MiniMax-M2.7 vs MiniMax-M3
Head-to-head specifications
| Metric | MiniMax-M2.7 | MiniMax-M3 | Difference |
|---|---|---|---|
| Intelligence Index | 38.0 | 44.0 | -13.6% |
| Coding Index | 52.6 | 58.6 | -10.2% |
| Agentic Index | 25.6 | 35.4 | — |
| Context window | 262K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.22 | $0.22 | — |
| Output speed (tokens/s) | 59 | 83 | -28.9% |
| Access | Open weights | Open weights | — |
- MiniMax-M3 leads overall capability (Intelligence Index 44.0 vs 38.0).
- Both cost about the same to run (~$0.22/1M blended tokens), so capability and speed should decide.
- MiniMax-M3 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: MiniMax-M2.7 or MiniMax-M3?
MiniMax-M2.7 advantages
- No decisive advantage on the tracked metrics.
MiniMax-M3 advantages
- General intelligence (+14%)
- Coding ability (+10%)
- Agentic task performance (+28%)
- Context window (+74%)
- Output speed (+29%)
Which should you choose?
- Choose the MiniMax-M3 if you need the strongest overall reasoning and accuracy.
Value for money
MiniMax-M3 offers more intelligence per dollar (1.2× 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.
MiniMax-M2.7 vs MiniMax-M3: which should you choose?
MiniMax-M2.7 — MiniMax multimodal model with an Intelligence Index of 38, a 262K-token context window and a blended price of $0.22/1M tokens (open weights).
MiniMax-M3 — MiniMax multimodal model with an Intelligence Index of 44, a 1M-token context window and a blended price of $0.22/1M tokens (open weights).
MiniMax-M2.7 vs MiniMax-M3: MiniMax-M3 scores higher on the Intelligence Index. MiniMax-M3 leads overall capability (Intelligence Index 44.0 vs 38.0). Both cost about the same to run (~$0.22/1M blended tokens), so capability and speed should decide.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiniMax-M3 scores 44.0 versus 38.0. For software development, the Coding Index puts MiniMax-M3 ahead (58.6 vs 52.6). On agentic, multi-step tool-use tasks, MiniMax-M3 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The MiniMax-M3 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-M3 generates faster (83 vs 59 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiniMax-M2.7 is the cheaper model to run ($0.22 vs $0.22 per 1M tokens). MiniMax-M2.7 is open weights and MiniMax-M3 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 MiniMax-M2.7 better than the MiniMax-M3?
MiniMax-M3 is the clearly stronger overall choice, winning most of the dimensions that matter. MiniMax-M3 leads overall capability (Intelligence Index 44.0 vs 38.0).
What is the main difference between the MiniMax-M2.7 and the MiniMax-M3?
MiniMax-M3 leads overall capability (Intelligence Index 44.0 vs 38.0). Both cost about the same to run (~$0.22/1M blended tokens), so capability and speed should decide.
Which is better value?
MiniMax-M3 offers more intelligence per dollar (1.2× 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 MiniMax-M3 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.