Why MiMo-V2.5-Pro Changes the Open-Source Coding Agent Equation
Every few months, an open-source model arrives that genuinely challenges the closed-source frontier. MiMo-V2.5-Pro, released by Xiaomi on April 22, 2026, is the latest to make that claim β and its benchmarks suggest it earns the comparison.
The model sits at 57.2% on SWE-bench Pro, placing it above Claude Opus 4.6 (53.4%) and within half a point of GPT-5.4 (57.7%). On ClawEval, the agentic benchmark tied to the OpenClaw framework, it hits 63.8% PassΒ³ while consuming roughly 40β60% fewer tokens per trajectory than the frontier closed models.
That combination matters for a specific reason: long-horizon agentic tasks are expensive. When a coding agent runs for 11 hours, makes 1,868 tool calls, and produces 8,000 lines of code, token cost is not a footnote β it's the budget line that determines whether the workflow is economically viable at all. MiMo-V2.5-Pro's efficiency advantage is the reason Xiaomi's demo showcasing an autonomous desktop video editor build is more than a party trick.
This guide covers the model's architecture, verified benchmark numbers, how to call it via API today, and what self-hosting actually requires.
What MiMo-V2.5-Pro Is
MiMo-V2.5-Pro is a 1.02 trillion total parameter, 42 billion active parameter Mixture-of-Experts (MoE) model. It is fully open-sourced under the MIT license, meaning you can use it commercially, fine-tune it, and redistribute derivatives without additional authorization. Weights, tokenizer, and the full model card are available at XiaomiMiMo/MiMo-V2.5-Pro on HuggingFace.
The model belongs to the MiMo series, which Xiaomi has positioned as its agentic coding lineup. MiMo-V2-Pro launched earlier in 2026 and demonstrated competitive results. MiMo-V2.5-Pro is described by Xiaomi as "a major leap in general agentic capabilities, complex software engineering, and long-horizon tasks."
Do not confuse it with MiMo-V2.5 (without the "Pro" suffix). That sibling model is a 310B total / 15B active parameter omnimodal model β it handles text, image, video, and audio in a unified architecture. MiMo-V2.5-Pro is language-and-reasoning focused, the one relevant for coding agent pipelines.
Architecture Deep Dive
Understanding the architecture explains why this model is efficient at long-context agentic tasks.
Mixture of Experts with 42B Active Parameters
With 1.02T total parameters and only 42B activated per forward pass, the MoE routing ensures each inference call has a compute footprint comparable to a dense 42B model β not a 1T one. This is the same design principle behind DeepSeek-V4 and Mistral's Mixtral series, but scaled to a larger expert pool.
Hybrid Attention for Long Contexts
MiMo-V2.5-Pro inherits the hybrid attention design from MiMo-V2-Flash:
- Local Sliding Window Attention (SWA) with a 128-token window
- Global Attention (GA) interleaved at a 6:1 ratio (6 SWA layers per GA layer)
The practical effect: KV-cache memory is reduced by approximately 7x compared to full global attention at long contexts. This makes the 1M-token context window economically usable in production β not just technically possible.
Multi-Token Prediction (MTP)
Three lightweight MTP modules using dense FFNs allow the model to predict multiple tokens ahead simultaneously. This accelerates inference without requiring speculative decoding infrastructure on the serving side. For agentic loops where the model outputs structured tool calls, faster token generation directly reduces wall-clock time per iteration.
Verified Benchmark Numbers
Effloow Lab collected these figures from Xiaomi's official product page, VentureBeat's April 22 2026 coverage, and the MarkTechPost analysis. The comparison data comes from Xiaomi's published efficiency chart, which places competing models on the same axes.
| Model | SWE-bench Pro | ClawEval PassΒ³ | Tokens/Trajectory | License |
|---|---|---|---|---|
| MiMo-V2.5-Pro | 57.2% | 63.8% | ~70K | MIT |
| Claude Opus 4.6 | 53.4% | Lower | ~120K+ | Proprietary |
| GPT-5.4 | 57.7% | Lower | ~120K+ | Proprietary |
| Gemini 3.1 Pro | [DATA NOT AVAILABLE] | Lower | ~120K+ | Proprietary |
Note on the token counts: "~120K+" represents Xiaomi's published comparison chart for competing models on ClawEval. Xiaomi did not publish third-party-audited token trace data, so treat these ratios as directional, not exact. The SWE-bench scores are from public leaderboard results.
