MiniMax-M2.7 vs Gemma 3 27B IT vs GPT-5.4 nano
GPT-5.4 nano comes out ahead, 68 to 61 and 60 on our weighted score, though Gemma 3 27B IT is 4.2× cheaper per token.
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
GoogleGemma 3 27B IT
60/100- ECI130.0
- Price$0.08 / $0.20
- Context131K
- 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 MiniMax-M2.7 (61) and Gemma 3 27B IT (60). It leads on inputs & features and context window. Gemma 3 27B IT wins on price. 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 · Gemma 3 27B IT 130.0
- Lowest priceGemma 3 27B ITGemma 3 27B IT $0.11 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 · Gemma 3 27B IT 131,072 tokens
- Widest inputsGemma 3 27B IT and GPT-5.4 nanoMiniMax-M2.7: Text · Gemma 3 27B IT: Text, Images · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.7 and Gemma 3 27B ITPublishes downloadable weights
| Measure | Weight | MiniMax-M2.7 | Gemma 3 27B IT | GPT-5.4 nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 53 | 73 |
| Price | 25% | 63 | 95 | 66 |
| Inputs & features | 15% | 35 | 50 | 70 |
| Context window | 10% | 32 | 24 | 44 |
| Overall | 100% | 61/100 | 60/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2.7MiniMax | Gemma 3 27B ITGoogle | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.9 (best) | 130.0 | 145.8 |
| ECI rank | #73 of 148 (best) | #125 of 148 | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | 47.7% | 78.5% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 22.5% | 87.8% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | $0.08 (best) | $0.20 |
| Output | $1.20 | $0.20 (best) | $1.25 |
| Cached input | $0.06 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | $0.11 (best) | $0.463 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Median of 9 providers | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 131,072 tokens | 400,000 tokens (best) |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.7 | — | gpt-5.4-nano |
| API providers | 29 (best) | 10 | 26 |
| Released | Mar 18, 2026 | Mar 12, 2025 | Mar 17, 2026 |
| Knowledge cutoff | — | Aug 2024 | 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
- Gemma 3 27B IT$1.20
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.7, Gemma 3 27B IT or GPT-5.4 nano?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against MiniMax-M2.7 (61) and Gemma 3 27B IT (60). It leads on inputs & features and context window. Gemma 3 27B IT wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, MiniMax-M2.7, Gemma 3 27B IT or GPT-5.4 nano?
Gemma 3 27B IT is cheaper at $0.08 input / $0.20 output per million tokens (median across 9 API providers). GPT-5.4 nano costs $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). At a typical mix of three input tokens to one output token, that is $0.11 per million tokens for Gemma 3 27B IT versus $0.463 for GPT-5.4 nano (4.2× as much) and $0.525 for MiniMax-M2.7 (4.8× 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 Gemma 3 27B IT 130.0 (#125 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, Gemma 3 27B IT 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?
GPT-5.4 nano has the largest context window at 400,000 tokens, against 204,800 for MiniMax-M2.7 and 131,072 for Gemma 3 27B IT. Maximum output per response: MiniMax-M2.7 up to 131,072, Gemma 3 27B IT up to 131,072, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7 accepts text; Gemma 3 27B IT accepts text and images; GPT-5.4 nano accepts text and images. Gemma 3 27B IT handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 and Gemma 3 27B IT publishes its weights and can be self-hosted; 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; Gemma 3 27B IT came out Mar 12, 2025. Knowledge cutoff: Gemma 3 27B IT Aug 2024, 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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