GPT-5.6 Sol vs MiniMax-M2.1
Head-to-head specifications
| Metric | GPT-5.6 Sol | MiniMax-M2.1 | Difference |
|---|---|---|---|
| Intelligence Index | 59.0 | 33.0 | +78.8% |
| Context window | 1M tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $1.54 | $0.36 | +327.8% |
| Output speed (tokens/s) | 57 | 79 | -27.8% |
| Access | Proprietary API | Open weights | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 33.0).
- MiniMax-M2.1 is the cheaper model to run at $0.36/1M blended tokens — about 4.3× cheaper.
- GPT-5.6 Sol offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.6 Sol or MiniMax-M2.1?
GPT-5.6 Sol advantages
- General intelligence (+44%)
- Context window (+74%)
MiniMax-M2.1 advantages
- Affordability (+77%)
- Output speed (+28%)
Which should you choose?
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
- Choose the MiniMax-M2.1 if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if you work with long documents or large codebases.
Value for money
MiniMax-M2.1 offers more intelligence per dollar (2.4× 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.
GPT-5.6 Sol vs MiniMax-M2.1: which should you choose?
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.
MiniMax-M2.1 — MiniMax multimodal model with an Intelligence Index of 33, a 262K-token context window and a blended price of $0.36/1M tokens (open weights).
GPT-5.6 Sol vs MiniMax-M2.1: GPT-5.6 Sol scores higher on the Intelligence Index. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 33.0). MiniMax-M2.1 is the cheaper model to run at $0.36/1M blended tokens — about 4.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 33.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.6 Sol 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, MiniMax-M2.1 generates faster (79 vs 57 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiniMax-M2.1 is the cheaper model to run ($0.36 vs $1.54 per 1M tokens). GPT-5.6 Sol is proprietary api and MiniMax-M2.1 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 GPT-5.6 Sol better than the MiniMax-M2.1?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 33.0).
What is the main difference between the GPT-5.6 Sol and the MiniMax-M2.1?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 33.0). MiniMax-M2.1 is the cheaper model to run at $0.36/1M blended tokens — about 4.3× cheaper.
Which is better value?
MiniMax-M2.1 offers more intelligence per dollar (2.4× 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 GPT-5.6 Sol if you need the strongest overall reasoning and accuracy. Choose the MiniMax-M2.1 if you want the lowest cost per token at scale.
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.