accounts for the delta.
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 | Claude Code | 52.9% |
| 2 | Junie | 52.1% |
| 3 | Claude Opus 4.6 | 51.7% |
| 4 | gpt-5.2-2025-12-11-xhigh | 51.7% |
| 5 | gpt-5.2-2025-12-11-medium | 51.0% |
| 6 | gpt-5.1-codex-max | 48.5% |
| 7 | Claude Sonnet 4.5 | 47.1% |
| 8 | Gemini 3 Pro Preview | 46.7% |
| 9 | Gemini 3 Flash Preview | 46.7% |
| 10 | gpt-5.2-codex | 45.0% |
Artificial Analysis composite index across coding, math, and reasoning benchmarks.
| # | Model | Score | tok/s | $/1M |
|---|---|---|---|---|
| 1 | GPT-5.4 | 57.2 | 86 | $5.63 |
| 2 | Gemini 3.1 Pro Preview | 57.2 | 117 | $4.50 |
| 3 | GPT-5.3 Codex | 54 | 74 | $4.81 |
| 4 | Claude Opus 4.6 | 53 | 54 | $10.00 |
| 5 | Claude Sonnet 4.6 | 51.7 | 70 | $6.00 |
| 6 | GPT-5.2 | 51.3 | 72 | $4.81 |
| 7 | GLM-5 | 49.8 | 83 | $1.55 |
| 8 | Claude Opus 4.5 | 49.7 | 59 | $10.00 |
| 9 | MiniMax-M2.7 | 49.6 | 43 | $0.525 |
| 10 | MiMo-V2-Pro | 49.2 | 0 | $0.00 |
Output tokens per second — higher is faster. Minimum intelligence score of 40.
| # | Model | tok/s |
|---|---|---|
| 1 | GPT-5.4 mini | 254 |
| 2 | GPT-5.4 nano | 213 |
| 3 | Gemini 3 Flash Preview | 199 |
| 4 | Grok 4.20 Beta 0309 | 192 |
| 5 | GPT-5 Codex | 184 |
| 6 | Qwen3.5 122B A10B | 153 |
| 7 | MiMo-V2-Flash | 137 |
| 8 | Gemini 3.1 Pro Preview | 117 |
| 9 | Gemini 3 Pro Preview | 115 |
| 10 | GPT-5.1 Codex | 102 |
Blended cost per 1M tokens (3:1 input/output) — lower is cheaper. Minimum intelligence score of 40.
| # | Model | $/1M |
|---|---|---|
| 1 | MiMo-V2-Flash | $0.15 |
| 2 | DeepSeek V3.2 | $0.315 |
| 3 | GPT-5.4 nano | $0.463 |
| 4 | MiniMax-M2.7 | $0.525 |
| 5 | MiniMax-M2.5 | $0.525 |
| 6 | GPT-5 mini | $0.688 |
| 7 | Qwen3.5 27B | $0.825 |
| 8 | GLM-4.7 | $1.00 |
| 9 | Kimi K2 Thinking | $1.07 |
| 10 | Qwen3.5 122B A10B | $1.10 |