MiMo-V2.5 vs GPT-5.1
Head-to-head specifications
| Metric | MiMo-V2.5 | GPT-5.1 | Difference |
|---|---|---|---|
| Intelligence Index | 37.0 | 37.0 | — |
| Coding Index | 56.8 | 49.4 | +15.0% |
| Agentic Index | 23.7 | 21.0 | — |
| Context window | 1M tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $0.06 | $0.77 | -92.2% |
| Output speed (tokens/s) | 83 | 106 | -21.7% |
| Access | Open weights | Proprietary API | — |
- MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 37.0).
- MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 12.8× cheaper.
- MiMo-V2.5 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: MiMo-V2.5 or GPT-5.1?
MiMo-V2.5 advantages
- Coding ability (+13%)
- Agentic task performance (+11%)
- Context window (+49%)
- Affordability (+92%)
GPT-5.1 advantages
- Output speed (+22%)
Which should you choose?
- Choose the MiMo-V2.5 if coding and software development are your main workload.
- Choose the GPT-5.1 if low latency and fast generation matter for your application.
- Choose the MiMo-V2.5 if you build agents or multi-step tool-use workflows.
Value for money
MiMo-V2.5 offers more intelligence per dollar (12.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
MiMo-V2.5 vs GPT-5.1: which should you choose?
MiMo-V2.5 — Xiaomi multimodal model with an Intelligence Index of 37, a 1M-token context window and a blended price of $0.06/1M tokens (open weights).
GPT-5.1 — OpenAI multimodal model with an Intelligence Index of 37, a 512K-token context window and a blended price of $0.77/1M tokens.
MiMo-V2.5 vs GPT-5.1: MiMo-V2.5 scores higher on the Intelligence Index. MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 37.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 12.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiMo-V2.5 scores 37.0 versus 37.0. For software development, the Coding Index puts MiMo-V2.5 ahead (56.8 vs 49.4). On agentic, multi-step tool-use tasks, MiMo-V2.5 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The MiMo-V2.5 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.1 generates faster (106 vs 83 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiMo-V2.5 is the cheaper model to run ($0.06 vs $0.77 per 1M tokens). MiMo-V2.5 is open weights and GPT-5.1 is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the MiMo-V2.5 better than the GPT-5.1?
MiMo-V2.5 is the clearly stronger overall choice, winning most of the dimensions that matter. MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 37.0).
What is the main difference between the MiMo-V2.5 and the GPT-5.1?
MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 37.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 12.8× cheaper.
Which is better value?
MiMo-V2.5 offers more intelligence per dollar (12.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the MiMo-V2.5 if coding and software development are your main workload. Choose the GPT-5.1 if low latency and fast generation matter for your application.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.