MiniMax-M3 vs Nemotron 3.5 Super 120B A12B vs Qwen3.7 Plus
Too close to call on our weighted score (MiniMax-M3 63, Qwen3.7 Plus 63, Nemotron 3.5 Super 120B A12B 59). The right pick depends on what you value most.
MiniMaxMiniMax-M3
63/100- ECI146.9
- Price$0.30 / $1.20
- Context1.05M
NVIDIANemotron 3.5 Super 120B A12B
59/100- ECI—
- Price$0.20 / $0.50
- Context1.05M
Alibaba (Qwen)Qwen3.7 Plus
63/100- ECI147.1
- Price$0.40 / $1.60
- Context1M
Too close to call
It is close. Our weighted score puts them within a point (MiniMax-M3 63/100, Qwen3.7 Plus 63/100, Nemotron 3.5 Super 120B A12B 59/100), so choose by what matters most for your work: Nemotron 3.5 Super 120B A12B on price. 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 priceNemotron 3.5 Super 120B A12BNemotron 3.5 Super 120B A12B $0.275 · MiniMax-M3 $0.525 · Qwen3.7 Plus $0.70 per 1M tokens (3:1 blend)
- Longest contextMiniMax-M3 and Nemotron 3.5 Super 120B A12BMiniMax-M3 1,048,576 · Nemotron 3.5 Super 120B A12B 1,048,576 · Qwen3.7 Plus 1,000,000 tokens
- Widest inputsMiniMax-M3 and Qwen3.7 PlusMiniMax-M3: Text, Images, Video · Nemotron 3.5 Super 120B A12B: Text · Qwen3.7 Plus: Text, Images, Video
- Self-hostingMiniMax-M3 and Nemotron 3.5 Super 120B A12BPublishes downloadable weights
| Measure | Weight | MiniMax-M3 | Nemotron 3.5 Super 120B A12B | Qwen3.7 Plus |
|---|---|---|---|---|
| Price | 50% | 60 | 73 | 55 |
| Inputs & features | 30% | 70 | 35 | 80 |
| Context window | 20% | 61 | 61 | 60 |
| Overall | 100% | 63/100 | 59/100 | 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-M3MiniMax | Nemotron 3.5 Super 120B A12BNVIDIA | Qwen3.7 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.9 | — | 147.1 (best) |
| ECI rank | #72 of 165 | — | #71 of 165 (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 | $0.30 | $0.20 (best) | $0.40 |
| Output | $1.20 | $0.50 (best) | $1.60 |
| Cached input | $0.06 (best) | — | $0.08 |
| Blended (3:1) | $0.525 | $0.275 (best) | $0.70 |
| Long-context rate | Over 512K: $0.60 / $2.40 | Same rate | Over 256K: $1.20 / $4.80 |
| Price source | Official MiniMax (minimax.io) API | Median of 1 providers | Official Alibaba API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 1,048,576 tokens (best) | 1,000,000 tokens |
| Max output | 512,000 tokens (best) | 131,072 tokens | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M3 | — | qwen3.7-plus |
| API providers | 50 (best) | 1 | 25 |
| Released | Jun 1, 2026 | Oct 15, 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.
- MiniMax-M3$5.40
- Nemotron 3.5 Super 120B A12B$3.00
- Qwen3.7 Plus$7.20
Which should you choose?
Which is better: MiniMax-M3, Nemotron 3.5 Super 120B A12B or Qwen3.7 Plus?
It is close. Our weighted score puts them within a point (MiniMax-M3 63/100, Qwen3.7 Plus 63/100, Nemotron 3.5 Super 120B A12B 59/100), so choose by what matters most for your work: Nemotron 3.5 Super 120B A12B on price. 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-M3, Nemotron 3.5 Super 120B A12B or Qwen3.7 Plus?
Nemotron 3.5 Super 120B A12B is cheaper at $0.20 input / $0.50 output per million tokens (median across 1 API provider). 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 $0.275 per million tokens for Nemotron 3.5 Super 120B A12B versus $0.525 for MiniMax-M3 (1.9× as much) and $0.70 for Qwen3.7 Plus (2.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M3 has an ECI of 146.9, Nemotron 3.5 Super 120B A12B has not been scored yet and Qwen3.7 Plus has an ECI of 147.1.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M3, Nemotron 3.5 Super 120B A12B 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 and Nemotron 3.5 Super 120B A12B have the largest context windows (1,048,576 and 1,048,576 tokens), against 1,000,000 for Qwen3.7 Plus. Maximum output per response: MiniMax-M3 up to 512,000, Nemotron 3.5 Super 120B A12B up to 131,072, Qwen3.7 Plus up to 64,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M3 accepts text, images and video; Nemotron 3.5 Super 120B A12B accepts text; Qwen3.7 Plus accepts text, images and video. MiniMax-M3 handles the widest range of inputs.
Are any of these open source?
MiniMax-M3 and Nemotron 3.5 Super 120B A12B publishes its weights and can be self-hosted; Qwen3.7 Plus is proprietary.
Which is newer?
Nemotron 3.5 Super 120B A12B is the newest, released Oct 15, 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 Nov 2, 2026.
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