MiniMax-M3.1-Flash-Preview vs Qwen3.7 Plus vs Ling 3.1 Flash
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.
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
inclusionAILing 3.1 Flash
36/100- ECI—
- Price$0.075 / $0.22
- Context262K
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 PlusMiniMax-M3.1-Flash-Preview: Text, Images, Video · Qwen3.7 Plus: Text, Images, Video · Ling 3.1 Flash: Text
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | MiniMax-M3.1-Flash-Preview | Qwen3.7 Plus | Ling 3.1 Flash |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 80 | 35 |
| Context window | 40% | 60 | 60 | 37 |
| Overall | 100% | 66/100 | 72/100 | 36/100 |
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 | MiniMax-M3.1-Flash-PreviewMiniMax | Qwen3.7 PlusAlibaba (Qwen) | Ling 3.1 FlashinclusionAI |
|---|---|---|---|
| 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 | — | $0.40 | $0.075 (best) |
| Output | — | $1.60 | $0.22 (best) |
| Cached input | — | $0.04 | — |
| Blended (3:1) | — | $0.70 | $0.111 (best) |
| Long-context rate | — | Over 256K: $1.20 / $4.80 | Same rate |
| Price source | — | Official Alibaba API | Median of 1 providers |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 512,000 tokens (best) | 64,000 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | — | qwen3.7-plus | — |
| API providers | — | 25 (best) | 3 |
| Released | Sep 27, 2026 | Jun 2, 2026 | Sep 29, 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-M3.1-Flash-Preview—
- Qwen3.7 Plus$7.20
- Ling 3.1 Flash$1.19
Which should you choose?
Which is better: MiniMax-M3.1-Flash-Preview, Qwen3.7 Plus or Ling 3.1 Flash?
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, MiniMax-M3.1-Flash-Preview, Qwen3.7 Plus or Ling 3.1 Flash?
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. MiniMax-M3.1-Flash-Preview has not been scored yet, Qwen3.7 Plus has an ECI of 147.4 and Ling 3.1 Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M3.1-Flash-Preview, Qwen3.7 Plus and Ling 3.1 Flash 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: MiniMax-M3.1-Flash-Preview up to 512,000, Qwen3.7 Plus up to 64,000, Ling 3.1 Flash up to 32,768 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M3.1-Flash-Preview accepts text, images and video; Qwen3.7 Plus accepts text, images and video; Ling 3.1 Flash accepts text. MiniMax-M3.1-Flash-Preview handles the widest range of inputs.
Are any of these open source?
No. MiniMax-M3.1-Flash-Preview, Qwen3.7 Plus and Ling 3.1 Flash 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.
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