Laguna S 2.1 vs Ling 3.1 Flash vs Qwen3.7 Plus
Laguna S 2.1 comes out ahead, 69 to 65 and 65 on our weighted score, though Ling 3.1 Flash is 11% cheaper per token.
- Our pick
PoolsideLaguna S 2.1
69/100- ECI—
- Price$0.10 / $0.20
- Context1.05M
inclusionAILing 3.1 Flash
65/100- ECI—
- Price$0.075 / $0.22
- Context262K
Alibaba (Qwen)Qwen3.7 Plus
65/100- ECI147.4
- Price$0.40 / $1.60
- Context1M
Laguna S 2.1 is our pick
Laguna S 2.1 is the better all-round choice, scoring 69/100 against Ling 3.1 Flash (65) and Qwen3.7 Plus (65). Ling 3.1 Flash wins 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 priceLing 3.1 FlashLing 3.1 Flash $0.111 · Laguna S 2.1 $0.125 · Qwen3.7 Plus $0.70 per 1M tokens (3:1 blend)
- Longest contextLaguna S 2.1Laguna S 2.1 1,048,576 · Qwen3.7 Plus 1,000,000 · Ling 3.1 Flash 262,144 tokens
- Widest inputsQwen3.7 PlusLaguna S 2.1: Text · Ling 3.1 Flash: Text · Qwen3.7 Plus: Text, Images, Video
- Self-hostingLaguna S 2.1Publishes downloadable weights
| Measure | Weight | Laguna S 2.1 | Ling 3.1 Flash | Qwen3.7 Plus |
|---|---|---|---|---|
| Price | 50% | 93 | 95 | 57 |
| Inputs & features | 30% | 35 | 35 | 80 |
| Context window | 20% | 61 | 37 | 60 |
| Overall | 100% | 69/100 | 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 | Laguna S 2.1Poolside | Ling 3.1 FlashinclusionAI | 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 | $0.10 | $0.075 (best) | $0.40 |
| Output | $0.20 (best) | $0.22 | $1.60 |
| Cached input | — | — | $0.04 |
| Blended (3:1) | $0.125 | $0.111 (best) | $0.70 |
| Long-context rate | Same rate | Same rate | Over 256K: $1.20 / $4.80 |
| Price source | Median of 4 providers | Median of 1 providers | Official Alibaba API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 262,144 tokens | 1,000,000 tokens |
| Max output | 32,768 tokens | 32,768 tokens | 64,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | poolside/laguna-s-2.1 | — | qwen3.7-plus |
| API providers | 6 | 3 | 25 (best) |
| Released | Jul 21, 2026 | Sep 29, 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.
- Laguna S 2.1$1.40
- Ling 3.1 Flash$1.19
- Qwen3.7 Plus$7.20
Which should you choose?
Which is better: Laguna S 2.1, Ling 3.1 Flash or Qwen3.7 Plus?
Laguna S 2.1 is the better all-round choice, scoring 69/100 against Ling 3.1 Flash (65) and Qwen3.7 Plus (65). Ling 3.1 Flash wins 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, Laguna S 2.1, Ling 3.1 Flash 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). Laguna S 2.1 costs $0.10 input / $0.20 output per million tokens (median across 4 API providers; free on Poolside); 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 $0.125 for Laguna S 2.1 (1.1× 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. Laguna S 2.1 has not been scored yet, Ling 3.1 Flash 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 Laguna S 2.1, Ling 3.1 Flash 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?
Laguna S 2.1 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: Laguna S 2.1 up to 32,768, Ling 3.1 Flash up to 32,768, Qwen3.7 Plus up to 64,000 tokens.
Which can read images, PDFs, audio or video?
Laguna S 2.1 accepts text; Ling 3.1 Flash accepts text; Qwen3.7 Plus accepts text, images and video. Qwen3.7 Plus handles the widest range of inputs.
Are any of these open source?
Laguna S 2.1 publishes its weights and can be self-hosted; Ling 3.1 Flash and Qwen3.7 Plus is proprietary.
Which is newer?
Ling 3.1 Flash is the newest, released Sep 29, 2026. Laguna S 2.1 came out Jul 21, 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.
