Laguna M.1 vs Mistral Large 2.1 vs Nemotron VoiceChat
Too close to call on our weighted score (Laguna M.1 68, Nemotron VoiceChat 65, Mistral Large 2.1 26). The right pick depends on what you value most.
PoolsideLaguna M.1
68/100- ECI—
- PriceFree / Free
- Context262K
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
NVIDIANemotron VoiceChat
65/100- ECI—
- PriceFree / Free
- Context128K
Too close to call
It is close. Our weighted score puts them within 3 points (Laguna M.1 68/100, Nemotron VoiceChat 65/100, Mistral Large 2.1 26/100), so choose by what matters most for your work: Laguna M.1 on price and Laguna M.1 for long inputs. 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.1 and Nemotron VoiceChatLaguna M.1 Free · Nemotron VoiceChat Free · 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 · Nemotron VoiceChat 128,000 tokens
- Widest inputsNemotron VoiceChatLaguna M.1: Text · Mistral Large 2.1: Text · Nemotron VoiceChat: Text, Audio
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Laguna M.1 | Mistral Large 2.1 | Nemotron VoiceChat |
|---|---|---|---|---|
| Price | 50% | 100 | 27 | 100 |
| Inputs & features | 30% | 35 | 25 | 35 |
| Context window | 20% | 37 | 24 | 24 |
| Overall | 100% | 68/100 | 26/100 | 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 M.1Poolside | Mistral Large 2.1Mistral AI | Nemotron VoiceChatNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 128.5 | — |
| ECI rank | — | #130 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 51.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 7.8% | — |
| Price per million tokens | |||
| Input | Free (best) | $2.00 | Free (best) |
| Output | Free (best) | $6.00 | Free (best) |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | $3.00 | Free (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Poolside API | Official Mistral API | Official Nvidia API |
| Limits | |||
| Context window | 262,144 tokens (best) | 131,072 tokens | 128,000 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 | Yes |
| 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 | nvidia/nemotron-voicechat |
| API providers | 2 (best) | 2 (best) | 1 |
| Released | Apr 28, 2026 | Nov 18, 2024 | Mar 16, 2026 |
| Knowledge cutoff | — | Nov 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
- Nemotron VoiceChatFree
Which should you choose?
Which is better: Laguna M.1, Mistral Large 2.1 or Nemotron VoiceChat?
It is close. Our weighted score puts them within 3 points (Laguna M.1 68/100, Nemotron VoiceChat 65/100, Mistral Large 2.1 26/100), so choose by what matters most for your work: Laguna M.1 on price and Laguna M.1 for long inputs. 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 Nemotron VoiceChat?
Laguna M.1 is cheaper at Free input / Free output per million tokens (official Poolside API price). Nemotron VoiceChat costs Free input / Free output per million tokens (official Nvidia 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 Nemotron VoiceChat has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Laguna M.1, Mistral Large 2.1 and Nemotron VoiceChat 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 128,000 for Nemotron VoiceChat. Maximum output per response: Laguna M.1 up to 32,768, Mistral Large 2.1 up to 16,384, Nemotron VoiceChat up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Laguna M.1 accepts text; Mistral Large 2.1 accepts text; Nemotron VoiceChat accepts text and audio. Nemotron VoiceChat handles the widest range of inputs.
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. Nemotron VoiceChat came out Mar 16, 2026; Mistral Large 2.1 came out Nov 18, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 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.
