MiMo-V2.6-Pro-RL review, pricing and limits

MiMo-V2.6-Pro-RL scores 46.32 on the Artificial Analysis Intelligence Index, the highest of any open-weight model at launch, ahead of Kimi K3 (44) and GLM-5.3 (45), though Xiaomi says leading closed-source models score around 53. On agentic evals Xiaomi reports, it scores 89.9 on Terminal-Bench 2.1 and 82.0 on OSWorld-Verified, close to Claude Opus 5 (89.1, 83.4) and GPT-5.6 Sol (88.8, 83.0). It trails those same models on cybersecurity exploit evals: 47.9 vs Claude Opus 5's 70.0 on ExploitBench.

The model ships a 1 million token context window and a 128,000 token max output, confirmed on Xiaomi's own API docs. A 5-layer speculative multi-token-prediction decoder predicts 7 tokens per forward pass to keep long sessions fast despite the trillion-parameter backbone. Artificial Analysis independently measured 48.6 output tokens per second and a 4.01-second time-to-first-answer-token, both toward the slow end of its open-weight peer group.

Input is natively omnimodal: text, image, video, and audio, via a 681M-parameter vision encoder and a two-part audio stack (a 308M AudioTokenizer plus a 127M audio patch encoder). Output is text only; there is no native audio or image generation. The model supports tool calling, streaming, structured output, web search, and context caching.

Xiaomi's API prices MiMo-V2.6-Pro-RL at $0.435 per 1M input tokens on a cache miss, $0.0036 per 1M on a cache hit (about a 99% discount), and $0.87 per 1M output tokens. Artificial Analysis computes a blended rate of $0.18 per 1M tokens, below the class median. A separate Token Plan subscription and a batch API for non-real-time workloads are also available.

Weights are published on Hugging Face and ModelScope under MIT, with vLLM and SGLang recipes; the full checkpoint needs 8 GPUs (vLLM) or 2 nodes of 16 GPUs (SGLang). It is not yet on Bedrock, Vertex, or Azure; access is via Xiaomi's own API, AI Studio, MiMo Desktop, and OpenRouter, with only 3 API providers live at listing time.

Xiaomi's Trust Center publishes training-data summaries for the wider MiMo-V2 family and MiMo-V2.5, but not MiMo-V2.6 specifically. Its technical report describes an "Aligned RL" stage where the model rewrites its own misaligned turns into grounded responses, plus adversarial screening and verifier cross-checks against reward hacking during RL.

MiMo-V2.6-Pro-RL suits teams wanting an open-weight model competitive with closed frontier models on agentic and coding benchmarks, needing the full 1M-token context, able to self-host on multi-GPU infrastructure or accept an early-stage API. It is weaker for exploit-development work, native audio/image output, low-latency small-prompt use, or enterprise compliance needs.

Training used one asynchronous GRPO reinforcement-learning run mixing coding, agent, visual, and cybersecurity tasks in the same batches, at 1,568 prompts times 16 rollouts per step. Xiaomi reports the Pro run completed 30 steps in under six days using about 750,000 trajectories at a reported cost of $2.62 million (a further $850,000 covered the smaller MiMo-V2.6-Flash-RL checkpoint trained alongside it).

MiMo-V2.6-Pro-RL beats its predecessor MiMo-V2.5-Pro by a wide margin on every benchmark reported side by side, for example 89.9 vs 65.2 on Terminal-Bench 2.1. Xiaomi has already previewed MiMo-V3, reported to adopt a new "HySparse2" architecture to cut long-context serving costs, as the next model in the line. A smaller research sibling, MiMo-V2.6-Distill-Qwen-9B, was released alongside it for self-hosting on far less hardware.