Qwen3.6 27B vs MiMo-V2.5-Pro
Head-to-head specifications
| Metric | Qwen3.6 27B | MiMo-V2.5-Pro | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 30.0 | +6.7% |
| Coding Index | 46.6 | 60.2 | -22.6% |
| Agentic Index | 23.3 | 29.1 | — |
| Context window | 512K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.65 | $0.18 | +261.1% |
| Output speed (tokens/s) | 54 | 55 | -1.8% |
| Access | Open weights | Open weights | — |
- Qwen3.6 27B leads overall capability (Intelligence Index 32.0 vs 30.0).
- 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: Qwen3.6 27B or MiMo-V2.5-Pro?
Qwen3.6 27B advantages
- General intelligence (+6%)
MiMo-V2.5-Pro advantages
- Coding ability (+23%)
- Agentic task performance (+20%)
- Context window (+49%)
- Affordability (+72%)
Which should you choose?
- Choose the Qwen3.6 27B if you need the strongest overall reasoning and accuracy.
- Choose the MiMo-V2.5-Pro if coding and software development are your main workload.
Value for money
MiMo-V2.5-Pro offers more intelligence per dollar (3.4× 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.
Qwen3.6 27B vs MiMo-V2.5-Pro: which should you choose?
Qwen3.6 27B — Alibaba multimodal model with an Intelligence Index of 32, a 512K-token context window and a blended price of $0.65/1M 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).
Qwen3.6 27B vs MiMo-V2.5-Pro: Qwen3.6 27B scores higher on the Intelligence Index. Qwen3.6 27B leads overall capability (Intelligence Index 32.0 vs 30.0). 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 Qwen3.6 27B scores 32.0 versus 30.0. For software development, the Coding Index puts MiMo-V2.5-Pro ahead (60.2 vs 46.6). 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, MiMo-V2.5-Pro generates faster (55 vs 54 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 $0.65 per 1M tokens). Qwen3.6 27B 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 Qwen3.6 27B better than the MiMo-V2.5-Pro?
MiMo-V2.5-Pro takes the overall edge, though Qwen3.6 27B wins in specific areas worth weighing. Qwen3.6 27B leads overall capability (Intelligence Index 32.0 vs 30.0).
What is the main difference between the Qwen3.6 27B and the MiMo-V2.5-Pro?
Qwen3.6 27B leads overall capability (Intelligence Index 32.0 vs 30.0). 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 (3.4× 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 Qwen3.6 27B if you need the strongest overall reasoning and accuracy. Choose the MiMo-V2.5-Pro if coding and software development are your main workload.
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.