MiMo-V2.5 vs GPT-5 mini
Head-to-head specifications
| Metric | MiMo-V2.5 | GPT-5 mini | Difference |
|---|---|---|---|
| Intelligence Index | 37.0 | 32.0 | +15.6% |
| Coding Index | 56.8 | 15.6 | +264.1% |
| Agentic Index | 23.7 | 19.4 | — |
| Context window | 1M tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.06 | $0.26 | -76.9% |
| Output speed (tokens/s) | 83 | 93 | -10.8% |
| Access | Open weights | Proprietary API | — |
- MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 32.0).
- MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 4.3× cheaper.
- MiMo-V2.5 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: MiMo-V2.5 or GPT-5 mini?
MiMo-V2.5 advantages
- General intelligence (+14%)
- Coding ability (+73%)
- Agentic task performance (+18%)
- Context window (+8%)
- Affordability (+77%)
GPT-5 mini advantages
- Output speed (+11%)
Which should you choose?
- Choose the MiMo-V2.5 if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5 mini if low latency and fast generation matter for your application.
- Choose the MiMo-V2.5 if coding and software development are your main workload.
Value for money
MiMo-V2.5 offers more intelligence per dollar (5.0× 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 mini: 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 mini — OpenAI multimodal model with an Intelligence Index of 32, a 922K-token context window and a blended price of $0.26/1M tokens.
MiMo-V2.5 vs GPT-5 mini: MiMo-V2.5 scores higher on the Intelligence Index. MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 32.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 4.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiMo-V2.5 scores 37.0 versus 32.0. For software development, the Coding Index puts MiMo-V2.5 ahead (56.8 vs 15.6). 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 mini generates faster (93 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 $0.26 per 1M tokens). MiMo-V2.5 is open weights and GPT-5 mini 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 mini?
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 32.0).
What is the main difference between the MiMo-V2.5 and the GPT-5 mini?
MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 32.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 4.3× cheaper.
Which is better value?
MiMo-V2.5 offers more intelligence per dollar (5.0× 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 you need the strongest overall reasoning and accuracy. Choose the GPT-5 mini 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.