MiMo V2.6 Pro RL is Xiaomi’s MIT-licensed multimodal checkpoint for long-context coding and agent work.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Pricing conditions: Overseas API rates in USD per million tokens: $0.435 uncached input, $0.0036 cached input and $0.87 output. Domestic CNY prices and Token Plan quotas are separate. Web search is billed separately. Batch token rates are half the corresponding realtime rates. The optional mimo-v2.6-pro-ultraspeed route instead costs $4.35/M uncached input, $0.036/M cached input and $8.70/M output, and has no batch API.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
The current lowercase hosted model ID is mimo-v2.6-pro. The V2.6 series was released on 22 September 2026. The public API and downloadable RL checkpoint are deployment options for this model; keep the selected route and runtime with each result. Coding, tool use and long-running agent tasks need a checkable final deliverable, rather than a plausible first answer.
The overseas API lists $0.435 per million uncached input tokens, $0.0036 for cached input and $0.87 for output. Domestic CNY prices and subscription allowances are separate arrangements. Batch token rates are half the corresponding realtime rates. Web search is billed separately. Record the account region, mode and cache usage when comparing a complete task’s cost.
For a hosted integration, pin the API model name and replay the tool schema and response handling before migration. For a local experiment, keep the exact RL checkpoint revision, weight format, chat template and runtime with the results. Local hardware and hosted quotas are different constraints. A processing tier’s speed claim does not establish the same throughput on another serving configuration.
Build a small evaluation set from the work your application actually handles. Include incomplete information, contradictory inputs and a case that should stop for clarification. Define an acceptable deliverable before running the model, then inspect failures as carefully as successful examples. Keep input material, tools and effort settings consistent when comparing candidates.
Record the exact model identifier, deployment route and request settings with each result. Count complete runs, including rejected outputs, retries and tool calls. When the workflow creates files or code, open the files or execute the existing checks before accepting the result. A plausible first response can hide an incomplete final deliverable.
The specifications describe supported interfaces and request capacity. They do not establish a measured quality ranking or a latency guarantee for your account. SyncDev has not independently benchmarked this model. Choose a deployment after testing the languages, input formats and operating conditions that matter to your users.
Explore xiaomi models, browse model tasks, or compare two models with the same acceptance criteria.
Compare the model on the work you need to complete.
MiMo V2.6 Pro RL is listed for Audio-text-to-text, Image-text-to-text, Text generation, Video-text-to-text. It accepts text, image, audio, video and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
Documented limits: 1.05M context. The listed route has starting rates of $0.435 input and $0.87 output per million tokens. Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
The weights are downloadable under MIT. Check runtime support and size the complete model and working context for your expected concurrency.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
Anthropic
Claude’s small model for high-volume extraction, classification and routing, with adaptive thinking and a 1M-token context window.
Mistral AI
Mistral Large 4 is an open-weight multimodal model released in public preview on the publisher’s announced release, for general reasoning, coding and agent applications.
OpenAI
GPT-6.1 Sol is OpenAI's updated Sol model for coding and professional tasks. It accepts text and images and returns text.
inclusionAI
Ling 3.1 Flash is inclusionAI’s hybrid reasoning model for coding and tool agents, available through a hosted AI Gateway route.
Anthropic
Claude Sonnet 5.5 is an Anthropic language model that accepts text and images and returns text.
MiniMax
MiniMax M3.1 Flash Preview is a multimodal reasoning model for code and agent workflows, accessed through eligible MiniMax plans and tools.