Try MiMo-V2.5-Pro: Non-Quantized Coding Agent Model

You can try MiMo-V2.5-Pro as a coding-agent model today through Xiaomi’s API, and its open weights are available under the MIT License. But the answer to whether a MiMo-V2.5-Pro non-quantized model is available is no in the usual deployment sense: Xiaomi’s official downloadable Pro checkpoint is FP8 mixed precision, rather than an all-BF16 or FP16 release.

Is MiMo-V2.5-Pro available as a non-quantized model?

The terminology needs a precise answer. “Non-quantized” usually means that a model is released in a higher-precision format such as BF16 or FP16, before someone converts it to lower-bit formats such as INT4, INT8, FP8, or FP4 for cheaper inference. MiMo-V2.5-Pro does not fit that expectation. The official model listing identifies its tensors as a mixture that includes FP8 E4M3, and Xiaomi describes the Pro model’s original release as FP8 mixed precision.

That does not mean the model is unusable or that it is a low-quality community conversion. FP8 is a floating-point format and can be part of a carefully engineered training and inference strategy. It does mean you should not describe the released weights as pristine full-precision weights. If your reason for searching for non-quantized weights is to make your own lossless conversion, compare precision behavior, or retain a BF16 master checkpoint for research, the official Pro download does not provide that artifact.

Also distinguish an FP8 checkpoint from a server configuration that uses BF16 arithmetic. A deployment guide may invoke a runtime with a BF16 dtype for compute or communication, sometimes dequantizing weights as part of loading. That can be necessary for hardware or framework compatibility, but it does not recreate unreleased BF16 source weights. The checkpoint’s stored numerical precision and the runtime’s requested compute dtype are separate facts.

What is MiMo-V2.5-Pro built for?

MiMo-V2.5-Pro is Xiaomi’s text-only flagship agent model. Xiaomi describes it as a trillion-parameter mixture-of-experts model with roughly 42 billion active parameters per token, a 1 million-token context window, tool calling, structured output, streaming, web search, and a maximum output of 128,000 tokens. Its emphasis is not autocomplete; it is sustained, multi-step work in which the model must inspect state, choose tools, interpret results, edit code, run checks, and continue.

That design makes it relevant for repository-level coding tasks: debugging an issue across packages, implementing a feature that touches API and UI layers, migrating a framework, or working through a test failure rather than merely proposing a patch. Xiaomi’s own framing includes complex software engineering and long-horizon agent tasks. Treat those vendor claims as a reason to evaluate the model, not as evidence that it will solve your production backlog autonomously.

The architectural trade-off is important for self-hosters. A trillion total parameters is still a colossal model to store and serve even when only a subset of experts is active for each token. “42B active” helps explain inference compute, but it does not shrink the checkpoint download, the memory required to hold weights, the KV cache, or the distributed-systems work needed to run long contexts reliably.

How can you try MiMo-V2.5-Pro for coding agents?

For most engineers, the sensible first path is Xiaomi’s hosted API. The API uses the model identifier mimo-v2.5-pro and supports both OpenAI-compatible and Anthropic-compatible protocols. That matters because many coding-agent clients already support one or both interfaces: usually you supply a Xiaomi API key, point the client at Xiaomi’s base URL, select the model, and make a small test task before trusting it with a major change.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_MIMO_API_KEY",
    base_url="https://api.xiaomimimo.com/v1",
)

response = client.chat.completions.create(
    model="mimo-v2.5-pro",
    messages=[
        {"role": "user", "content": "Inspect this repository and propose a test-first plan."}
    ],
)

print(response.choices[0].message.content)

This snippet proves connectivity; it does not turn a chat completion into a coding agent by itself. A coding agent needs a tool loop around the model: repository access, command execution, a patch application mechanism, test output returned as context, approval boundaries, and a stop condition. Use the tool-calling protocol supported by your client rather than asking the model to simulate shell output in plain text.

What does MiMo-V2.5-Pro cost to try?

At Xiaomi’s listed pay-as-you-go USD rates, MiMo-V2.5-Pro costs $0.435 per million input tokens on a cache miss, $0.0036 per million cached input tokens, and $0.87 per million output tokens. Xiaomi also offers subscription token plans, but their value depends on current plan terms and your volume. Check the live pricing page before estimating a large migration or autonomous run.

For coding agents, cache behavior deserves more attention than the headline input rate. An agent repeatedly sends its system prompt, repository rules, task history, file contents, and tool traces. When the provider can reuse that prefix, the difference between cache-hit and cache-miss pricing can dominate the economics. Keep durable instructions stable, avoid needlessly restarting tasks, and do not shovel an entire repository into every request when targeted retrieval will do.

Can you self-host MiMo-V2.5-Pro?

Yes, in the sense that the MiMo-V2.5 series weights were released under MIT and Xiaomi says they can be used commercially, fine-tuned, and deployed without additional authorization. Xiaomi also states that the release received day-zero adaptation work for vLLM and SGLang. That makes serious deployment technically possible rather than merely hypothetical.

No, in the sense that this is not a practical “download it to a workstation” model. The FP8 release is around a trillion total parameters. Weight storage alone is on the order of a terabyte before replicas, runtime overhead, KV cache, activation memory, and operational headroom. Its 1M-token context specification is a capability ceiling, not a free setting: long context consumes substantial memory and can fundamentally change serving capacity and latency.

A realistic self-hosting plan therefore starts with a multi-accelerator or multi-node serving environment, hardware that supports the relevant numeric formats efficiently, a supported inference stack, distributed parallelism, observability, and an honest target context length. If you need local control but do not operate that kind of infrastructure, a hosted endpoint—or a much smaller model—is usually the better engineering decision.

How should you evaluate MiMo-V2.5-Pro as a coding agent?

Do not judge it by asking for a function in an empty prompt. Build a small evaluation harness from work that resembles your team’s actual loop. Include a bug that requires reading several files, a task requiring a database migration and tests, a dependency upgrade, a failed CI reproduction, and an issue with deliberately incomplete requirements. Give each run the same repository state, tools, time budget, approval policy, and success criteria.

1. Measure task completion, not prose quality: tests pass, lint succeeds, the requested behavior works, and the patch is maintainable.
2. Record intervention count: how many times a developer had to correct the plan, point to a file, repair a command, or prevent a destructive action.
3. Track token use and wall-clock time separately. A cheap token price can still produce an expensive task if the agent loops or emits long reasoning and tool traces.
4. Test long-context claims with realistic retrieval. Load only relevant files first, then intentionally add distractors and larger histories to see where task quality changes.
5. Keep a human review gate. Tool-capable models can make broad changes quickly, including confidently incorrect ones.

The outcome you want is not a universal ranking. It is an operating envelope: the class of task, repository size, context budget, and tool policy where MiMo-V2.5-Pro saves engineering time without creating review debt. That is a more durable purchasing and architecture decision than any one benchmark score.

Using MiMo-V2.5-Pro with Cline

If you want to evaluate the model in an agent interface rather than build the loop yourself, Cline is an AI coding agent for editors and the terminal. Its documentation says it can read and write files, run terminal commands, and use a browser through natural-language tasks, with explicit approval required for actions. Xiaomi’s MiMo documentation also lists a direct integration path for Cline using the same API format.

Cline’s open-source offering is free for individual developers; model inference is usage-based, either through its billing options or by bringing your own provider credentials. Its site also lists a $9.99/month ClinePass option for increased usage on popular open coding models, while enterprise pricing is custom. For this specific model, the appeal is control over the actual coding-agent loop—repository context, commands, diffs, and approvals—while you test whether MiMo-V2.5-Pro’s FP8 open release is useful for your real codebase.