Mistral Nemotron vs Laguna M.1
Laguna M.1 comes out ahead, 68 to 62 on our weighted score.
NVIDIAMistral Nemotron
62/100- ECI—
- PriceFree / Free
- Context128K
- Our pick
PoolsideLaguna M.1
68/100- ECI—
- PriceFree / Free
- Context262K
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 priceMistral Nemotron Free · Laguna M.1 Free per 1M tokens (3:1 blend)
- Longest contextLaguna M.1Laguna M.1 262,144 · Mistral Nemotron 128,000 tokens
- Widest inputsSame inputsMistral Nemotron: Text · Laguna M.1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Nemotron | Laguna M.1 |
|---|---|---|---|
| Price | 50% | 100 | 100 |
| Inputs & features | 30% | 25 | 35 |
| Context window | 20% | 24 | 37 |
| Overall | 100% | 62/100 | 68/100 |
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 | Mistral NemotronNVIDIA | Laguna M.1Poolside |
|---|---|---|
| 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 Nvidia API | Official Poolside API |
| Limits | ||
| Context window | 128,000 tokens | 262,144 tokens (best) |
| Max output | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | mistralai/mistral-nemotron | poolside/laguna-m.1 |
| API providers | 1 | 2 (best) |
| Released | Jun 11, 2025 | 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.
- Mistral NemotronFree
- Laguna M.1Free
Which should you choose?
Which is better: Mistral Nemotron or Laguna M.1?
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, Mistral Nemotron or Laguna M.1?
Mistral Nemotron and Laguna M.1 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. Mistral Nemotron 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 Mistral Nemotron 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 M.1 has the largest context window at 262,144 tokens, against 128,000 for Mistral Nemotron. Maximum output per response: Mistral Nemotron up to 8,192, Laguna M.1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Mistral Nemotron 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 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.
