MiniMax-M2.7 vs Gemini 2.5 Pro vs GPT-5.4 nano
GPT-5.4 nano comes out ahead, 68 to 63 and 61 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
GoogleGemini 2.5 Pro
63/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
- Our pick
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
GPT-5.4 nano is our pick
GPT-5.4 nano is the better all-round choice, scoring 68/100 against Gemini 2.5 Pro (63) and MiniMax-M2.7 (61). It leads on price. Gemini 2.5 Pro wins on inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMiniMax-M2.7Capabilities Index (ECI): MiniMax-M2.7 145.9 · GPT-5.4 nano 145.8 · Gemini 2.5 Pro 145.3
- Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 · Gemini 2.5 Pro $3.44 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · GPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 tokens
- Widest inputsGemini 2.5 ProMiniMax-M2.7: Text · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.7Publishes downloadable weights
| Measure | Weight | MiniMax-M2.7 | Gemini 2.5 Pro | GPT-5.4 nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 72 | 73 |
| Price | 25% | 63 | 24 | 66 |
| Inputs & features | 15% | 35 | 100 | 70 |
| Context window | 10% | 32 | 61 | 44 |
| Overall | 100% | 61/100 | 63/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2.7MiniMax | Gemini 2.5 ProGoogle | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.9 (best) | 145.3 | 145.8 |
| ECI rank | #73 of 148 (best) | #78 of 148 | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | 85.3% (best) | 78.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 24.6% | 44.9% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 84.7% | 87.8% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | 57.6% | — |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | $1.25 | $0.20 (best) |
| Output | $1.20 (best) | $10.00 | $1.25 |
| Cached input | $0.06 | $0.125 | $0.02 (best) |
| Blended (3:1) | $0.525 | $3.44 | $0.463 (best) |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | Official MiniMax (minimax.io) API | Official Google API | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 1,048,576 tokens (best) | 400,000 tokens |
| Max output | 131,072 tokens (best) | 65,536 tokens | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | MiniMax-M2.7 | gemini-2.5-pro | gpt-5.4-nano |
| API providers | 29 (best) | 22 | 26 |
| Released | Mar 18, 2026 | Jun 17, 2025 | Mar 17, 2026 |
| Knowledge cutoff | — | Jan 2025 | Aug 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniMax-M2.7$5.40
- Gemini 2.5 Pro$32.50
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.7, Gemini 2.5 Pro or GPT-5.4 nano?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against Gemini 2.5 Pro (63) and MiniMax-M2.7 (61). It leads on price. Gemini 2.5 Pro wins on inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, MiniMax-M2.7, Gemini 2.5 Pro or GPT-5.4 nano?
GPT-5.4 nano is cheaper at $0.20 input / $1.25 output per million tokens (official OpenAI API price). MiniMax-M2.7 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price); Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.463 per million tokens for GPT-5.4 nano versus $0.525 for MiniMax-M2.7 (1.1× as much) and $3.44 for Gemini 2.5 Pro (7.4× as much).
Which scores higher on benchmarks?
MiniMax-M2.7 scores higher on the Capabilities Index (ECI): MiniMax-M2.7 145.9 (#73 of 148), GPT-5.4 nano 145.8 (#75 of 148) and Gemini 2.5 Pro 145.3 (#78 of 148). The confidence ranges of the top two overlap (138.2–148.0 vs 143.2–147.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.7 and GPT-5.4 nano yet, so there is no like-for-like coding score. On overall capability, MiniMax-M2.7 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.5 Pro has the largest context window at 1,048,576 tokens, against 400,000 for GPT-5.4 nano and 204,800 for MiniMax-M2.7. Maximum output per response: MiniMax-M2.7 up to 131,072, Gemini 2.5 Pro up to 65,536, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7 accepts text; Gemini 2.5 Pro accepts text, images, PDFs, audio and video; GPT-5.4 nano accepts text and images. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 publishes its weights and can be self-hosted; Gemini 2.5 Pro and GPT-5.4 nano is proprietary.
Which is newer?
MiniMax-M2.7 is the newest, released Mar 18, 2026. GPT-5.4 nano came out Mar 17, 2026; Gemini 2.5 Pro came out Jun 17, 2025. Knowledge cutoff: Gemini 2.5 Pro Jan 2025, GPT-5.4 nano Aug 31, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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