MiMo-V2.5-Pro vs Qwen3.5 9B
Head-to-head specifications
| Metric | MiMo-V2.5-Pro | Qwen3.5 9B | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 25.0 | +20.0% |
| Coding Index | 60.2 | 23.5 | +156.2% |
| Agentic Index | 29.1 | 7.4 | — |
| Context window | 1M tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $0.18 | $0.11 | +63.6% |
| Output speed (tokens/s) | 55 | 70 | -21.4% |
| Access | Open weights | Open weights | — |
- MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 25.0).
- Qwen3.5 9B is the cheaper model to run at $0.11/1M blended tokens — about 1.6× cheaper.
- MiMo-V2.5-Pro offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: MiMo-V2.5-Pro or Qwen3.5 9B?
MiMo-V2.5-Pro advantages
- General intelligence (+17%)
- Coding ability (+61%)
- Agentic task performance (+75%)
- Context window (+49%)
Qwen3.5 9B advantages
- Affordability (+39%)
- Output speed (+21%)
Which should you choose?
- Choose the MiMo-V2.5-Pro if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3.5 9B if you want the lowest cost per token at scale.
- Choose the MiMo-V2.5-Pro if coding and software development are your main workload.
Value for money
Qwen3.5 9B offers more intelligence per dollar (1.4× 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 Qwen3.5 9B: 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).
Qwen3.5 9B — Alibaba text model with an Intelligence Index of 25, a 512K-token context window and a blended price of $0.11/1M tokens (open weights).
MiMo-V2.5-Pro vs Qwen3.5 9B: MiMo-V2.5-Pro scores higher on the Intelligence Index. MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 25.0). Qwen3.5 9B is the cheaper model to run at $0.11/1M blended tokens — about 1.6× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiMo-V2.5-Pro scores 30.0 versus 25.0. For software development, the Coding Index puts MiMo-V2.5-Pro ahead (60.2 vs 23.5). On agentic, multi-step tool-use tasks, MiMo-V2.5-Pro measures stronger. 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, Qwen3.5 9B generates faster (70 vs 55 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.5 9B is the cheaper model to run ($0.11 vs $0.18 per 1M tokens). MiMo-V2.5-Pro is open weights and Qwen3.5 9B is open weights. 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 Qwen3.5 9B?
MiMo-V2.5-Pro takes the overall edge, though Qwen3.5 9B wins in specific areas worth weighing. MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 25.0).
What is the main difference between the MiMo-V2.5-Pro and the Qwen3.5 9B?
MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 25.0). Qwen3.5 9B is the cheaper model to run at $0.11/1M blended tokens — about 1.6× cheaper.
Which is better value?
Qwen3.5 9B offers more intelligence per dollar (1.4× 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 need the strongest overall reasoning and accuracy. Choose the Qwen3.5 9B if you want the lowest cost per token at scale.
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.