Xiaomi's trillion-parameter open-weight flagship for long-horizon agents, coding, and omnimodal reasoning at a low cost tier.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Why MiMo-V2.6-Pro Leads Open-Weight Agentic Work
Benchmarks
MiMo-V2.6-Pro Benchmarks: Intelligence, Speed, and Tool Reliability
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Long-horizon coding agents
With a Xiaomi-reported 78.6 on SWE-Bench Verified in Thinking Mode and tool-calling accuracy reported at 97%, MiMo-V2.6-Pro is well-suited to autonomous software-engineering agents that resolve issues, refactor, and run multi-step tool sequences. Its 1M-token context accommodates large repositories and long tool traces in a single pass. Combined with its low cost tier, this makes sustained, high-volume coding-agent runs practical where proprietary alternatives are more expensive.
Large-context research and document analysis
The 1M-token context window lets the model ingest entire research corpora, literature sets, or multi-document knowledge bases without chunking heuristics. Xiaomi highlights research use such as reviewing literature, forming hypotheses, and shortlisting candidates. For RAG-adjacent and long-document workflows, the large window plus strong reasoning reduces context-management complexity, though verbose Deep Thinking output should be budgeted for on very long tasks.
Omnimodal understanding pipelines
Native text, image, audio, and video input in a single model supports workflows that reason across mixed media — analyzing screenshots alongside logs, or video and audio alongside text instructions. Because output is text only, it fits classification, extraction, description, and reasoning tasks rather than media generation. This suits agents that must perceive multimodal context and act via tools.
Self-hosted, cost-controlled deployment
Because MiMo-V2.6-Pro ships under an MIT license with open weights on Hugging Face, teams can fine-tune and self-host without per-token API fees or dependence on China-based endpoints. This suits organizations with data-residency requirements or very high volumes where owned infrastructure amortizes GPU cost. The trade-off is the substantial hardware needed to serve a trillion-parameter MoE model efficiently.