OpenRouter MiMo V2.5 Official Provider API: Non-Quantized Coding Agent, Cursor, Cline

For <strong>OpenRouter MiMo-v2.5 non quantized official provider API quantized coding agent Cursor Cline</strong>, the short answer is: MiMo-V2.5 has official Xiaomi-released weights that include BF16 artifacts, plus Xiaomi’s own OpenAI- and Anthropic-compatible API; but OpenRouter is a separate routing layer, and an API endpoint does not by itself prove that requests run on non-quantized weights. If non-quantized serving is your requirement, use the official weights yourself or obtain an explicit precision statement from the exact inference provider—not a model name, benchmark card, or routing catalog. [1][2][3]

That distinction matters more for coding agents than ordinary chat. An agent makes repeated tool calls, carries long histories, parses structured outputs, and depends on a model staying coherent after many turns. A precision change can be reasonable engineering, but you should treat it as a deployment property to verify rather than assuming every endpoint labelled mimo-v2.5 behaves identically.

Is MiMo-V2.5 officially available in a non-quantized format?

Yes—if “available” means downloadable official model artifacts. Xiaomi’s official Hugging Face repository for MiMo-V2.5 lists F32, BF16, and F8_E4M3 tensor types, and the model card provides deployment paths for Transformers, SGLang, and vLLM. The main MiMo-V2.5 model is a sparse mixture-of-experts model with 310B total parameters and 15B activated parameters, a stated 1M-token context window, and text, image, video, and audio inputs. [1]

But “non-quantized” needs a precise definition. BF16 is normally what engineers mean when they ask for an unquantized or full-precision practical checkpoint, even though it is lower precision than FP32. F32 may appear in auxiliary tensors or model files, while FP8 is explicitly a lower-precision representation. The official repository’s mix of tensor dtypes is evidence that Xiaomi publishes original-weight artifacts; it is not evidence that every hosted service loads BF16 weights end to end. [1]

For a reproducible non-quantized deployment, record the exact Hugging Face revision, model file names and dtypes, inference runtime version, GPU type, and runtime flags. Xiaomi has already issued a config and tokenizer-config update warning for early downloaders because outdated files could degrade behavior. “Official weights” alone is therefore not a complete reproducibility claim. [1]

What is the official MiMo-V2.5 API provider?

The official hosted provider is Xiaomi MiMo. Its documentation exposes https://api.xiaomimimo.com/v1/chat/completions, lists mimo-v2.5 among Xiaomi-owned model IDs, and describes its developer API as compatible with OpenAI and Anthropic request formats. Xiaomi documents support for function tools and notes an agent-specific requirement: when thinking mode produces tool calls, preserve prior reasoning_content in subsequent turns for the best multi-turn behavior. [2][4]

Xiaomi publishes standard MiMo-V2.5 API pricing separately from its UltraSpeed variant. The public product page lists standard MiMo-V2.5 at $0.14 per million cache-miss input tokens, $0.0028 per million cache-hit input tokens, and $0.28 per million output tokens. The same page specifically describes MiMo-V2.5-Pro-UltraSpeed as using FP4 “lossless quantization.” That wording is useful because it shows Xiaomi will identify a serving optimization when it chooses to—but it does not establish that standard MiMo-V2.5 API traffic is BF16, FP16, FP8, or another internal precision. [2]

So the technically honest conclusion is: Xiaomi is the official API provider, but Xiaomi’s public API documentation does not give you a general guarantee that the standard endpoint is non-quantized. Ask Xiaomi support for an explicit answer if serving precision is a procurement, evaluation, or debugging requirement.

Does OpenRouter provide non-quantized MiMo-V2.5 inference?

Do not assume so. OpenRouter offers the model identifier xiaomi/mimo-v2.5 through its unified API and documents an OpenAI-compatible chat-completions endpoint. Its model page advertises a 1M context window and currently lists pricing, but that page describes access to the model—not the exact checkpoint dtype, quantization method, kernel stack, or infrastructure configuration of the provider selected for a request. [3]

That is not a criticism of routing. It is a normal abstraction boundary. A router can make it easier to switch providers, centralize billing, and use one API shape. It cannot turn an opaque deployment into a verifiable BF16 deployment. The words “official model weights” and “official provider” answer different questions: one identifies who released the checkpoint; the other identifies who is serving your request.

