MiMo-V2.5-Pro vs Qwen3.5 27B
Head-to-head specifications
| Metric | MiMo-V2.5-Pro | Qwen3.5 27B | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 31.0 | -3.2% |
| Context window | 1M tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $0.18 | $0.42 | -57.1% |
| Output speed (tokens/s) | 55 | 74 | -25.7% |
| Access | Open weights | Open weights | — |
- Qwen3.5 27B leads overall capability (Intelligence Index 31.0 vs 30.0).
- MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 2.3× 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 27B?
MiMo-V2.5-Pro advantages
- Context window (+49%)
- Affordability (+57%)
Qwen3.5 27B advantages
- Output speed (+26%)
Which should you choose?
- Choose the MiMo-V2.5-Pro if you work with long documents or large codebases.
- Choose the Qwen3.5 27B 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 (2.3× 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 27B: 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 27B — Alibaba multimodal model with an Intelligence Index of 31, a 512K-token context window and a blended price of $0.42/1M tokens (open weights).
MiMo-V2.5-Pro vs Qwen3.5 27B: Qwen3.5 27B scores higher on the Intelligence Index. Qwen3.5 27B leads overall capability (Intelligence Index 31.0 vs 30.0). MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 2.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.5 27B scores 31.0 versus 30.0. 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, Qwen3.5 27B generates faster (74 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 $0.42 per 1M tokens). MiMo-V2.5-Pro is open weights and Qwen3.5 27B 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 27B?
MiMo-V2.5-Pro takes the overall edge, though Qwen3.5 27B wins in specific areas worth weighing. Qwen3.5 27B leads overall capability (Intelligence Index 31.0 vs 30.0).
What is the main difference between the MiMo-V2.5-Pro and the Qwen3.5 27B?
Qwen3.5 27B leads overall capability (Intelligence Index 31.0 vs 30.0). MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 2.3× cheaper.
Which is better value?
MiMo-V2.5-Pro offers more intelligence per dollar (2.3× 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 Qwen3.5 27B 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.