Mistral Large 2.1 vs Nemotron VoiceChat vs Qwen2.5 32B Instruct
Nemotron VoiceChat comes out ahead, 65 to 35 and 26 on our weighted score, and it is the cheaper option too.
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
- Our pick
NVIDIANemotron VoiceChat
65/100- ECI—
- PriceFree / Free
- Context128K
Alibaba (Qwen)Qwen2.5 32B Instruct
35/100- ECI128.5
- Price$0.70 / $2.80
- Context131K
Nemotron VoiceChat is our pick
Nemotron VoiceChat is the better all-round choice, scoring 65/100 against Qwen2.5 32B Instruct (35) and Mistral Large 2.1 (26). It leads on price and inputs & features. 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 priceNemotron VoiceChatNemotron VoiceChat Free · Qwen2.5 32B Instruct $1.23 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextMistral Large 2.1 and Qwen2.5 32B InstructMistral Large 2.1 131,072 · Qwen2.5 32B Instruct 131,072 · Nemotron VoiceChat 128,000 tokens
- Widest inputsNemotron VoiceChatMistral Large 2.1: 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 | Mistral Large 2.1 | Nemotron VoiceChat | Qwen2.5 32B Instruct |
|---|---|---|---|---|
| Price | 50% | 27 | 100 | 46 |
| Inputs & features | 30% | 25 | 35 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 26/100 | 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 | Mistral Large 2.1Mistral AI | Nemotron VoiceChatNVIDIA | Qwen2.5 32B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 128.5 | — | 128.5 |
| ECI rank | #130 of 148 (best) | — | #131 of 148 |
| GPQA DiamondGraduate-level science questions | 51.3% (best) | — | 46.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% (best) | — | 7.4% |
| Price per million tokens | |||
| Input | $2.00 | Free (best) | $0.70 |
| Output | $6.00 | Free (best) | $2.80 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | Free (best) | $1.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Nvidia API | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 16,384 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 | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | mistral-large-2411 | nvidia/nemotron-voicechat | qwen2-5-32b-instruct |
| API providers | 2 (best) | 1 | 1 |
| Released | Nov 18, 2024 | Mar 16, 2026 | Sep 17, 2024 |
| Knowledge cutoff | Nov 2024 | — | 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.
- Mistral Large 2.1$32.00
- Nemotron VoiceChatFree
- Qwen2.5 32B Instruct$12.60
Which should you choose?
Which is better: Mistral Large 2.1, Nemotron VoiceChat or Qwen2.5 32B Instruct?
Nemotron VoiceChat is the better all-round choice, scoring 65/100 against Qwen2.5 32B Instruct (35) and Mistral Large 2.1 (26). It leads on price and inputs & features. 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 Large 2.1, Nemotron VoiceChat or Qwen2.5 32B Instruct?
Nemotron VoiceChat is cheaper at 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); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral API price). Nemotron VoiceChat is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mistral Large 2.1 has an ECI of 128.5, 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 Mistral Large 2.1, 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?
Mistral Large 2.1 and Qwen2.5 32B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Nemotron VoiceChat. Maximum output per response: Mistral Large 2.1 up to 16,384, Nemotron VoiceChat up to 8,192, Qwen2.5 32B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 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?
Nemotron VoiceChat is the newest, released Mar 16, 2026. Mistral Large 2.1 came out Nov 18, 2024; Qwen2.5 32B Instruct came out Sep 17, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, 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.
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