MiMo-V2.5 vs GPT-5.6 Sol
Head-to-head specifications
| Metric | MiMo-V2.5 | GPT-5.6 Sol | Difference |
|---|---|---|---|
| Intelligence Index | 37.0 | 59.0 | -37.3% |
| Coding Index | 56.8 | 77.4 | -26.6% |
| Agentic Index | 23.7 | 54.0 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.06 | $1.54 | -96.1% |
| Output speed (tokens/s) | 83 | 57 | +45.6% |
| Access | Open weights | Proprietary API | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 37.0).
- MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 25.7× cheaper.
Verdict: MiMo-V2.5 or GPT-5.6 Sol?
MiMo-V2.5 advantages
- Affordability (+96%)
- Output speed (+31%)
GPT-5.6 Sol advantages
- General intelligence (+37%)
- Coding ability (+27%)
- Agentic task performance (+56%)
Which should you choose?
- Choose the MiMo-V2.5 if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
- Choose the MiMo-V2.5 if low latency and fast generation matter for your application.
Value for money
MiMo-V2.5 offers more intelligence per dollar (16.1× 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 vs GPT-5.6 Sol: which should you choose?
MiMo-V2.5 — Xiaomi multimodal model with an Intelligence Index of 37, a 1M-token context window and a blended price of $0.06/1M tokens (open weights).
GPT-5.6 Sol — OpenAI multimodal model with an Intelligence Index of 59, a 1M-token context window and a blended price of $1.54/1M tokens.
MiMo-V2.5 vs GPT-5.6 Sol: GPT-5.6 Sol scores higher on the Intelligence Index. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 37.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 25.7× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 37.0. For software development, the Coding Index puts GPT-5.6 Sol ahead (77.4 vs 56.8). On agentic, multi-step tool-use tasks, GPT-5.6 Sol measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The MiMo-V2.5 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 generates faster (83 vs 57 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiMo-V2.5 is the cheaper model to run ($0.06 vs $1.54 per 1M tokens). MiMo-V2.5 is open weights and GPT-5.6 Sol is proprietary api. 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 better than the GPT-5.6 Sol?
GPT-5.6 Sol takes the overall edge, though MiMo-V2.5 wins in specific areas worth weighing. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 37.0).
What is the main difference between the MiMo-V2.5 and the GPT-5.6 Sol?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 37.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 25.7× cheaper.
Which is better value?
MiMo-V2.5 offers more intelligence per dollar (16.1× 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 if you want the lowest cost per token at scale. Choose the GPT-5.6 Sol 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.