Xiaomi: MiMo-V2.5
xiaomi/mimo-v2.5
Model weightsMiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding tasks. Its 1M context window supports complete documents, extended conversations, and complex task contexts in a single pass, making it ideal for integration with agent frameworks where strong reasoning, rich perception, and cost efficiency all matter.
Providers
This model is hosted by one provider. OpenRouter forwards every request without routing.
| $0.40 | $2.00 | $0.08 | 1.11s | 52 tok/s |
- Input $0.40 / Output $2.0052 tok/s98.33% uptime
Pricing
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Weighted average
MiMo-V2.5 — Price history
| $0.174 | $2.00 | 70.5% | 100.0% |
- Effective in $0.174 / Effective out $2.00Token share100.0%
Performance
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
P50, best across providers
P50, best provider
Uptime
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Availability over the last 3 days
Last 72 hoursAvailability over the last 24 hours
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.
Benchmarks
Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | MiMo-V2.5 Coding Index | 56.8 |
| Artificial Analysis | MiMo-V2.5 Agentic Index | 15.8 |
| Artificial Analysis | MiMo-V2.5 GPQA Diamond | 84.9% |
| Artificial Analysis | MiMo-V2.5 HLE | 27.2% |
| Artificial Analysis | MiMo-V2.5 IFBench | 67.1% |
| Artificial Analysis | MiMo-V2.5 τ²-Bench Telecom | 90.6% |
| Artificial Analysis | MiMo-V2.5 AA-LCR | 73.0% |
| Artificial Analysis | MiMo-V2.5 τ-Bench Banking | 8.7% |
| Artificial Analysis | MiMo-V2.5 GDPval-AA | 25.3% |
| Artificial Analysis | MiMo-V2.5 CritPt | 3.7% |
| Artificial Analysis | MiMo-V2.5 SciCode | 43.9% |
| Artificial Analysis | MiMo-V2.5 Terminal-Bench Hard | 41.7% |
| Artificial Analysis | MiMo-V2.5 Terminal-Bench 4.0 | 0.0% |
| Artificial Analysis | MiMo-V2.5 Terminal-Bench 2.1 | 63.7% |
| Design Arena | MiMo-V2.5 Models Arena 3D Elo | 1197 |
| Design Arena | MiMo-V2.5 Models Arena Asciiart Elo | 1139 |
| Design Arena | MiMo-V2.5 Models Arena Code Categories Elo | 1252 |
| Design Arena | MiMo-V2.5 Models Arena Data Visualization Elo | 1249 |
| Design Arena | MiMo-V2.5 Models Arena Game Development Elo | 1242 |
| Design Arena | MiMo-V2.5 Models Arena SVG Elo | 1175 |
| Design Arena | MiMo-V2.5 Models Arena UI Component Elo | 1249 |
| Design Arena | MiMo-V2.5 Models Arena Website Elo | 1260 |
| Vals AI | MiMo V2.5 Code Migration | 14.2% |
| Vals AI | MiMo V2.5 CorpFin v2 | 59.9% |
| Vals AI | MiMo V2.5 EMB | 55.1% |
| Vals AI | MiMo V2.5 Finance Agent (v2) | 36.7% |
| Vals AI | MiMo V2.5 GPQA Diamond | 81.6% |
| Vals AI | MiMo V2.5 Harvey's Legal Agent Benchmark v1 | 1.7% |
| Vals AI | MiMo V2.5 LiveCodeBench | 81.5% |
| Vals AI | MiMo V2.5 LegalBench | 78.9% |
| Vals AI | MiMo V2.5 Legal Research Bench | 9.1% |
| Vals AI | MiMo V2.5 MedCode | 31.9% |
| Vals AI | MiMo V2.5 MedScribe | 72.2% |
| Vals AI | MiMo V2.5 MMLU Pro | 82.9% |
| Vals AI | MiMo V2.5 MMMU Pro | 80.0% |
| Vals AI | MiMo V2.5 MortgageTax | 59.3% |
| Vals AI | MiMo V2.5 ProofBench v1.1 | 16.0% |
| Vals AI | MiMo V2.5 SAGE v1 | 43.3% |
| Vals AI | MiMo V2.5 SWE-bench | 71.0% |
| Vals AI | MiMo V2.5 Tax Agent Bench | 7.8% |
| Vals AI | MiMo V2.5 TaxEval v2 | 71.8% |
| Vals AI | MiMo V2.5 Terminal-Bench 2.1 | 60.7% |
| Vals AI | MiMo V2.5 Vals Index | 39.9% |
| Vals AI | MiMo V2.5 Vals Multimodal Index | 52.8% |
| Vals AI | MiMo V2.5 Vibe Code Bench v1.1 | 42.2% |
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity
Token volume and request traffic to this model over time.
API
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
Not currently available through EU in-region routing. In-region routing docs
Frequently asked questions
MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding tasks.
MiMo-V2.5 costs $0.40/M input tokens and $2.00/M output tokens, with separate rates for Cache Read at $0.08/M tokens.
MiMo-V2.5 has a 1,000,000 token context window. It supports up to 65,536 completion tokens.
Yes. MiMo-V2.5 accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
MiMo-V2.5 accepts text, audio, images, and video as input and returns text.
MiMo-V2.5 is served by 1 provider on OpenRouter: Venice.
MiMo-V2.6-Pro-UltraSpeed, MiMo-V2.6-Flash, MiMo-V2.6-Pro and 1 more are other text models from Xiaomi.
MiMo-V2.5 was released on April 22, 2026.