Xiaomi • llm
Grande modelo de linguagem (llm) desenvolvido pela Xiaomi — Intelligence Index 42/100 no Artificial Analysis; US$ 0.100/1M tokens de entrada; 150 tokens/s de velocidade.
Context Window
—
Input Price/1M
$0.10
Output Price/1M
$0.30
Parameters
—
Speed
134 tok/s
Latency (TTFT)
1.4s
MiMo-V2-Flash (Feb 2026) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 31.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 86.8 | 100.0 | Artificial Analysis official API |
| SciCode | 38.0 | 100.0 | — |
| AA Coding Index | 33.5 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 64.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Math Index | 96.3 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 96.3 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 41.5 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 84.3 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 84.0 | 100.0 | Artificial Analysis official API |
| IFBench | 72.0 | 100.0 | — |
| HLE | 20.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 93.0 | 100.0 | — |
MiMo-V2-Flash (Feb 2026) is an AI model developed by Xiaomi, classified as a llm model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Xiaomi's cloud API.
MiMo-V2-Flash (Feb 2026) is usage-based, priced at $0.1/1M input tokens and $0.3/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
MiMo-V2-Flash (Feb 2026) was evaluated on 13 different benchmarks, covering categories like Agentic, Coding, Long Context, Math, overall, Reasoning, Tool Use. Results show exceptional performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
MiMo-V2-Flash (Feb 2026) is suitable for a wide range of AI applications: high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, MiMo-V2-Flash (Feb 2026) competes directly with similarly capable models. Xiaomi competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
MiMo-V2-Flash (Feb 2026) is an AI model developed by Xiaomi. It is a llm model.
MiMo-V2-Flash (Feb 2026) costs $0.1/1M input tokens and $0.3/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, MiMo-V2-Flash (Feb 2026) scored: Terminal-Bench Hard: 31/100, LiveCodeBench: 86.8/100, SciCode: 38/100. See the full table above for a detailed comparison.
No, MiMo-V2-Flash (Feb 2026) is a proprietary model from Xiaomi. It is available via cloud API. For open source alternatives, check our open source model ranking.
MiMo-V2-Flash (Feb 2026) excels at general-purpose language tasks.
Last updated: June 01, 2026 • View methodology →