1. Use xiaomi/mimo-v2.5 when you want the OpenRouter model route, but inspect the active provider and routing controls before benchmarking.
2. Ask the provider for the deployed weight format, whether quantization is weight-only or activation-aware, and whether the route can change automatically.
3. Pin a provider or disable fallbacks when comparing coding-agent results; otherwise two runs may use different infrastructure.
4. For maximum control, serve Xiaomi’s official BF16 artifacts yourself with the documented SGLang or vLLM path and test the exact tool-calling workload you care about. [1]

Is MiMo-V2.5 a good model for coding agents?

It is designed with agentic and coding use cases in mind, but that does not mean every integration will work perfectly on day one. Xiaomi describes MiMo-V2.5 as a native omnimodal model with agentic capabilities and says its post-training includes agentic reinforcement learning. The official API supports function tools, while the model card highlights long-context and agent-workflow use cases. Those are promising primitives for an agent that needs repository context, command execution, browser or test tools, and multi-step planning. [1][4]

The engineering question is narrower: does the endpoint preserve the information your agent needs? Test tool-call JSON validity, parallel or repeated tool calls, retention of reasoning metadata where required, handling of failed commands, long-session drift, and cost under real repository context. A model can be excellent at a coding benchmark and still be frustrating in an IDE if the client drops a field, truncates tool output, or sends a request format the provider does not accept.

Can you use MiMo-V2.5 through OpenRouter in Cursor?

Possibly, through Cursor’s OpenAI-base-URL override, but treat it as an unsupported integration rather than a dependable native path. Cursor staff have said OpenRouter is not an officially supported BYOK provider and that overriding the OpenAI base URL routes OpenAI-compatible requests through that URL instead of allowing the normal built-in Cursor models in the same session. Their guidance also says models must be added manually and that some agent features may be limited. [5]

For MiMo-V2.5 specifically, that means you may be able to enter an OpenRouter key, set the override URL, and add xiaomi/mimo-v2.5; it does not mean Cursor has certified the model’s agent protocol, reasoning-token handling, or tool-call behavior. Start with a disposable repository and a small task. If chat works but Agent mode fails, collect the request ID and inspect whether the failure happens before OpenRouter, at the router, or upstream at the selected provider.

How should you evaluate quantized MiMo-V2.5 for agent work?

Compare behavior, not just a label. Quantization can reduce memory use and cost dramatically, and a good deployment can be entirely suitable for many coding tasks. But agent workloads amplify small mistakes because one malformed tool call can derail the next ten steps. Run an A/B test against the same fixed commit, task list, system prompt, tools, token budget, temperature, and provider routing policy.

• Tool-call success rate and schema validity
• Completion rate for test-fix and repository-navigation tasks
• Number of retries, loops, and invalid shell commands
• Regression-test pass rate after the agent’s changes
• Input/output tokens, cache-hit rate, latency, and total task cost
• Failure modes after long context or multiple tool-call rounds

If the provider will not disclose precision, phrase your conclusion accurately: “This provider’s MiMo-V2.5 route performed acceptably on our workload,” not “this is the non-quantized official model.” That distinction protects you from overclaiming and makes future regressions easier to diagnose.

Use Cline when you want to test providers without committing to one

If you reached this point because you want to put MiMo-V2.5 behind a coding agent and evaluate it rather than merely chat with it, <a href="https://cline.bot">Cline</a> is an open-source coding agent with a VS Code extension and CLI. Its site says it supports OpenRouter along with many other providers, so it is a practical way to test a routed MiMo-V2.5 endpoint against the direct Xiaomi API while keeping the agent workflow constant.

For individual developers, Cline says its open-source version is free: you bring your own API keys or use its provider and pay usage-based inference costs, with no subscription or seat fee for that version. Its Enterprise offering is custom-priced and adds organizational controls such as centralized billing, SSO, role-based access control, and provider restrictions. That provider flexibility is directly relevant here: it lets you compare the official API, an OpenRouter route, and—where available—your own hosted deployment instead of mistaking the agent client for the model provider.