Laguna M.1 vs Nemotron VoiceChat vs Qwen2.5 32B Instruct
Too close to call on our weighted score (Laguna M.1 68, Nemotron VoiceChat 65, Qwen2.5 32B Instruct 35). The right pick depends on what you value most.
PoolsideLaguna M.1
68/100- ECI—
- PriceFree / Free
- Context262K
NVIDIANemotron VoiceChat
65/100- ECI—
- PriceFree / Free
- Context128K
Alibaba (Qwen)Qwen2.5 32B Instruct
35/100- ECI128.5
- Price$0.70 / $2.80
- Context131K
Too close to call
It is close. Our weighted score puts them within 3 points (Laguna M.1 68/100, Nemotron VoiceChat 65/100, Qwen2.5 32B Instruct 35/100), 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 · Qwen2.5 32B Instruct $1.23 per 1M tokens (3:1 blend)
- Longest contextLaguna M.1Laguna M.1 262,144 · Qwen2.5 32B Instruct 131,072 · Nemotron VoiceChat 128,000 tokens
- Widest inputsNemotron VoiceChatLaguna M.1: Text · Nemotron VoiceChat: Text, Audio · 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 | Nemotron VoiceChat | Qwen2.5 32B Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 46 |
| Inputs & features | 30% | 35 | 35 | 25 |
| Context window | 20% | 37 | 24 | 24 |
| Overall | 100% | 68/100 | 65/100 | 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 | Nemotron VoiceChatNVIDIA | Qwen2.5 32B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 128.5 |
| ECI rank | — | — | #131 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 46.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 7.4% |
| Price per million tokens | |||
| Input | Free (best) | Free (best) | $0.70 |
| Output | Free (best) | Free (best) | $2.80 |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | Free (best) | $1.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Poolside API | Official Nvidia API | Official Alibaba API |
| Limits | |||
| Context window | 262,144 tokens (best) | 128,000 tokens | 131,072 tokens |
| Max output | 32,768 tokens (best) | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | Yes | 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 | nvidia/nemotron-voicechat | qwen2-5-32b-instruct |
| API providers | 2 (best) | 1 | 1 |
| Released | Apr 28, 2026 | Mar 16, 2026 | Sep 17, 2024 |
| Knowledge cutoff | — | — | 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
- Nemotron VoiceChatFree
- Qwen2.5 32B Instruct$12.60
Which should you choose?
Which is better: Laguna M.1, Nemotron VoiceChat or Qwen2.5 32B Instruct?
It is close. Our weighted score puts them within 3 points (Laguna M.1 68/100, Nemotron VoiceChat 65/100, Qwen2.5 32B Instruct 35/100), 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, Nemotron VoiceChat or Qwen2.5 32B Instruct?
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); Qwen2.5 32B Instruct costs $0.70 input / $2.80 output per million tokens (official Alibaba 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, Nemotron VoiceChat has not been scored yet 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, Nemotron VoiceChat 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 Qwen2.5 32B Instruct and 128,000 for Nemotron VoiceChat. Maximum output per response: Laguna M.1 up to 32,768, Nemotron VoiceChat up to 8,192, Qwen2.5 32B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Laguna M.1 accepts text; Nemotron VoiceChat accepts text and audio; Qwen2.5 32B Instruct accepts text. 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; Qwen2.5 32B Instruct came out Sep 17, 2024. Knowledge cutoff: 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.
