The SWE-rebench leaderboard shows no movement since the previous update, the top 24 entries remain identical in rank and score, with OpenAI's gpt-5.5-2026-04-23-xhigh still at 62.7% (±0.91%), Junie's agent at 61.6% (±0.64%), and the rest holding steady through the Claude Sonnet 4.6 at 51.3% (±0.55%) and down to Gemma 4 31B at 16.5% (±1.13%). The confidence intervals are tight enough that no model has shifted position within measurement error. By contrast, the Artificial Analysis benchmark shows substantial reshuffling in the lower ranks, models from position 304 onward have reordered themselves, with Gemma 3 12B Instruct climbing from #345 to #304 (3.4 to 5.5), and LFM2.5-1.2B-Thinking and LFM2.5-1.2B-Instruct swapping positions 364 and 366, but the top 50 models remain in nearly identical order, suggesting the two benchmarks measure overlapping but not identical capabilities. The SWE-rebench methodology, which evaluates agent-based code completion on real repository issues, appears more stable than Artificial Analysis across time, though without historical context it is unclear whether this stability reflects genuine plateau or simply infrequent test runs. The lack of motion in the coding benchmark despite ongoing model releases is noteworthy: neither the latest Claude variants nor new reasoning models have displaced the April OpenAI and Junie entries that occupy the summit.
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 | 61 | $20.00 |
| 2 | Claude Opus 4.8 | 55.7 | 51 | $10.00 |
| 3 | GPT-5.5 | 54.8 | 80 | $11.25 |
| 4 | Claude Opus 4.7 | 53.5 | 47 | $10.00 |
| 5 | Claude Sonnet 5 | 53.4 | 78 | $6.00 |
| 6 | GPT-5.4 | 51.4 | 153 | $5.63 |
| 7 | GLM-5.2 | 51.1 | 174 | $2.15 |
| 8 | Gemini 3.5 Flash | 50.2 | 199 | $3.38 |
| 9 | Claude Sonnet 4.6 | 47.2 | 49 | $6.00 |
| 10 | Gemini 3.1 Pro Preview | 46.5 | 134 | $4.50 |
Output tokens per second — higher is faster. Minimum intelligence score of 40.
| # | Model | tok/s |
|---|---|---|
| 1 | Gemini 3.5 Flash | 199 |
| 2 | Qwen3.7 Max | 196 |
| 3 | GPT-5.4 mini | 176 |
| 4 | GLM-5.2 | 174 |
| 5 | GPT-5.4 | 153 |
| 6 | GPT-5.2 Codex | 138 |
| 7 | Gemini 3.1 Pro Preview | 134 |
| 8 | MiniMax-M3 | 106 |
| 9 | Nex-N2-Pro | 104 |
| 10 | DeepSeek V4 Flash | 103 |
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 | MiMo-V2.5 | $0.175 |
| 3 | MiniMax-M3 | $0.525 |
| 4 | DeepSeek V4 Pro | $0.544 |
| 5 | MiMo-V2.5-Pro | $0.544 |
| 6 | Nex-N2-Pro | $1.00 |
| 7 | MiMo-V2-Pro | $1.50 |
| 8 | GPT-5.4 mini | $1.69 |
| 9 | Kimi K2.6 | $1.71 |
| 10 | Kimi K2.7 Code | $1.71 |