MiMo-V2.6-Pro-RL

MiMo-V2.6-Pro-RL is the flagship open-weights checkpoint from Xiaomi MiMo, released under the MIT license. It is a sparse mixture-of-experts model with 1.02T total parameters and 42B activated parameters, a 1M token context window, and native support for text, image, video, and audio input. Xiaomi trained it with a single mixed reinforcement learning run spanning coding, general agents, visual, and cybersecurity tasks, followed by multi-prefix multi-teacher on-policy distillation (MOPD2). The result is a model built for long-horizon agentic workloads, complex reasoning, and professional workflows where context length and multimodal understanding matter.

This model sits at the top end of the open-weights landscape. It competes directly with other trillion-scale sparse MoE models like DeepSeek-V3 and dense giants like Llama 3.1 405B. Where MiMo-V2.6-Pro-RL differentiates itself is the combination of a 1M token context, full modality support, and an MIT license that permits commercial use without restrictions. Xiaomi reports that the Pro variant scores 46 on the Artificial Analysis Intelligence Index, placing it among the strongest open-source models available. The RL checkpoint specifically targets self-improvement: the training pipeline uses groupwise agentic grading to rank passing solutions, not just binary pass/fail, which steers the model toward shorter paths and fewer tokens per task.

For practitioners, the key question is whether this model is practical to run locally. The short answer: it is not a consumer-hardware model. The 1.02T total parameter count means the full weights must reside in memory, even though only 42B parameters are active per token. That distinction affects inference speed, not memory capacity. If you have access to a multi-GPU server or a high-memory workstation, MiMo-V2.6-Pro-RL can be served with SGLang or vLLM. For everyone else, the Xiaomi MiMo API platform and OpenRouter offer the realistic path to using it.

MiMo-V2.6-Pro-RL uses a sparse MoE architecture: 1.02T total parameters, 42B activated per token. In practical terms, this means the compute cost per token is similar to a dense 42B model, but the memory footprint is that of a 1T model. You need enough VRAM to hold all experts, even though only a fraction are used for any given token. This is the central tradeoff of large MoE models: fast inference relative to parameter count, but high memory requirements.

The 1M token context window is the headline feature. It enables entire repositories, long tool traces, multi-session agent runs, and hours of video or audio to be processed in a single pass. The model uses a 681M-parameter MiMo ViT vision encoder (28 layers: 24 sliding-window attention + 4 full attention) and a 308M AudioTokenizer plus 127M audio patch encoder for audio input. A 5-layer multi-token prediction (MTP) speculative decoder is included to accelerate generation.

Training involved a fully asynchronous GRPO run on very large batches, 1,568 prompts × 16 rollouts per step, billions of tokens per update. The reward signal was scaled beyond binary pass/fail using Groupwise Reward Synthesis (GRS) and Groupwise Advantage Redistribution (GAR), which compare rollouts within each group and move advantage toward higher-quality solutions. After mixed RL, MOPD2 combined autonomous student rollouts with prefix-conditioned single-turn rollouts to extend capabilities to hard-to-verify tasks. The model is served with SGLang or vLLM, both of which support tensor parallelism for multi-GPU deployment.

MiMo-V2.6-Pro-RL is multimodal and multilingual, with primary support for English and Chinese. Its capabilities span chat, code, vision, reasoning, function-calling, and agentic tool use. This is not a general-purpose chatbot, it is built for demanding, long-horizon tasks.

• Coding: The 1M context window allows the model to ingest entire codebases, track dependencies across files, and reason about large refactors. It is suitable for repository-level code generation, debugging, and code review.
• Agentic workflows: Function-calling and tool use are native. The model can browse, comprehend, reason, and act across multiple sessions, making it a candidate for autonomous agents that need to maintain state over long periods.
• Vision and video: Image and video understanding are supported out of the box. Use cases include visual question answering, video summarization, and multimodal document analysis.
• Cybersecurity: Xiaomi explicitly trained the model on cybersecurity tasks. It can assist with threat analysis, log inspection, and vulnerability assessment, though human oversight remains essential.
• Research and long-document reasoning: The 1M context is useful for processing legal contracts, scientific papers, and technical manuals without chunking.

For coding specifically, MiMo-V2.6-Pro-RL competes with models like DeepSeek-V3 and Qwen2.5-Coder. Its advantage is the combination of long context and multimodal input, you can feed it screenshots, diagrams, and code in the same prompt.

This is not a model you run on a single RTX 4090 or an M4 Max. The VRAM requirements are extreme because all 1.02T parameters must be loaded, regardless of how many are active per token.

VRAM requirements by quantization:

• FP16: ~2 TB
• 8-bit: ~1 TB
• 4-bit (Q4_K_M): ~512 GB
• 2-bit (Q2_K): ~256 GB

Minimum hardware: At least 256 GB of VRAM for 2-bit quantization. That means 4× A100 80GB or 4× H100 80GB. For 4-bit, you need 8× 80GB GPUs. Consumer GPUs like the RTX 4090 (24GB) and Apple M4 Max (128GB unified memory) cannot run this model, even with CPU offloading. If you attempt CPU offloading with 256GB+ system RAM, expect tokens per second in the low single digits at best.

Recommended quantization: Q4_K_M offers the best balance of quality and memory, but requires 512 GB of VRAM. If you have exactly 256 GB, Q2_K is the only option, and you should expect noticeable degradation in reasoning and code quality. For production use, 8-bit or FP16 is preferable if you have the hardware.

Expected performance: On an 8× H100 setup with 4-bit quantization, expect roughly 20–40 tokens per second for single-stream generation, depending on batch size and sequence length. The 42B active parameters keep compute manageable, but memory bandwidth is the bottleneck. Multi-GPU tensor parallelism via vLLM or SGLang is required.

Ollama: Ollama does not currently support models of this scale. The quickest way to get started locally is to use vLLM or SGLang with a multi-GPU server. If you do not have that hardware, the Xiaomi MiMo API platform and OpenRouter provide hosted access. Renting a multi-GPU cloud instance is another option for evaluation.

MiMo-V2.6-Pro-RL vs DeepSeek-V3: DeepSeek-V3 is a 671B total / 37B active MoE with a 128K context window and an MIT license. MiMo-V2.6-Pro-RL has a larger total parameter count (1.02T vs 671B), a much larger context window (1M vs 128K), and native multimodal input. DeepSeek-V3 is easier to run locally, at 4-bit it needs roughly 400 GB of VRAM, still beyond consumer hardware but more accessible than MiMo's 512 GB. Choose MiMo when you need the 1M context, multimodal capabilities, or the specific RL-trained agentic behavior. Choose DeepSeek-V3 when you want a more established MoE with a smaller memory footprint and a wider ecosystem of fine-tunes.

MiMo-V2.6-Pro-RL vs Llama 3.1 405B: Llama 3.1 405B is a dense model, meaning all 405B parameters are active per token. That makes inference slower and more compute-intensive than MiMo's 42B active parameters. Llama 3.1 has a 128K context window, no native multimodal support, and a large community ecosystem. MiMo-V2.6-Pro-RL is more efficient per token, supports 1M context and multimodal input, but requires more VRAM to store the full weights. Choose Llama 3.1 if you prefer a dense architecture with broad tooling support. Choose MiMo if you need long-context multimodal reasoning and can afford the memory.

Both alternatives are more mature in terms of third-party tooling and deployment guides. MiMo-V2.6-Pro-RL is newer, but its MIT license and strong benchmark results make it a serious contender for teams with the hardware to run it.