Gemini 2.5 Pro (May) vs MiniMax-M2.7
Head-to-head specifications
| Metric | Gemini 2.5 Pro (May) | MiniMax-M2.7 | Difference |
|---|---|---|---|
| Intelligence Index | 27.0 | 38.0 | -28.9% |
| Context window | 1M tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.86 | $0.22 | +290.9% |
| Access | Proprietary API | Open weights | — |
- MiniMax-M2.7 leads overall capability (Intelligence Index 38.0 vs 27.0).
- MiniMax-M2.7 is the cheaper model to run at $0.22/1M blended tokens — about 3.9× cheaper.
- Gemini 2.5 Pro (May) offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Gemini 2.5 Pro (May) or MiniMax-M2.7?
Gemini 2.5 Pro (May) advantages
- Context window (+74%)
MiniMax-M2.7 advantages
- General intelligence (+29%)
- Affordability (+74%)
Which should you choose?
- Choose the Gemini 2.5 Pro (May) if you work with long documents or large codebases.
- Choose the MiniMax-M2.7 if you need the strongest overall reasoning and accuracy.
Value for money
MiniMax-M2.7 offers more intelligence per dollar (5.5× 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.
Gemini 2.5 Pro (May) vs MiniMax-M2.7: which should you choose?
Gemini 2.5 Pro (May) — Google multimodal model with an Intelligence Index of 27, a 1M-token context window and a blended price of $0.86/1M tokens.
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).
Gemini 2.5 Pro (May) vs MiniMax-M2.7: MiniMax-M2.7 scores higher on the Intelligence Index. MiniMax-M2.7 leads overall capability (Intelligence Index 38.0 vs 27.0). MiniMax-M2.7 is the cheaper model to run at $0.22/1M blended tokens — about 3.9× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiniMax-M2.7 scores 38.0 versus 27.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemini 2.5 Pro (May) accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once.
Pricing and access
At blended per-token rates, MiniMax-M2.7 is the cheaper model to run ($0.22 vs $0.86 per 1M tokens). Gemini 2.5 Pro (May) is proprietary api and MiniMax-M2.7 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 Gemini 2.5 Pro (May) better than the MiniMax-M2.7?
MiniMax-M2.7 takes the overall edge, though Gemini 2.5 Pro (May) wins in specific areas worth weighing. MiniMax-M2.7 leads overall capability (Intelligence Index 38.0 vs 27.0).
What is the main difference between the Gemini 2.5 Pro (May) and the MiniMax-M2.7?
MiniMax-M2.7 leads overall capability (Intelligence Index 38.0 vs 27.0). MiniMax-M2.7 is the cheaper model to run at $0.22/1M blended tokens — about 3.9× cheaper.
Which is better value?
MiniMax-M2.7 offers more intelligence per dollar (5.5× 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 Gemini 2.5 Pro (May) if you work with long documents or large codebases. Choose the MiniMax-M2.7 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.