Ling 3.1 Flash vs MiniMax-M3.1-Flash-Preview vs Nemotron 3.5 Super 120B A12B
MiniMax-M3.1-Flash-Preview comes out ahead, 66 to 45 and 36 on our weighted score.
inclusionAILing 3.1 Flash
36/100- ECI—
- Price$0.30 / $0.90
- Context262K
- Our pick
MiniMaxMiniMax-M3.1-Flash-Preview
66/100- ECI—
- Price—
- Context1M
NVIDIANemotron 3.5 Super 120B A12B
45/100- ECI—
- Price$0.20 / $0.50
- Context1.05M
MiniMax-M3.1-Flash-Preview is our pick
MiniMax-M3.1-Flash-Preview is the better all-round choice, scoring 66/100 against Nemotron 3.5 Super 120B A12B (45) and Ling 3.1 Flash (36). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. 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 · Ling 3.1 Flash $0.45 per 1M tokens (3:1 blend) · MiniMax-M3.1-Flash-Preview unpriced
- Longest contextNemotron 3.5 Super 120B A12BNemotron 3.5 Super 120B A12B 1,048,576 · MiniMax-M3.1-Flash-Preview 1,000,000 · Ling 3.1 Flash 262,144 tokens
- Widest inputsMiniMax-M3.1-Flash-PreviewLing 3.1 Flash: Text · MiniMax-M3.1-Flash-Preview: Text, Images, Video · Nemotron 3.5 Super 120B A12B: Text
- Self-hostingLing 3.1 Flash and Nemotron 3.5 Super 120B A12BPublishes downloadable weights
| Measure | Weight | Ling 3.1 Flash | MiniMax-M3.1-Flash-Preview | Nemotron 3.5 Super 120B A12B |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 70 | 35 |
| Context window | 40% | 37 | 60 | 61 |
| Overall | 100% | 36/100 | 66/100 | 45/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Ling 3.1 FlashinclusionAI | MiniMax-M3.1-Flash-PreviewMiniMax | Nemotron 3.5 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | — | $0.20 (best) |
| Output | $0.90 | — | $0.50 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 | — | $0.275 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Median of 5 providers | — | Median of 1 providers |
| Limits | |||
| Context window | 262,144 tokens | 1,000,000 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 512,000 tokens (best) | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | — | — |
| API providers | 7 (best) | — | 1 |
| Released | Sep 29, 2026 | Sep 27, 2026 | Oct 15, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ling 3.1 Flash$4.80
- MiniMax-M3.1-Flash-Preview—
- Nemotron 3.5 Super 120B A12B$3.00
Which should you choose?
Which is better: Ling 3.1 Flash, MiniMax-M3.1-Flash-Preview or Nemotron 3.5 Super 120B A12B?
MiniMax-M3.1-Flash-Preview is the better all-round choice, scoring 66/100 against Nemotron 3.5 Super 120B A12B (45) and Ling 3.1 Flash (36). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Ling 3.1 Flash, MiniMax-M3.1-Flash-Preview or Nemotron 3.5 Super 120B A12B?
Nemotron 3.5 Super 120B A12B is cheaper at $0.20 input / $0.50 output per million tokens (median across 1 API provider). Ling 3.1 Flash costs $0.30 input / $0.90 output per million tokens (median across 5 API providers). 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 $0.45 for Ling 3.1 Flash (1.6× as much). MiniMax-M3.1-Flash-Preview has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ling 3.1 Flash has not been scored yet, MiniMax-M3.1-Flash-Preview has not been scored yet and Nemotron 3.5 Super 120B A12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ling 3.1 Flash, MiniMax-M3.1-Flash-Preview and Nemotron 3.5 Super 120B A12B 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?
Nemotron 3.5 Super 120B A12B has the largest context window at 1,048,576 tokens, against 1,000,000 for MiniMax-M3.1-Flash-Preview and 262,144 for Ling 3.1 Flash. Maximum output per response: Ling 3.1 Flash up to 32,768, MiniMax-M3.1-Flash-Preview up to 512,000, Nemotron 3.5 Super 120B A12B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling 3.1 Flash accepts text; MiniMax-M3.1-Flash-Preview accepts text, images and video; Nemotron 3.5 Super 120B A12B accepts text. MiniMax-M3.1-Flash-Preview handles the widest range of inputs.
Are any of these open source?
Ling 3.1 Flash and Nemotron 3.5 Super 120B A12B publishes its weights and can be self-hosted; MiniMax-M3.1-Flash-Preview is proprietary.
Which is newer?
Nemotron 3.5 Super 120B A12B is the newest, released Oct 15, 2026. Ling 3.1 Flash came out Sep 29, 2026; MiniMax-M3.1-Flash-Preview came out Sep 27, 2026.
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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