The Long-Horizon Demo Numbers
Xiaomi's official site documents two sustained autonomous runs:
- SysY Rust Compiler β 233/233 test cases passed, 672 tool calls, 4.3 hours
- Desktop Video Editor β 8,192 lines of code produced, 1,868 tool calls, 11.5 hours
These are controlled demos, not independent reproductions. They demonstrate that the model architecture can sustain coherent multi-step plans across thousands of iterations β a known failure mode for weaker models that lose task context mid-run.
Calling MiMo-V2.5-Pro via API Today
The fastest path to using the model is through OpenRouter, which exposes it at xiaomi/mimo-v2.5-pro on an OpenAI-compatible endpoint. Pricing: $1.00/M input tokens, $3.00/M output tokens.
For reference, Claude Opus 4.6 costs $5/$25 per million input/output tokens. On output β which dominates agentic workloads β MiMo-V2.5-Pro is approximately 8x cheaper.
Basic Setup
Install the OpenAI SDK (it's compatible with OpenRouter's endpoint):
pip install openai
Set your OpenRouter key as an environment variable:
export OPENROUTER_API_KEY="your-key-here"
Simple Coding Task Call
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="your-openrouter-key",
)
response = client.chat.completions.create(
model="xiaomi/mimo-v2.5-pro",
messages=[
{
"role": "system",
"content": "You are an expert software engineer. Respond with complete, working code."
},
{
"role": "user",
"content": "Write a Python function that parses a cron expression and returns the next 5 run times."
}
],
max_tokens=4096,
)
print(response.choices[0].message.content)
Enabling Reasoning Mode
MiMo-V2.5-Pro supports extended reasoning via a reasoning parameter. This activates the model's internal chain-of-thought before the final response:
response = client.chat.completions.create(
model="xiaomi/mimo-v2.5-pro",
messages=[...],
extra_body={
"reasoning": {"effort": "high"}
},
max_tokens=16384,
)
# Access internal reasoning trace
if hasattr(response.choices[0].message, "reasoning_details"):
for block in response.choices[0].message.reasoning_details:
print(f"[Reasoning] {block}")
print(response.choices[0].message.content)
Enabling reasoning increases token consumption but can meaningfully improve results on complex multi-file refactoring or system design tasks.
Using the Xiaomi API Platform
Xiaomi operates its own platform at platform.xiaomimimo.com, which uses the same model weights. At launch, Xiaomi offered 100 trillion free tokens to early users β availability of that promotion should be confirmed directly on the platform, as terms may have changed since April 2026.
Self-Hosting MiMo-V2.5-Pro
Self-hosting a 1T-parameter MoE model is not a developer-laptop task. Here is what the infrastructure actually looks like based on official vLLM documentation and AMD's day-0 support announcement.
Minimum Hardware
The AMD Instinct MI355X is the reference configuration used in AMD's official deployment guide: 288 GB on-chip memory, 8 TB/s bandwidth. For NVIDIA, tensor parallel across 8x H100 SXM (640 GB aggregate HBM3) is the minimum practical configuration.
vLLM Deployment
Standard stable vLLM does not yet support MiMo-V2.5-Pro. You need the vLLM nightly build:
pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly/
Launch the server:
vllm serve XiaomiMiMo/MiMo-V2.5-Pro \
--tensor-parallel-size 8 \
--gpu-memory-utilization 0.95 \
--max-model-len 32768 \
--trust-remote-code
A few notes:
-
--max-model-len 32768limits context to 32K for memory management β increase carefully if you have headroom -
--trust-remote-codeis required because MiMo's custom attention layers are not in the default vLLM registry - For AMD GPUs, ROCm 6.x with PyTorch 2.4+ is required; vLLM's ROCm build is separate from the CUDA wheel
SGLang Alternative
SGLang supports MiMo-V2.5-Pro and can be faster than vLLM for high-concurrency agentic workloads due to its RadixAttention KV cache sharing. Check the SGLang docs for the current compatibility matrix before deploying.
MiMo-V2.5 vs MiMo-V2.5-Pro: Which to Use
If you're new to the MiMo series, the naming can confuse which model belongs in which pipeline.
MiMo-V2.5-Pro is a language-and-reasoning specialist. It handles text, code, and structured tool use. It's the right choice for:
- Autonomous coding agents
- Long-horizon software engineering tasks
- API-driven coding workflows where token cost matters
MiMo-V2.5 (without "Pro") is the omnimodal model: 310B total / 15B active parameters, trained on ~48T tokens, with native image, video, and audio understanding. Use it for:
- Multimodal workflows (analyzing UI screenshots, processing audio logs)
- Tasks where 15B active parameters is sufficient compute
- Scenarios where model size on disk matters (it's significantly smaller)
Both are MIT-licensed and available on HuggingFace under the XiaomiMiMo organization.
