Gemma 4 12B IT vs MiniMax-M3 vs Qwen3.7 Plus
Gemma 4 12B IT comes out ahead, 75 to 65 and 65 on our weighted score, and it is the cheaper option too.
- Our pick
GoogleGemma 4 12B IT
75/100- ECI—
- Price$0.075 / $0.275
- Context262K
MiniMaxMiniMax-M3
65/100- ECI147.0
- Price$0.30 / $1.20
- Context1.05M
Alibaba (Qwen)Qwen3.7 Plus
65/100- ECI147.4
- Price$0.40 / $1.60
- Context1M
Gemma 4 12B IT is our pick
Gemma 4 12B IT is the better all-round choice, scoring 75/100 against MiniMax-M3 (65) and Qwen3.7 Plus (65). It leads on price. Qwen3.7 Plus wins on inputs & features. 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 · MiniMax-M3 $0.525 · Qwen3.7 Plus $0.70 per 1M tokens (3:1 blend)
- Longest contextMiniMax-M3MiniMax-M3 1,048,576 · Qwen3.7 Plus 1,000,000 · Gemma 4 12B IT 262,144 tokens
- Widest inputsMiniMax-M3 and Qwen3.7 PlusGemma 4 12B IT: Text, Images · MiniMax-M3: Text, Images, Video · Qwen3.7 Plus: Text, Images, Video
- Self-hostingGemma 4 12B IT and MiniMax-M3Publishes downloadable weights
| Measure | Weight | Gemma 4 12B IT | MiniMax-M3 | Qwen3.7 Plus |
|---|---|---|---|---|
| Price | 50% | 93 | 63 | 57 |
| Inputs & features | 30% | 70 | 70 | 80 |
| Context window | 20% | 37 | 61 | 60 |
| Overall | 100% | 75/100 | 65/100 | 65/100 |
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 | Gemma 4 12B ITGoogle | MiniMax-M3MiniMax | Qwen3.7 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 147.0 | 147.4 (best) |
| ECI rank | — | #62 of 148 | #61 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 90.9% (best) | 87.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 34.4% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 71.1% | 93.3% (best) |
| Price per million tokens | |||
| Input | $0.075 (best) | $0.30 | $0.40 |
| Output | $0.275 (best) | $1.20 | $1.60 |
| Cached input | — | $0.06 | $0.04 (best) |
| Blended (3:1) | $0.125 (best) | $0.525 | $0.70 |
| Long-context rate | Same rate | Over 512K: $0.60 / $2.40 | Over 256K: $1.20 / $4.80 |
| Price source | Median of 2 providers | Official MiniMax (minimax.io) API | Official Alibaba API |
| Limits | |||
| Context window | 262,144 tokens | 1,048,576 tokens (best) | 1,000,000 tokens |
| Max output | 32,768 tokens | 512,000 tokens (best) | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | MiniMax-M3 | qwen3.7-plus |
| API providers | 2 | 42 (best) | 25 |
| Released | Jun 9, 2026 | Jun 1, 2026 | Jun 2, 2026 |
| Knowledge cutoff | — | — | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemma 4 12B IT$1.30
- MiniMax-M3$5.40
- Qwen3.7 Plus$7.20
Which should you choose?
Which is better: Gemma 4 12B IT, MiniMax-M3 or Qwen3.7 Plus?
Gemma 4 12B IT is the better all-round choice, scoring 75/100 against MiniMax-M3 (65) and Qwen3.7 Plus (65). It leads on price. Qwen3.7 Plus wins on inputs & features. 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, Gemma 4 12B IT, MiniMax-M3 or Qwen3.7 Plus?
Gemma 4 12B IT is cheaper at $0.075 input / $0.275 output per million tokens (median across 2 API providers). MiniMax-M3 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price); Qwen3.7 Plus costs $0.40 input / $1.60 output per million tokens (official Alibaba 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 $0.525 for MiniMax-M3 (4.2× as much) and $0.70 for Qwen3.7 Plus (5.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemma 4 12B IT has not been scored yet, MiniMax-M3 has an ECI of 147.0 and Qwen3.7 Plus has an ECI of 147.4.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 4 12B IT, MiniMax-M3 and Qwen3.7 Plus 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?
MiniMax-M3 has the largest context window at 1,048,576 tokens, against 1,000,000 for Qwen3.7 Plus and 262,144 for Gemma 4 12B IT. Maximum output per response: Gemma 4 12B IT up to 32,768, MiniMax-M3 up to 512,000, Qwen3.7 Plus up to 64,000 tokens.
Which can read images, PDFs, audio or video?
Gemma 4 12B IT accepts text and images; MiniMax-M3 accepts text, images and video; Qwen3.7 Plus accepts text, images and video. MiniMax-M3 handles the widest range of inputs.
Are any of these open source?
Gemma 4 12B IT and MiniMax-M3 publishes its weights and can be self-hosted; Qwen3.7 Plus is proprietary.
Which is newer?
Gemma 4 12B IT is the newest, released Jun 9, 2026. Qwen3.7 Plus came out Jun 2, 2026; MiniMax-M3 came out Jun 1, 2026. Knowledge cutoff: Qwen3.7 Plus Apr 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.
