Laguna XS.2 vs Laguna M.1
Too close to call on our weighted score (Laguna XS.2 36, Laguna M.1 36). The right pick depends on what you value most.
PoolsideLaguna XS.2
36/100- ECI—
- Price—
- Context262K
- Poolside
Laguna M.1
36/100- ECI—
- PriceFree / Free
- Context262K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (Laguna XS.2 36/100, Laguna M.1 36/100), so choose by what matters most for your work: Laguna M.1 on price. The score weighs inputs & features 60%, context window 40%. 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 per 1M tokens (3:1 blend) · Laguna XS.2 unpriced
- Longest contextAbout the sameLaguna XS.2 262,144 · Laguna M.1 262,144 tokens
- Widest inputsSame inputsLaguna XS.2: Text · Laguna M.1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Laguna XS.2 | Laguna M.1 |
|---|---|---|---|
| Inputs & features | 60% | 35 | 35 |
| Context window | 40% | 37 | 37 |
| Overall | 100% | 36/100 | 36/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Laguna XS.2Poolside | Laguna M.1Poolside |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | Free |
| Output | — | Free |
| Cached input | — | — |
| Blended (3:1) | — | Free |
| Long-context rate | — | Same rate |
| Price source | — | Official Poolside API |
| Limits | ||
| Context window | 262,144 tokens | 262,144 tokens |
| Max output | 32,768 tokens | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | — | poolside/laguna-m.1 |
| API providers | 1 | 2 (best) |
| Released | Apr 28, 2026 | Apr 28, 2026 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Laguna XS.2—
- Laguna M.1Free
Which should you choose?
Which is better: Laguna XS.2 or Laguna M.1?
It is close. Our weighted score puts them within a point (Laguna XS.2 36/100, Laguna M.1 36/100), so choose by what matters most for your work: Laguna M.1 on price. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Laguna XS.2 or Laguna M.1?
Laguna M.1 is cheaper at Free input / Free output per million tokens (official Poolside API price). . Laguna M.1 is listed as free. Laguna XS.2 has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Laguna XS.2 has not been scored yet and Laguna M.1 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Laguna XS.2 and Laguna M.1 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Laguna XS.2 and Laguna M.1 share the same 262,144-token context window. Maximum output per response: Laguna XS.2 up to 32,768, Laguna M.1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Laguna XS.2 accepts text; Laguna M.1 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
Laguna XS.2 is the newest, released Apr 28, 2026. Laguna M.1 came out Apr 28, 2026.
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.
