Open-weight omnimodal MoE model tuned for agentic coding and long-horizon tool use at low token cost.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Agentic Coding and Tool Use Are Where Flash Earns Its Place
Benchmarks
How MiMo-V2.6-Flash Performs on Independent and Provider Benchmarks
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
Autonomous coding agents
MiMo-V2.6-Flash is tuned specifically for agentic coding: Xiaomi reports 78.6 on SWE-Bench Verified in Thinking Mode and tool-calling accuracy of 97.0%, with the lineage leading open-source SWE-Bench results. Combined with the 1M-token window for whole-repository context and reliable multi-turn tool calls, it fits autonomous coding agents, PR-fixing bots and code-scaffold integrations (Cline, Roo, Kilo). Validate on your own repos, and note that Thinking Mode adds latency.
Long-horizon tool-use automation
The model was trained through large-scale agentic RL for complex, long-horizon tasks with robust generalisation across agent harnesses, and its DeepSWE v1.1 score improved to 65.68 during training. Reliable tool calling plus the large context make it suitable for multi-step automation that plans, calls external tools and maintains state across many turns. Its lower active-parameter cost keeps such long, tool-heavy runs economical relative to flagship tiers.
Long-context repository and document analysis
With a ~1M-token context window and hybrid SWA/global attention that cuts KV-cache cost at long context, MiMo-V2.6-Flash can ingest entire codebases, long tool traces or large document sets in a single pass. Its predecessor's long-context evaluations reportedly surpassed a much larger full-global-attention model. This suits repository-level reasoning, large-scale log analysis and multi-document synthesis without aggressive chunking.
Multimodal understanding pipelines
Because Flash natively accepts text, image, video and audio and returns text, it fits pipelines that reason over mixed media — extracting structure from screenshots, describing video frames, or transcribing intent from audio into actionable text and tool calls. It is a perception-and-reasoning component, not a media generator, so pair it with generation models where synthesized images, audio or video are required.