Ling 3.1 Flash vs MiniMax-M3.1-Flash-Preview vs Qwen3.7 Plus
Qwen3.7 Plus comes out ahead, 72 to 66 and 36 on our weighted score, though Ling 3.1 Flash is 6.3× cheaper per token.
inclusionAILing 3.1 Flash
36/100- ECI—
- Price$0.075 / $0.22
- Context262K
MiniMaxMiniMax-M3.1-Flash-Preview
66/100- ECI—
- Price—
- Context1M
- Our pick
Alibaba (Qwen)Qwen3.7 Plus
72/100- ECI147.4
- Price$0.40 / $1.60
- Context1M
Qwen3.7 Plus is our pick
Qwen3.7 Plus is the better all-round choice, scoring 72/100 against MiniMax-M3.1-Flash-Preview (66) 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 priceLing 3.1 FlashLing 3.1 Flash $0.111 · Qwen3.7 Plus $0.70 per 1M tokens (3:1 blend) · MiniMax-M3.1-Flash-Preview unpriced
- Longest contextMiniMax-M3.1-Flash-Preview and Qwen3.7 PlusMiniMax-M3.1-Flash-Preview 1,000,000 · Qwen3.7 Plus 1,000,000 · Ling 3.1 Flash 262,144 tokens
- Widest inputsMiniMax-M3.1-Flash-Preview and Qwen3.7 PlusLing 3.1 Flash: Text · MiniMax-M3.1-Flash-Preview: Text, Images, Video · Qwen3.7 Plus: Text, Images, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Ling 3.1 Flash | MiniMax-M3.1-Flash-Preview | Qwen3.7 Plus |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 70 | 80 |
| Context window | 40% | 37 | 60 | 60 |
| Overall | 100% | 36/100 | 66/100 | 72/100 |
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 | Qwen3.7 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 147.4 |
| ECI rank | — | — | #61 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 87.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 34.4% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 93.3% |
| Price per million tokens | |||
| Input | $0.075 (best) | — | $0.40 |
| Output | $0.22 (best) | — | $1.60 |
| Cached input | — | — | $0.04 |
| Blended (3:1) | $0.111 (best) | — | $0.70 |
| Long-context rate | Same rate | — | Over 256K: $1.20 / $4.80 |
| Price source | Median of 1 providers | — | Official Alibaba API |
| Limits | |||
| Context window | 262,144 tokens | 1,000,000 tokens (best) | 1,000,000 tokens (best) |
| Max output | 32,768 tokens | 512,000 tokens (best) | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | 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 | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | — | — | qwen3.7-plus |
| API providers | 3 | — | 25 (best) |
| Released | Sep 29, 2026 | Sep 27, 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.
- Ling 3.1 Flash$1.19
- MiniMax-M3.1-Flash-Preview—
- Qwen3.7 Plus$7.20
Which should you choose?
Which is better: Ling 3.1 Flash, MiniMax-M3.1-Flash-Preview or Qwen3.7 Plus?
Qwen3.7 Plus is the better all-round choice, scoring 72/100 against MiniMax-M3.1-Flash-Preview (66) 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 Qwen3.7 Plus?
Ling 3.1 Flash is cheaper at $0.075 input / $0.22 output per million tokens (median across 1 API provider). 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 $0.111 per million tokens for Ling 3.1 Flash versus $0.70 for Qwen3.7 Plus (6.3× 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 Qwen3.7 Plus has an ECI of 147.4.
Which is better for coding?
There are no published SWE-bench Verified results for Ling 3.1 Flash, MiniMax-M3.1-Flash-Preview 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-M3.1-Flash-Preview and Qwen3.7 Plus have the largest context windows (1,000,000 and 1,000,000 tokens), against 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, Qwen3.7 Plus up to 64,000 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; Qwen3.7 Plus accepts text, images and video. MiniMax-M3.1-Flash-Preview handles the widest range of inputs.
Are any of these open source?
No. Ling 3.1 Flash, MiniMax-M3.1-Flash-Preview and Qwen3.7 Plus are proprietary and only available through APIs and apps.
Which is newer?
Ling 3.1 Flash is the newest, released Sep 29, 2026. MiniMax-M3.1-Flash-Preview came out Sep 27, 2026; Qwen3.7 Plus came out Jun 2, 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.