When MiMo-V2.5-Pro Is the Right Choice
The model's profile makes it a strong fit in specific circumstances:
Good fit:
- Teams running agentic coding pipelines that process thousands of tool calls daily β the token efficiency directly translates to budget
- Open-source projects that need a capable coding model but cannot afford closed API costs at scale
- Organizations with data residency or privacy requirements that prevent sending code to third-party APIs
- Research groups fine-tuning a frontier-class MoE under MIT terms
Less ideal:
- Teams without the GPU infrastructure for self-hosting and prefer a simple managed API β Claude or GPT-5.4 remain simpler to operate
- Multimodal pipelines that need to process images or audio alongside code β use MiMo-V2.5 or a specialized vision model
- Teams on a single GPU or small cluster β 8x H100 minimum is a real barrier
Common Integration Mistakes
Treating it like a small model. At 42B active parameters, MiMo-V2.5-Pro generates tokens more slowly than a 7B or 14B model. For interactive applications that need sub-second latency, benchmark your specific hardware before committing.
Ignoring the vLLM nightly requirement. Attempting deployment on stable vLLM will fail. The nightly build is necessary until MiMo's hybrid attention lands in a stable release.
Skipping the reasoning parameter for hard tasks. The default chat completion mode does not activate the full reasoning chain. For complex refactoring or algorithm design, the reasoning parameter in the request body can substantially improve output quality at the cost of more tokens.
Not using temperature 0.0 for code generation. Like most coding models, MiMo-V2.5-Pro performs more consistently on deterministic tasks when temperature is set to 0. Sampling at higher temperatures is appropriate for brainstorming but not for producing executable code in production pipelines.
Frequently Asked Questions
Q: Is MiMo-V2.5-Pro actually better than Claude Opus 4.6 for coding?
On SWE-bench Pro, MiMo-V2.5-Pro (57.2%) scores higher than Claude Opus 4.6 (53.4%). On ClawEval, it also leads. However, benchmarks measure specific task distributions, and real-world performance on your codebase may differ. Token efficiency is where the advantage is clearest: if you're running agents at scale, you spend meaningfully less per trajectory.
Q: Can I fine-tune MiMo-V2.5-Pro?
Yes. The MIT license explicitly allows continued training and fine-tuning. Xiaomi provides the base checkpoint (XiaomiMiMo/MiMo-V2.5-Pro-Base on HuggingFace) for teams that want to start from pre-RLHF weights. Fine-tuning a 42B-active-parameter MoE still requires substantial compute β at minimum, a multi-GPU setup with gradient checkpointing.
Q: What is ClawEval and why does it matter?
ClawEval is an agentic benchmark tied to the OpenClaw evaluation framework. It measures a model's ability to complete multi-step, real-world autonomous tasks β coding, system design, tool use, and long-horizon planning β not just single-turn question answering. It's more representative of how coding agents actually behave in production than MMLU or HumanEval, which test isolated knowledge or simple function generation.
Q: How do I use it with Claude Code or other agent frameworks?
MiMo-V2.5-Pro exposes an OpenAI-compatible endpoint via OpenRouter and the Xiaomi API Platform. Any framework that accepts an OpenAI base URL can route to it: set base_url to OpenRouter's endpoint, api_key to your OpenRouter key, and model to xiaomi/mimo-v2.5-pro. There is also a community guide for integrating it with Claude Code via the ANTHROPIC_BASE_URL override pattern.
Key Takeaways
- MiMo-V2.5-Pro is a 1.02T total / 42B active MoE model released April 22, 2026, under MIT license
- It scores 57.2% on SWE-bench Pro (above Claude Opus 4.6 at 53.4%) and 63.8% on ClawEval
- Its primary competitive advantage is token efficiency: ~70K tokens per ClawEval trajectory versus ~120K+ for comparable frontier models β roughly 40β60% fewer tokens
- API cost via OpenRouter is $1/$3 per million input/output tokens, compared to $5/$25 for Claude Opus 4.6 β approximately 8x cheaper on output
- Self-hosting requires 8x H100 or AMD MI355X class hardware and vLLM nightly build
- MiMo-V2.5 (without "Pro") is the omnimodal sibling β 310B/15B active, adds image/video/audio, different use case
Bottom Line
For teams running coding agents at scale, MiMo-V2.5-Pro offers a rare combination: frontier-class benchmark scores, MIT license that permits commercial fine-tuning, and 8x cheaper output tokens than Claude Opus 4.6. The barrier is hardware β self-hosting is a multi-H100 commitment. For managed API access, OpenRouter removes that blocker entirely.
