Model reference · open weights
MiMo-Flash is an open-weight language model from XiaomiMiMo. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | XiaomiMiMo |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 309.8B |
| Context | 256k tokens |
| Runs with | transformers |
| Released | 2025-12-16 |
| Popularity | 93k downloads / month |
| Licence | Open weights |
About
| | | |
MiMo-V2-Flash is a Mixture-of-Experts (MoE) language model with 309B total parameters and 15B active parameters. Designed for high-speed reasoning and agentic workflows, it utilizes a novel hybrid attention architecture and Multi-Token Prediction (MTP) to achieve state-of-the-art performance while significantly reducing inference costs.
MiMo-V2-Flash creates a new balance between long-context modeling capability and inference efficiency. Key features include:
| Model | Total Params | Active Params | Context Length | Download |
|---|---|---|---|---|
| MiMo-V2-Flash-Base | 309B | 15B | 256k | 🤗 HuggingFace |
| MiMo-V2-Flash | 309B | 15B | 256k | 🤗 HuggingFace |
[!IMPORTANT] We also open-source the 3-layer MTP weights to foster community research.
MiMo-V2-Flash-Base demonstrates strong performance across standard benchmarks, surpassing models with significantly larger parameter counts.
| Category | Benchmark | Setting/Length | MiMo-V2-Flash Base | Kimi-K2 Base | DeepSeek-V3.1 Base | DeepSeek-V3.2 Exp Base |
|---|---|---|---|---|---|---|
| Params | #Activated / #Total | - | 15B / 309B | 32B / 1043B | 37B / 671B | 37B / 671B |
| General | BBH | 3-shot | 88.5 | 88.7 | 88.2 | 88.7 |
| MMLU | 5-shot | 86.7 | 87.8 | 87.4 | 87.8 | |
| MMLU-Redux | 5-shot | 90.6 | 90.2 | 90.0 | 90.4 | |
| MMLU-Pro | 5-shot | 73.2 | 69.2 | 58.8 | 62.1 | |
| DROP | 3-shot | 84.7 | 83.6 | 86.3 | 86.6 | |
| ARC-Challenge | 25-shot | 95.9 | 96.2 | 95.6 | 95.5 | |
| HellaSwag | 10-shot | 88.5 | 94.6 | 89.2 | 89.4 | |
| WinoGrande | 5-shot | 83.8 | 85.3 | 85.9 | 85.6 | |
| TriviaQA | 5-shot | 80.3 | 85.1 | 83.5 | 83.9 | |
| GPQA-Diamond | 5-shot | 55.1 | 48.1 | 51.0 | 52.0 | |
| SuperGPQA | 5-shot | 41.1 | 44.7 | 42.3 | 43.6 | |
| SimpleQA | 5-shot | 20.6 | 35.3 | 26.3 | 27.0 | |
| Math | GSM8K | 8-shot | 92.3 | 92.1 | 91.4 | 91.1 |
| MATH | 4-shot | 71.0 | 70.2 | 62.6 | 62.5 | |
| AIME 24&25 | 2-shot | 35.3 | 31.6 | 21.6 | 24.8 | |
| Code | HumanEval+ | 1-shot | 70.7 | 84.8 | 64.6 | 67.7 |
| MBPP+ | 3-shot | 71.4 | 73.8 | 72.2 | 69.8 | |
| CRUXEval-I | 1-shot | 67.5 | 74.0 | 62.1 | 63.9 | |
| CRUXEval-O | 1-shot | 79.1 | 83.5 | 76.4 | 74.9 | |
| MultiPL-E HumanEval | 0-shot | 59.5 | 60.5 | 45.9 | 45.7 | |
| MultiPL-E MBPP | 0-shot | 56.7 | 58.8 | 52.5 | 50.6 | |
| BigCodeBench | 0-shot | 70.1 |
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys mimo-flash for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (mimo-flash below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"mimo-flash","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.