Qwen3.5 9B vs MiMo-V2.5-Pro
Head-to-head specifications
| Metric | Qwen3.5 9B | MiMo-V2.5-Pro | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 30.0 | -16.7% |
| Coding Index | 23.5 | 60.2 | -61.0% |
| Agentic Index | 7.4 | 29.1 | — |
| Context window | 512K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.11 | $0.18 | -38.9% |
| Output speed (tokens/s) | 70 | 55 | +27.3% |
| 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: Qwen3.5 9B or MiMo-V2.5-Pro?
Qwen3.5 9B advantages
- Affordability (+39%)
- Output speed (+21%)
MiMo-V2.5-Pro advantages
- General intelligence (+17%)
- Coding ability (+61%)
- Agentic task performance (+75%)
- Context window (+49%)
Which should you choose?
- Choose the Qwen3.5 9B if you want the lowest cost per token at scale.
- Choose the MiMo-V2.5-Pro if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3.5 9B if low latency and fast generation matter for your application.
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.
Qwen3.5 9B vs MiMo-V2.5-Pro: which should you choose?
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 — 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 vs MiMo-V2.5-Pro: 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). Qwen3.5 9B is open weights and MiMo-V2.5-Pro 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 Qwen3.5 9B better than the MiMo-V2.5-Pro?
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 Qwen3.5 9B and the MiMo-V2.5-Pro?
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 Qwen3.5 9B if you want the lowest cost per token at scale. Choose the MiMo-V2.5-Pro if you need the strongest overall reasoning and accuracy.
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.