MiMo-V2.5-Pro vs o4-mini
Head-to-head specifications
| Metric | MiMo-V2.5-Pro | o4-mini | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 29.0 | +3.4% |
| Context window | 1M tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.18 | $0.64 | -71.9% |
| Output speed (tokens/s) | 55 | 167 | -67.1% |
| Access | Open weights | Proprietary API | — |
- MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 29.0).
- MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 3.6× cheaper.
- MiMo-V2.5-Pro offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: MiMo-V2.5-Pro or o4-mini?
MiMo-V2.5-Pro advantages
- Context window (+74%)
- Affordability (+72%)
o4-mini advantages
- Output speed (+67%)
Which should you choose?
- Choose the MiMo-V2.5-Pro if you work with long documents or large codebases.
- Choose the o4-mini if low latency and fast generation matter for your application.
- Choose the MiMo-V2.5-Pro if you want the lowest cost per token at scale.
Value for money
MiMo-V2.5-Pro offers more intelligence per dollar (3.7× 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-Pro vs o4-mini: which should you choose?
MiMo-V2.5-Pro — Xiaomi multimodal model with an Intelligence Index of 30, a 1M-token context window and a blended price of $0.18/1M tokens (open weights).
o4-mini — OpenAI multimodal model with an Intelligence Index of 29, a 256K-token context window and a blended price of $0.64/1M tokens.
MiMo-V2.5-Pro vs o4-mini: MiMo-V2.5-Pro scores higher on the Intelligence Index. MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 29.0). MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 3.6× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiMo-V2.5-Pro scores 30.0 versus 29.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The MiMo-V2.5-Pro 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, o4-mini generates faster (167 vs 55 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiMo-V2.5-Pro is the cheaper model to run ($0.18 vs $0.64 per 1M tokens). MiMo-V2.5-Pro is open weights and o4-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-Pro better than the o4-mini?
MiMo-V2.5-Pro takes the overall edge, though o4-mini wins in specific areas worth weighing. MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 29.0).
What is the main difference between the MiMo-V2.5-Pro and the o4-mini?
MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 29.0). MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 3.6× cheaper.
Which is better value?
MiMo-V2.5-Pro offers more intelligence per dollar (3.7× 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-Pro if you work with long documents or large codebases. Choose the o4-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.