Qwen3.7 Plus vs Ling 3.1 Flash vs MiniMax-M3
Too close to call on our weighted score (Ling 3.1 Flash 65, MiniMax-M3 65, Qwen3.7 Plus 65). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.7 Plus
65/100- ECI147.4
- Price$0.40 / $1.60
- Context1M
inclusionAILing 3.1 Flash
65/100- ECI—
- Price$0.075 / $0.22
- Context262K
MiniMaxMiniMax-M3
65/100- ECI147.0
- Price$0.30 / $1.20
- Context1.05M
Too close to call
It is close. Our weighted score puts them within a point (Ling 3.1 Flash 65/100, MiniMax-M3 65/100, Qwen3.7 Plus 65/100), so choose by what matters most for your work: Ling 3.1 Flash on price and MiniMax-M3 for long inputs. 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 priceLing 3.1 FlashLing 3.1 Flash $0.111 · 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 · Ling 3.1 Flash 262,144 tokens
- Widest inputsQwen3.7 Plus and MiniMax-M3Qwen3.7 Plus: Text, Images, Video · Ling 3.1 Flash: Text · MiniMax-M3: Text, Images, Video
- Self-hostingMiniMax-M3Publishes downloadable weights
| Measure | Weight | Qwen3.7 Plus | Ling 3.1 Flash | MiniMax-M3 |
|---|---|---|---|---|
| Price | 50% | 57 | 95 | 63 |
| Inputs & features | 30% | 80 | 35 | 70 |
| Context window | 20% | 60 | 37 | 61 |
| Overall | 100% | 65/100 | 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 | Qwen3.7 PlusAlibaba (Qwen) | Ling 3.1 FlashinclusionAI | MiniMax-M3MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 147.4 (best) | — | 147.0 |
| ECI rank | #61 of 148 (best) | — | #62 of 148 |
| GPQA DiamondGraduate-level science questions | 87.9% | — | 90.9% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 34.4% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% (best) | — | 71.1% |
| Price per million tokens | |||
| Input | $0.40 | $0.075 (best) | $0.30 |
| Output | $1.60 | $0.22 (best) | $1.20 |
| Cached input | $0.04 (best) | — | $0.06 |
| Blended (3:1) | $0.70 | $0.111 (best) | $0.525 |
| Long-context rate | Over 256K: $1.20 / $4.80 | Same rate | Over 512K: $0.60 / $2.40 |
| Price source | Official Alibaba API | Median of 1 providers | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 1,000,000 tokens | 262,144 tokens | 1,048,576 tokens (best) |
| Max output | 64,000 tokens | 32,768 tokens | 512,000 tokens (best) |
| 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 | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | qwen3.7-plus | — | MiniMax-M3 |
| API providers | 25 | 3 | 42 (best) |
| Released | Jun 2, 2026 | Sep 29, 2026 | Jun 1, 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.
- Qwen3.7 Plus$7.20
- Ling 3.1 Flash$1.19
- MiniMax-M3$5.40
Which should you choose?
Which is better: Qwen3.7 Plus, Ling 3.1 Flash or MiniMax-M3?
It is close. Our weighted score puts them within a point (Ling 3.1 Flash 65/100, MiniMax-M3 65/100, Qwen3.7 Plus 65/100), so choose by what matters most for your work: Ling 3.1 Flash on price and MiniMax-M3 for long inputs. 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, Qwen3.7 Plus, Ling 3.1 Flash or MiniMax-M3?
Ling 3.1 Flash is cheaper at $0.075 input / $0.22 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 $0.111 per million tokens for Ling 3.1 Flash versus $0.525 for MiniMax-M3 (4.7× as much) and $0.70 for Qwen3.7 Plus (6.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.7 Plus has an ECI of 147.4, Ling 3.1 Flash has not been scored yet and MiniMax-M3 has an ECI of 147.0.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.7 Plus, Ling 3.1 Flash and MiniMax-M3 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 Ling 3.1 Flash. Maximum output per response: Qwen3.7 Plus up to 64,000, Ling 3.1 Flash up to 32,768, MiniMax-M3 up to 512,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3.7 Plus accepts text, images and video; Ling 3.1 Flash accepts text; MiniMax-M3 accepts text, images and video. Qwen3.7 Plus handles the widest range of inputs.
Are any of these open source?
MiniMax-M3 publishes its weights and can be self-hosted; Qwen3.7 Plus and Ling 3.1 Flash is proprietary.
Which is newer?
Ling 3.1 Flash is the newest, released Sep 29, 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.
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