MiniMax-M2.7 vs Gemma 4 12B IT vs GPT-5.4 nano
Gemma 4 12B IT comes out ahead, 75 to 63 and 48 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2.7
48/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
- Our pick
GoogleGemma 4 12B IT
75/100- ECI—
- Price$0.075 / $0.275
- Context262K
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Gemma 4 12B IT is our pick
Gemma 4 12B IT is the better all-round choice, scoring 75/100 against GPT-5.4 nano (63) and MiniMax-M2.7 (48). It leads on price. GPT-5.4 nano wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGemma 4 12B ITGemma 4 12B IT $0.125 · 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 · Gemma 4 12B IT 262,144 · MiniMax-M2.7 204,800 tokens
- Widest inputsGemma 4 12B IT and GPT-5.4 nanoMiniMax-M2.7: Text · Gemma 4 12B IT: Text, Images · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.7 and Gemma 4 12B ITPublishes downloadable weights
| Measure | Weight | MiniMax-M2.7 | Gemma 4 12B IT | GPT-5.4 nano |
|---|---|---|---|---|
| Price | 50% | 63 | 93 | 66 |
| Inputs & features | 30% | 35 | 70 | 70 |
| Context window | 20% | 32 | 37 | 44 |
| Overall | 100% | 48/100 | 75/100 | 63/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2.7MiniMax | Gemma 4 12B ITGoogle | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.9 (best) | — | 145.8 |
| ECI rank | #73 of 148 (best) | — | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 78.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 87.8% |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | $0.075 (best) | $0.20 |
| Output | $1.20 | $0.275 (best) | $1.25 |
| Cached input | $0.06 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | $0.125 (best) | $0.463 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Median of 2 providers | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens | 400,000 tokens (best) |
| Max output | 131,072 tokens (best) | 32,768 tokens | 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 | Yes | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.7 | — | gpt-5.4-nano |
| API providers | 29 (best) | 2 | 26 |
| Released | Mar 18, 2026 | Jun 9, 2026 | Mar 17, 2026 |
| Knowledge cutoff | — | — | 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 4 12B IT$1.30
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.7, Gemma 4 12B IT or GPT-5.4 nano?
Gemma 4 12B IT is the better all-round choice, scoring 75/100 against GPT-5.4 nano (63) and MiniMax-M2.7 (48). It leads on price. GPT-5.4 nano wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, MiniMax-M2.7, Gemma 4 12B IT or GPT-5.4 nano?
Gemma 4 12B IT is cheaper at $0.075 input / $0.275 output per million tokens (median across 2 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 $0.125 per million tokens for Gemma 4 12B IT versus $0.463 for GPT-5.4 nano (3.7× as much) and $0.525 for MiniMax-M2.7 (4.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M2.7 has an ECI of 145.9, Gemma 4 12B IT has not been scored yet and GPT-5.4 nano has an ECI of 145.8.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.7, Gemma 4 12B IT and GPT-5.4 nano yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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 262,144 for Gemma 4 12B IT and 204,800 for MiniMax-M2.7. Maximum output per response: MiniMax-M2.7 up to 131,072, Gemma 4 12B IT up to 32,768, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7 accepts text; Gemma 4 12B IT accepts text and images; GPT-5.4 nano accepts text and images. Gemma 4 12B IT handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 and Gemma 4 12B IT publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
Gemma 4 12B IT is the newest, released Jun 9, 2026. MiniMax-M2.7 came out Mar 18, 2026; GPT-5.4 nano came out Mar 17, 2026. Knowledge cutoff: 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.
