MiniMax-M2.5 vs GPT-5.1 Codex mini
Head-to-head specifications
| Metric | MiniMax-M2.5 | GPT-5.1 Codex mini | Difference |
|---|---|---|---|
| Intelligence Index | 34.0 | 32.0 | +6.3% |
| Context window | 262K tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.22 | $0.37 | -40.5% |
| Output speed (tokens/s) | 84 | 205 | -59.0% |
| Access | Open weights | Proprietary API | — |
- MiniMax-M2.5 leads overall capability (Intelligence Index 34.0 vs 32.0).
- MiniMax-M2.5 is the cheaper model to run at $0.22/1M blended tokens — about 1.7× cheaper.
- GPT-5.1 Codex mini offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: MiniMax-M2.5 or GPT-5.1 Codex mini?
MiniMax-M2.5 advantages
- General intelligence (+6%)
- Affordability (+41%)
GPT-5.1 Codex mini advantages
- Context window (+72%)
- Output speed (+59%)
Which should you choose?
- Choose the MiniMax-M2.5 if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5.1 Codex mini if you work with long documents or large codebases.
- Choose the MiniMax-M2.5 if you want the lowest cost per token at scale.
Value for money
MiniMax-M2.5 offers more intelligence per dollar (1.8× 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.
MiniMax-M2.5 vs GPT-5.1 Codex mini: which should you choose?
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 Codex mini — OpenAI multimodal model with an Intelligence Index of 32, a 922K-token context window and a blended price of $0.37/1M tokens.
MiniMax-M2.5 vs GPT-5.1 Codex mini: MiniMax-M2.5 scores higher on the Intelligence Index. MiniMax-M2.5 leads overall capability (Intelligence Index 34.0 vs 32.0). MiniMax-M2.5 is the cheaper model to run at $0.22/1M blended tokens — about 1.7× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiniMax-M2.5 scores 34.0 versus 32.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.1 Codex mini accepts up to 922K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.1 Codex mini generates faster (205 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 $0.37 per 1M tokens). MiniMax-M2.5 is open weights and GPT-5.1 Codex mini 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 MiniMax-M2.5 better than the GPT-5.1 Codex mini?
MiniMax-M2.5 takes the overall edge, though GPT-5.1 Codex mini wins in specific areas worth weighing. MiniMax-M2.5 leads overall capability (Intelligence Index 34.0 vs 32.0).
What is the main difference between the MiniMax-M2.5 and the GPT-5.1 Codex mini?
MiniMax-M2.5 leads overall capability (Intelligence Index 34.0 vs 32.0). MiniMax-M2.5 is the cheaper model to run at $0.22/1M blended tokens — about 1.7× cheaper.
Which is better value?
MiniMax-M2.5 offers more intelligence per dollar (1.8× 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 MiniMax-M2.5 if you need the strongest overall reasoning and accuracy. Choose the GPT-5.1 Codex mini 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.