GPT-5.1 vs MiniMax-M2.5
Head-to-head specifications
| Metric | GPT-5.1 | MiniMax-M2.5 | Difference |
|---|---|---|---|
| Intelligence Index | 37.0 | 34.0 | +8.8% |
| Context window | 512K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.77 | $0.22 | +250.0% |
| Output speed (tokens/s) | 106 | 84 | +26.2% |
| Access | Proprietary API | Open weights | — |
- GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 34.0).
- MiniMax-M2.5 is the cheaper model to run at $0.22/1M blended tokens — about 3.5× cheaper.
- GPT-5.1 offers the larger context window (512K tokens), useful for long documents and codebases.
Verdict: GPT-5.1 or MiniMax-M2.5?
GPT-5.1 advantages
- General intelligence (+8%)
- Context window (+49%)
- Output speed (+21%)
MiniMax-M2.5 advantages
- Affordability (+71%)
Which should you choose?
- Choose the GPT-5.1 if you need the strongest overall reasoning and accuracy.
- Choose the MiniMax-M2.5 if you want the lowest cost per token at scale.
- Choose the GPT-5.1 if you work with long documents or large codebases.
Value for money
MiniMax-M2.5 offers more intelligence per dollar (3.2× 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-5.1 vs MiniMax-M2.5: which should you choose?
GPT-5.1 — OpenAI multimodal model with an Intelligence Index of 37, a 512K-token context window and a blended price of $0.77/1M tokens.
MiniMax-M2.5 — MiniMax multimodal model with an Intelligence Index of 34, a 262K-token context window and a blended price of $0.22/1M tokens (open weights).
GPT-5.1 vs MiniMax-M2.5: GPT-5.1 scores higher on the Intelligence Index. GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 34.0). MiniMax-M2.5 is the cheaper model to run at $0.22/1M blended tokens — about 3.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.1 scores 37.0 versus 34.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.1 accepts up to 512K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.1 generates faster (106 vs 84 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiniMax-M2.5 is the cheaper model to run ($0.22 vs $0.77 per 1M tokens). GPT-5.1 is proprietary api and MiniMax-M2.5 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-5.1 better than the MiniMax-M2.5?
GPT-5.1 takes the overall edge, though MiniMax-M2.5 wins in specific areas worth weighing. GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 34.0).
What is the main difference between the GPT-5.1 and the MiniMax-M2.5?
GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 34.0). MiniMax-M2.5 is the cheaper model to run at $0.22/1M blended tokens — about 3.5× cheaper.
Which is better value?
MiniMax-M2.5 offers more intelligence per dollar (3.2× 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-5.1 if you need the strongest overall reasoning and accuracy. Choose the MiniMax-M2.5 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.