# MiMo-V2.5 Xiaomi · Proprietary · rank 57 · bench-align-v5 > Every figure below is reproduced as its upstream source published it: nothing is modelled, estimated, interpolated or converted. `Unavailable` means no source published the value — it is never a zero. Each value carries its evidence state and the date it was observed. Page: https://modelscale.dev/models/mimo-v2-5 JSON: https://modelscale.dev/api/model/mimo-v2-5 ## Facts | Field | Value | Evidence | | --- | --- | --- | | Canonical id | `mimo-v2-5` | — | | Overall score | 55.56 | Last good 2026-09-24 · source benchlm:models | | Context window | 1M tokens | Observed 2026-10-23 · source benchlm:models | | Release date | 2026-04-22 | Observed 2026-10-23 · source benchlm:models | | Access type | Proprietary | — | | Blended $/1M (75% input / 25% output) | Unavailable | Derived from the input and output rates below | ## Capability evidence Seven axes from the ranking source. An axis the source did not score is unavailable, not zero. | Axis | Score | Evidence | | --- | --- | --- | | Agentic | 66 | Observed 2026-10-23 · source benchlm:models | | Coding | 47.8 | Observed 2026-10-23 · source benchlm:models | | Knowledge | 53.9 | Observed 2026-10-23 · source benchlm:models | | Reasoning | Unavailable | Unavailable · source benchlm:models | | Multimodal & Grounded | 72.2 | Observed 2026-10-23 · source benchlm:models | | Instruction Following | Unavailable | Unavailable · source benchlm:models | | Math | Unavailable | Unavailable · source benchlm:models | ## Runtime service evidence Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim. Regional or per-endpoint measurements appear only when the API supplies them; none are modelled. | Measurement | Value | Observed | Last good | Evidence | | --- | --- | --- | --- | --- | | Time to first token | 63.46 s | 2026-10-23 | 2026-10-23 | Observed 2026-10-23 · source benchlm:speed | | Throughput | 35 tok/s | 2026-10-23 | 2026-10-23 | Observed 2026-10-23 · source benchlm:speed | ## Endpoint and price matrix Every published price component, including cache reads and writes. | Component | USD | Evidence | | --- | --- | --- | | Input / 1M tokens | Unavailable | Unavailable · source benchlm:pricing | | Output / 1M tokens | Unavailable | Unavailable · source benchlm:pricing | | Cache read / 1M tokens | Unavailable | Unavailable · source benchlm:pricing | | Cache write / 1M tokens | Unavailable | Unavailable · source openrouter:pricing | | Blended / 1M (75% input / 25% output) | Unavailable | Derived — from the input and output rates above; it has no source record of its own | | Cost per successful task (LiveBench) | Unavailable | Fetch failed · source livebench:table | **Self-hosted listing.** We did not find a current first-party public API pricing page for Xiaomi MiMo-V2.5. BenchLM treats pricing as unavailable until Xiaomi publishes one. No hosted token rate was published for this model, so its per-token price is unavailable rather than zero. ## Workload-aware monthly cost example 10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. Derived here from the published rates above by this site's own calculator — not a figure any source published. | Field | Value | | --- | --- | | Modelled monthly cost | Unavailable | | Modelled tokens | Unavailable | | Reason | The applicable input rate is unavailable. | ## Benchmark record 16 matched benchmark rows with their published value, unit, and provenance. ### Knowledge | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | GPQA Diamond (Vals) (GPQA Diamond, Vals AI run) | 81.6 | Graduate-level science questions | Expert reasoning | [Vals AI GPQA Diamond, Vals AI run leaderboard](https://www.vals.ai/benchmarks/gpqa) | | MMLU-Pro (Vals) (MMLU-Pro, Vals AI run) | 82.9 | Academic multiple-choice questions | Broad academic knowledge | [Vals AI MMLU-Pro, Vals AI run leaderboard](https://www.vals.ai/benchmarks/mmlu_pro) | ### Coding | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | Terminal-Bench 2.0 | 65.8 | Terminal-based software tasks | Professional software engineering | [Terminal-Bench 2.0](https://www.tbench.ai/) | | SWE-bench Pro | 56.1 | 1,865 repository problems | Long-horizon professional engineering | [SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?](https://arxiv.org/abs/2509.16941) | | LiveCodeBench (Vals) (LiveCodeBench, Vals AI run) | 81.5 | Competitive programming problems (easy, medium, hard) | Frontier coding | [Vals AI LiveCodeBench, Vals AI run leaderboard](https://www.vals.ai/benchmarks/lcb) | | SWE-bench (Vals) (SWE-bench, Vals AI run) | 71.0 | Real repository issues by human time bucket | Frontier coding agents | [Vals AI SWE-bench, Vals AI run leaderboard](https://www.vals.ai/benchmarks/swebench) | ### Agentic | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | Terminal-Bench 2.0 | 65.8 | Terminal-based software tasks | Professional software engineering | [Terminal-Bench 2.0](https://www.tbench.ai/) | | Gert Labs (Gert Labs Composite Game Benchmark) | 46.89 | Novel game environments | Agentic coding and decision-making | [Gert Labs rankings](https://gertlabs.com/rankings) | | MM-ClawBench | 23.8 | OpenClaw-style real-world tasks | Broad real-world agentic execution | [MiniMax M2.7: Early Echoes of Self-Evolution](https://www.minimax.io/news/minimax-m27-en) | | Claw-Eval | 62.3 | 300 tasks, 2,159 rubrics | Real-world general, multi-turn, and native multimodal agent execution | [Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents](https://arxiv.org/abs/2604.06132) | | ResearchClawBench | 16.9 | 40 tasks across 10 scientific domains | Scientific research re-discovery | [ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research](https://arxiv.org/abs/2606.07591) | | Terminal-Bench 2.1 (Vals) (Terminal-Bench 2.1, Vals AI run) | 60.7 | Difficult terminal tasks | Frontier agentic | [Vals AI Terminal-Bench 2.1, Vals AI run leaderboard](https://www.vals.ai/benchmarks/terminal-bench-2-1) | ### Multimodal & Grounded | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | MMMU-Pro (Massive Multi-discipline Multimodal Understanding Pro) | 77.9 | Multimodal academic reasoning | Frontier multimodal | [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://arxiv.org/abs/2409.02813) | | Design Arena Website (Design Arena Website Elo) | 1266 | Website generation comparisons | Design and website generation | [OpenRouter Grok 4.3 benchmarks](https://openrouter.ai/x-ai/grok-4.3/benchmarks) | | Video-MME (with subtitle) (Video-MME with subtitle) | 87.7 | Video understanding | Multimodal video reasoning | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) | | CharXiv (CharXiv Reasoning) | 81 | Scientific chart reasoning | Scientific visualization reasoning | [CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://charxiv.github.io/) | ## Lifecycle and limitations log Lifecycle events the source associates with this model. No lifecycle event references this model. That is not evidence the model has no lifecycle plan — only that this source published none. ## What this profile does not claim Values are reproduced exactly as their sources published them, in the units those sources declared; none are converted, interpolated, or averaged across providers. Any field marked unavailable was attempted and not returned. Last attempted fetch for this model's score: 2026-10-23 13:17 UTC.