Laguna M.1 vs Mistral Nemotron
Laguna M.1 comes out ahead, 68 to 62 on our weighted score.
- Our pick
PoolsideLaguna M.1
68/100- ECI—
- PriceFree / Free
- Context262K
NVIDIAMistral Nemotron
62/100- ECI—
- PriceFree / Free
- Context128K
Add a model
Make it a three-way comparison.
Laguna M.1 is our pick
Laguna M.1 is the better all-round choice, scoring 68/100 against Mistral Nemotron (62). It leads on 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 priceSame priceLaguna M.1 Free · Mistral Nemotron Free per 1M tokens (3:1 blend)
- Longest contextLaguna M.1Laguna M.1 262,144 · Mistral Nemotron 128,000 tokens
- Widest inputsSame inputsLaguna M.1: Text · Mistral Nemotron: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Laguna M.1 | Mistral Nemotron |
|---|---|---|---|
| Price | 50% | 100 | 100 |
| Inputs & features | 30% | 35 | 25 |
| Context window | 20% | 37 | 24 |
| Overall | 100% | 68/100 | 62/100 |
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 NemotronNVIDIA |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | Free | Free |
| Output | Free | Free |
| Cached input | — | — |
| Blended (3:1) | Free | Free |
| Long-context rate | Same rate | Same rate |
| Price source | Official Poolside API | Official Nvidia API |
| Limits | ||
| Context window | 262,144 tokens (best) | 128,000 tokens |
| Max output | 32,768 tokens (best) | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | poolside/laguna-m.1 | mistralai/mistral-nemotron |
| API providers | 2 (best) | 1 |
| Released | Apr 28, 2026 | Jun 11, 2025 |
| 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 M.1Free
- Mistral NemotronFree
Which should you choose?
Which is better: Laguna M.1 or Mistral Nemotron?
Laguna M.1 is the better all-round choice, scoring 68/100 against Mistral Nemotron (62). It leads on 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 or Mistral Nemotron?
Laguna M.1 and Mistral Nemotron cost the same: Free input / Free output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Laguna M.1 has not been scored yet and Mistral Nemotron has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Laguna M.1 and Mistral Nemotron 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 M.1 has the largest context window at 262,144 tokens, against 128,000 for Mistral Nemotron. Maximum output per response: Laguna M.1 up to 32,768, Mistral Nemotron up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Laguna M.1 accepts text; Mistral Nemotron 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 M.1 is the newest, released Apr 28, 2026. Mistral Nemotron came out Jun 11, 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.
