Qwen3.7 Flash vs MiniMax-M2.7 vs DeepSeek V4 Flash
Qwen3.7 Flash comes out ahead, 79 to 71 and 61 on our weighted score, and it is the cheaper option too.
- Our pick
Alibaba (Qwen)Qwen3.7 Flash
79/100- ECI144.6
- Price$0.03 / $0.13
- Context1M
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
DeepSeekDeepSeek V4 Flash
71/100- ECI146.1
- Price$0.14 / $0.28
- Context1M
Qwen3.7 Flash is our pick
Qwen3.7 Flash is the better all-round choice, scoring 79/100 against DeepSeek V4 Flash (71) and MiniMax-M2.7 (61). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V4 FlashCapabilities Index (ECI): DeepSeek V4 Flash 146.1 · MiniMax-M2.7 145.9 · Qwen3.7 Flash 144.6
- Lowest priceQwen3.7 FlashQwen3.7 Flash $0.055 · DeepSeek V4 Flash $0.175 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextQwen3.7 Flash and DeepSeek V4 FlashQwen3.7 Flash 1,000,000 · DeepSeek V4 Flash 1,000,000 · MiniMax-M2.7 204,800 tokens
- Widest inputsQwen3.7 FlashQwen3.7 Flash: Text, Images, Video · MiniMax-M2.7: Text · DeepSeek V4 Flash: Text
- Self-hostingMiniMax-M2.7 and DeepSeek V4 FlashPublishes downloadable weights
| Measure | Weight | Qwen3.7 Flash | MiniMax-M2.7 | DeepSeek V4 Flash |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 71 | 73 | 73 |
| Price | 25% | 100 | 63 | 86 |
| Inputs & features | 15% | 80 | 35 | 45 |
| Context window | 10% | 60 | 32 | 60 |
| Overall | 100% | 79/100 | 61/100 | 71/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.7 FlashAlibaba (Qwen) | MiniMax-M2.7MiniMax | DeepSeek V4 FlashDeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 144.6 | 145.9 | 146.1 (best) |
| ECI rank | #80 of 148 | #73 of 148 | #71 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 82.3% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 19.3% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 86.7% | — | — |
| Price per million tokens | |||
| Input | $0.03 (best) | $0.30 | $0.14 |
| Output | $0.13 (best) | $1.20 | $0.28 |
| Cached input | $0.003 (best) | $0.06 | — |
| Blended (3:1) | $0.055 (best) | $0.525 | $0.175 |
| Long-context rate | Over 32K: $0.10 / $0.40 | Same rate | Same rate |
| Price source | Official Alibaba API | Official MiniMax (minimax.io) API | Median of 42 providers |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 204,800 tokens | 1,000,000 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens | 384,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | qwen3.7-flash | MiniMax-M2.7 | — |
| API providers | 12 | 29 | 48 (best) |
| Released | Jul 15, 2026 | Mar 18, 2026 | Apr 24, 2026 |
| Knowledge cutoff | — | — | May 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 Flash$0.56
- MiniMax-M2.7$5.40
- DeepSeek V4 Flash$1.96
Which should you choose?
Which is better: Qwen3.7 Flash, MiniMax-M2.7 or DeepSeek V4 Flash?
Qwen3.7 Flash is the better all-round choice, scoring 79/100 against DeepSeek V4 Flash (71) and MiniMax-M2.7 (61). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.7 Flash, MiniMax-M2.7 or DeepSeek V4 Flash?
Qwen3.7 Flash is cheaper at $0.03 input / $0.13 output per million tokens (official Alibaba API price). DeepSeek V4 Flash costs $0.14 input / $0.28 output per million tokens (median across 42 API providers); MiniMax-M2.7 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). At a typical mix of three input tokens to one output token, that is $0.055 per million tokens for Qwen3.7 Flash versus $0.175 for DeepSeek V4 Flash (3.2× as much) and $0.525 for MiniMax-M2.7 (9.5× as much).
Which scores higher on benchmarks?
DeepSeek V4 Flash scores higher on the Capabilities Index (ECI): DeepSeek V4 Flash 146.1 (#71 of 148), MiniMax-M2.7 145.9 (#73 of 148) and Qwen3.7 Flash 144.6 (#80 of 148). The confidence ranges of the top two overlap (143.6–147.9 vs 138.2–148.0), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.7 Flash, MiniMax-M2.7 and DeepSeek V4 Flash yet, so there is no like-for-like coding score. On overall capability, DeepSeek V4 Flash leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.7 Flash and DeepSeek V4 Flash have the largest context windows (1,000,000 and 1,000,000 tokens), against 204,800 for MiniMax-M2.7. Maximum output per response: Qwen3.7 Flash up to 131,072, MiniMax-M2.7 up to 131,072, DeepSeek V4 Flash up to 384,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3.7 Flash accepts text, images and video; MiniMax-M2.7 accepts text; DeepSeek V4 Flash accepts text. Qwen3.7 Flash handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 and DeepSeek V4 Flash publishes its weights and can be self-hosted; Qwen3.7 Flash is proprietary.
Which is newer?
Qwen3.7 Flash is the newest, released Jul 15, 2026. DeepSeek V4 Flash came out Apr 24, 2026; MiniMax-M2.7 came out Mar 18, 2026. Knowledge cutoff: DeepSeek V4 Flash May 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.
