Xiaomi's MiMo-V2.5-Pro open source model achieved a perfect score on a Peking University compiler project, completing it in 4.3 hours - a feat that typically takes students weeks. The 233/233 test pass rate on an unseen hidden test suite demonstrates its coding prowess. This isn't just about speed: the model also built a functional video editor in 11.5 hours and designed a complex analog circuit in under an hour, tasks requiring sustained multi-step reasoning.

The hybrid attention architecture enables MiMo-V2.5-Pro to handle 1 million token contexts without quality degradation, a major improvement over previous versions that failed at 128k tokens. By combining local sliding window attention with sparse global attention, it maintains coherent long-term reasoning while reducing KV cache storage by 7x. This allows practical use cases like processing entire codebases or research papers in a single session.

Compared to closed models, MiMo-V2.5-Pro scores 63.8 on Claw-Eval Pass@3 - higher than Claude Opus 4.6's reported performance. On coding-specific benchmarks like Terminal-Bench 2.0, it leads Claude Opus 4.6 and Gemini 3.1 Pro. While DeepSeek V4 Pro shows stronger general reasoning, MiMo excels in agentic coding workflows requiring thousands of tool calls, as demonstrated by its self-correcting behavior during the compiler project.

With 40-60% lower token costs than competitors at similar performance levels, MiMo-V2.5-Pro offers a compelling open source alternative for developers. Available on HuggingFace under an MIT license, it represents a significant leap in accessible, high-performance coding assistance. Developers seeking coding-first models with robust long-context capabilities should prioritize this release.