Laguna M.1 vs Mistral Large 2.1 vs Qwen2.5 32B Instruct
Laguna M.1 comes out ahead, 68 to 35 and 26 on our weighted score, and it is the cheaper option too.
- Our pick
PoolsideLaguna M.1
68/100- ECI—
- PriceFree / Free
- Context262K
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
Alibaba (Qwen)Qwen2.5 32B Instruct
35/100- ECI128.5
- Price$0.70 / $2.80
- Context131K
Laguna M.1 is our pick
Laguna M.1 is the better all-round choice, scoring 68/100 against Qwen2.5 32B Instruct (35) and Mistral Large 2.1 (26). It leads on price, inputs & features and context window. 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 M.1Laguna M.1 Free · Qwen2.5 32B Instruct $1.23 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextLaguna M.1Laguna M.1 262,144 · Mistral Large 2.1 131,072 · Qwen2.5 32B Instruct 131,072 tokens
- Widest inputsSame inputsLaguna M.1: Text · Mistral Large 2.1: Text · Qwen2.5 32B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Laguna M.1 | Mistral Large 2.1 | Qwen2.5 32B Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 27 | 46 |
| Inputs & features | 30% | 35 | 25 | 25 |
| Context window | 20% | 37 | 24 | 24 |
| Overall | 100% | 68/100 | 26/100 | 35/100 |
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 M.1Poolside | Mistral Large 2.1Mistral AI | Qwen2.5 32B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 128.5 | 128.5 |
| ECI rank | — | #130 of 148 (best) | #131 of 148 |
| GPQA DiamondGraduate-level science questions | — | 51.3% (best) | 46.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 7.8% (best) | 7.4% |
| Price per million tokens | |||
| Input | Free (best) | $2.00 | $0.70 |
| Output | Free (best) | $6.00 | $2.80 |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | $3.00 | $1.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Poolside API | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 262,144 tokens (best) | 131,072 tokens | 131,072 tokens |
| Max output | 32,768 tokens (best) | 16,384 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | poolside/laguna-m.1 | mistral-large-2411 | qwen2-5-32b-instruct |
| API providers | 2 (best) | 2 (best) | 1 |
| Released | Apr 28, 2026 | Nov 18, 2024 | Sep 17, 2024 |
| Knowledge cutoff | — | Nov 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Laguna M.1Free
- Mistral Large 2.1$32.00
- Qwen2.5 32B Instruct$12.60
Which should you choose?
Which is better: Laguna M.1, Mistral Large 2.1 or Qwen2.5 32B Instruct?
Laguna M.1 is the better all-round choice, scoring 68/100 against Qwen2.5 32B Instruct (35) and Mistral Large 2.1 (26). It leads on price, inputs & features and context window. 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 M.1, Mistral Large 2.1 or Qwen2.5 32B Instruct?
Laguna M.1 is cheaper at Free input / Free output per million tokens (official Poolside API price). Qwen2.5 32B Instruct costs $0.70 input / $2.80 output per million tokens (official Alibaba API price); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral API price). Laguna M.1 is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Laguna M.1 has not been scored yet, Mistral Large 2.1 has an ECI of 128.5 and Qwen2.5 32B Instruct has an ECI of 128.5.
Which is better for coding?
There are no published SWE-bench Verified results for Laguna M.1, Mistral Large 2.1 and Qwen2.5 32B Instruct 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 M.1 has the largest context window at 262,144 tokens, against 131,072 for Mistral Large 2.1 and 131,072 for Qwen2.5 32B Instruct. Maximum output per response: Laguna M.1 up to 32,768, Mistral Large 2.1 up to 16,384, Qwen2.5 32B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Laguna M.1 accepts text; Mistral Large 2.1 accepts text; Qwen2.5 32B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Laguna M.1 is the newest, released Apr 28, 2026. Mistral Large 2.1 came out Nov 18, 2024; Qwen2.5 32B Instruct came out Sep 17, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Qwen2.5 32B Instruct Apr 2024.
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.
