MiMo-V2.5-Pro vs Qwen3 Max Thinking (Preview)
Head-to-head specifications
| Metric | MiMo-V2.5-Pro | Qwen3 Max Thinking (Preview) | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 28.0 | +7.1% |
| Context window | 1M tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $0.18 | $0.90 | -80.0% |
| Output speed (tokens/s) | 55 | 55 | — |
| Access | Open weights | Open weights | — |
- MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 28.0).
- MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 5.0× 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 Max Thinking (Preview)?
MiMo-V2.5-Pro advantages
- General intelligence (+7%)
- Context window (+49%)
- Affordability (+80%)
Qwen3 Max Thinking (Preview) advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the MiMo-V2.5-Pro if you need the strongest overall reasoning and accuracy.
- Choose the MiMo-V2.5-Pro if you work with long documents or large codebases.
Value for money
MiMo-V2.5-Pro offers more intelligence per dollar (5.4× 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 Max Thinking (Preview): 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 Max Thinking (Preview) — Alibaba text model with an Intelligence Index of 28, a 512K-token context window and a blended price of $0.9/1M tokens (open weights).
MiMo-V2.5-Pro vs Qwen3 Max Thinking (Preview): MiMo-V2.5-Pro scores higher on the Intelligence Index. MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 28.0). MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 5.0× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiMo-V2.5-Pro scores 30.0 versus 28.0. 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, MiMo-V2.5-Pro generates faster (55 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 $0.90 per 1M tokens). MiMo-V2.5-Pro is open weights and Qwen3 Max Thinking (Preview) 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 Max Thinking (Preview)?
MiMo-V2.5-Pro is the clearly stronger overall choice, winning most of the dimensions that matter. MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 28.0).
What is the main difference between the MiMo-V2.5-Pro and the Qwen3 Max Thinking (Preview)?
MiMo-V2.5-Pro leads overall capability (Intelligence Index 30.0 vs 28.0). MiMo-V2.5-Pro is the cheaper model to run at $0.18/1M blended tokens — about 5.0× cheaper.
Which is better value?
MiMo-V2.5-Pro offers more intelligence per dollar (5.4× 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 MiMo-V2.5-Pro if you work with long documents or large codebases.
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.