Qwen3.7 Plus vs Laguna S 2.1 vs MiniMax-M3
Laguna S 2.1 comes out ahead, 69 to 65 and 65 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3.7 Plus
65/100- ECI147.4
- Price$0.40 / $1.60
- Context1M
- Our pick
PoolsideLaguna S 2.1
69/100- ECI—
- Price$0.10 / $0.20
- Context1.05M
MiniMaxMiniMax-M3
65/100- ECI147.0
- Price$0.30 / $1.20
- Context1.05M
Laguna S 2.1 is our pick
Laguna S 2.1 is the better all-round choice, scoring 69/100 against MiniMax-M3 (65) and Qwen3.7 Plus (65). It leads on price. Qwen3.7 Plus wins on inputs & features. 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 priceLaguna S 2.1Laguna S 2.1 $0.125 · MiniMax-M3 $0.525 · Qwen3.7 Plus $0.70 per 1M tokens (3:1 blend)
- Longest contextLaguna S 2.1 and MiniMax-M3Laguna S 2.1 1,048,576 · MiniMax-M3 1,048,576 · Qwen3.7 Plus 1,000,000 tokens
- Widest inputsQwen3.7 Plus and MiniMax-M3Qwen3.7 Plus: Text, Images, Video · Laguna S 2.1: Text · MiniMax-M3: Text, Images, Video
- Self-hostingLaguna S 2.1 and MiniMax-M3Publishes downloadable weights
| Measure | Weight | Qwen3.7 Plus | Laguna S 2.1 | MiniMax-M3 |
|---|---|---|---|---|
| Price | 50% | 57 | 93 | 63 |
| Inputs & features | 30% | 80 | 35 | 70 |
| Context window | 20% | 60 | 61 | 61 |
| Overall | 100% | 65/100 | 69/100 | 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) | Laguna S 2.1Poolside | 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 | $0.10 (best) | $0.30 |
| Output | $1.60 | $0.20 (best) | $1.20 |
| Cached input | $0.04 (best) | — | $0.06 |
| Blended (3:1) | $0.70 | $0.125 (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 4 providers | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) | 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 | Open | Open |
| API model ID | qwen3.7-plus | poolside/laguna-s-2.1 | MiniMax-M3 |
| API providers | 25 | 6 | 42 (best) |
| Released | Jun 2, 2026 | Jul 21, 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
- Laguna S 2.1$1.40
- MiniMax-M3$5.40
Which should you choose?
Which is better: Qwen3.7 Plus, Laguna S 2.1 or MiniMax-M3?
Laguna S 2.1 is the better all-round choice, scoring 69/100 against MiniMax-M3 (65) and Qwen3.7 Plus (65). It leads on price. Qwen3.7 Plus wins on inputs & features. 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, Laguna S 2.1 or MiniMax-M3?
Laguna S 2.1 is cheaper at $0.10 input / $0.20 output per million tokens (median across 4 API providers; free on Poolside). 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 $0.125 per million tokens for Laguna S 2.1 versus $0.525 for MiniMax-M3 (4.2× as much) and $0.70 for Qwen3.7 Plus (5.6× 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, Laguna S 2.1 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, Laguna S 2.1 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?
Laguna S 2.1 and MiniMax-M3 have the largest context windows (1,048,576 and 1,048,576 tokens), against 1,000,000 for Qwen3.7 Plus. Maximum output per response: Qwen3.7 Plus up to 64,000, Laguna S 2.1 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; Laguna S 2.1 accepts text; MiniMax-M3 accepts text, images and video. Qwen3.7 Plus handles the widest range of inputs.
Are any of these open source?
Laguna S 2.1 and MiniMax-M3 publishes its weights and can be self-hosted; Qwen3.7 Plus is proprietary.
Which is newer?
Laguna S 2.1 is the newest, released Jul 21, 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.
