Xiaomi launched MiMo-V2.5-Pro on April 22, 2026, officially describing it as "a leap in agentic and long horizon coherence." Rather than chasing leaderboard scores alone, the model targets real-world tasks requiring hundreds or thousands of tool calls over hours or days.
Long-Horizon Coherence
While traditional large models tend to lose coherence as context grows, MiMo-V2.5-Pro sustains task chains of over 1,000 tool calls while maintaining logical rigor and instruction fidelity. It is particularly adept at following subtle contextual instructions hidden within agentic scenarios.
Official Demos
SysY Compiler in Rust — 4.3 hours
Based on a Peking University compiler course project, the model independently completed lexical analysis, parsing, AST construction, Koopa IR generation, a RISC-V assembly backend, and optimization:
- 4.3 hours, 672 tool calls
- First full compile passed 137/233 hidden tests (59% cold-start pass rate)
- Final result: 233/233
- Koopa IR: 110/110; RISC-V backend: 103/103; optimization: 20/20
- 11.5 hours, 8,192 lines of code, 1,868 tool calls
- Integrated AI voice narration powered by MiMo-V2-TTS
- Showed strong "harness awareness," collaborating seamlessly with external frameworks
- Closed-loop iteration with ngspice simulation
- All six specs met in about 1 hour
- Four specs improved by an order of magnitude over the initial design
- Coding agent / SWE-Bench related: 57.2 (near Claude Opus 4.6's 57.3 and GPT-5.4's 57.7); up to 73.7 on a SWE-Bench Pro variant
- MiMo Coding Bench: 68.4 (vs. Claude Opus 4.6's 65.4)
- GDPVal-AA: 72.9 (Elo 1581)
- τ3-bench: 63.8
- Claw-Eval (Pass@3): 34.0 with tools / 48.0 without; ~64% pass rate at roughly 70K tokens per trajectory
- Hybrid attention mechanism with very long context (up to 1M tokens in the series)
- Speed of roughly 60-80 tokens/s
- API pricing: $1/M input tokens, $3/M output tokens
- Public beta available; multimodal capabilities (image, audio, video) in the MiMo-V2.5 series
- Open-sourced weights planned at official release
The model demonstrated structured self-correction discipline: it planned milestones and validated intermediate results instead of blind trial-and-error.
Desktop Video Editor — 11.5 hours
Given instructions to implement a multi-track timeline, clip cutting, cross-fades, audio mixing, and an export pipeline:
Analog Circuit Optimization — ~1 hour
Designing and optimizing an FVF-LDO (flipped voltage follower low-dropout regulator) under TSMC 180nm CMOS, meeting six specifications (phase margin, line/load regulation, quiescent current, PSRR, transient response):
Benchmarks and Efficiency
Key reported scores include:
The model uses 40-60% fewer tokens to achieve comparable results, positioning it in the ideal high-score/low-cost region and making it attractive for integration into agent frameworks like Claude Code, OpenCode, and Kilo.
Architecture and Access
Conclusion
MiMo-V2.5-Pro demonstrates that higher intelligence means completing harder tasks with fewer resources—moving AI from a chat tool toward a genuine collaborator capable of building software, media tools, and hardware designs end-to-end.