0T params
Total model parameters
0B active
Active per token (MoE)
0K tokens
Context window
0
Expert modules
| K2.5 Score | Context | |
|---|---|---|
| SWE-Bench Verified | 76.8 | Outperforms Gemini Pro 3.1 |
| SWE-Bench Multilingual | 73.0 | Outperforms GPT-5.2 and Gemini Pro 3.1 |
| AIME 2025 | 96.1 | Advanced math competition problems |
| GPQA Diamond | 87.6 | Graduate level science questions |
| HLE (with tools) | 50.2 | At 76% lower cost than Claude Opus 4.6 |
| LiveCodeBench | 85.0 | Real time coding evaluation |
| MathVision | 84.2 | Mathematical visual reasoning |
| OCRBench | 92.3 | Optical character recognition accuracy |
| VideoMMMU | 86.6 | Video understanding and reasoning |
| WeirdML | 46% | Unusual/edge case reasoning (lower than competitors) |
| Speed | Best For | |
|---|---|---|
| Instant | Fastest | Quick questions, simple tasks, casual conversation |
| Thinking | Medium | Math, logic, complex analysis, coding problems |
| Agent | Slower | Research, multi-step tasks, file processing |
| Agent Swarm (beta) | Variable | Deep research, large codebase analysis, comprehensive investigations |
Why MoE matters for cost
With only 32 billion parameters active per token out of 1 trillion total, K2.5 achieves frontier performance while using a fraction of the compute per inference. This is why API pricing can be $0.60/$3.00 per million tokens while matching models that cost 5x to 25x more.