Llama 3.3 Nemotron Super 49B v1 vs Qwen3-VL 30B-A3B vs Voxtral Small 24B 2507
Too close to call on our weighted score (Llama 3.3 Nemotron Super 49B v1 60, Qwen3-VL 30B-A3B 59, Voxtral Small 24B 2507 55). The right pick depends on what you value most.
NVIDIALlama 3.3 Nemotron Super 49B v1
60/100- ECI—
- Price$0.15 / $0.15
- Context131K
Alibaba (Qwen)Qwen3-VL 30B-A3B
59/100- ECI—
- Price$0.20 / $0.80
- Context131K
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
Too close to call
It is close. Our weighted score puts them within 1 points (Llama 3.3 Nemotron Super 49B v1 60/100, Qwen3-VL 30B-A3B 59/100, Voxtral Small 24B 2507 55/100), so choose by what matters most for your work: Llama 3.3 Nemotron Super 49B v1 on price. 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 priceLlama 3.3 Nemotron Super 49B v1 and Voxtral Small 24B 2507Llama 3.3 Nemotron Super 49B v1 $0.15 · Voxtral Small 24B 2507 $0.15 · Qwen3-VL 30B-A3B $0.35 per 1M tokens (3:1 blend)
- Longest contextLlama 3.3 Nemotron Super 49B v1 and Qwen3-VL 30B-A3BLlama 3.3 Nemotron Super 49B v1 131,072 · Qwen3-VL 30B-A3B 131,072 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsQwen3-VL 30B-A3B and Voxtral Small 24B 2507Llama 3.3 Nemotron Super 49B v1: Text · Qwen3-VL 30B-A3B: Text, Images · Voxtral Small 24B 2507: Text, Audio
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama 3.3 Nemotron Super 49B v1 | Qwen3-VL 30B-A3B | Voxtral Small 24B 2507 |
|---|---|---|---|---|
| Price | 50% | 89 | 72 | 89 |
| Inputs & features | 30% | 35 | 60 | 35 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 60/100 | 59/100 | 55/100 |
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 | Llama 3.3 Nemotron Super 49B v1NVIDIA | Qwen3-VL 30B-A3BAlibaba (Qwen) | Voxtral Small 24B 2507Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | $0.20 | $0.10 (best) |
| Output | $0.15 (best) | $0.80 | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 (best) | $0.35 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Alibaba API | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 32,768 tokens |
| Max output | 131,072 tokens (best) | 32,768 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenApache 2.0 |
| API model ID | nvidia/llama-3.3-nemotron-super-49b-v1 | qwen3-vl-30b-a3b | voxtral-small-latest |
| API providers | 2 | 1 | 7 (best) |
| Released | Apr 7, 2025 | Apr 2025 | Jul 15, 2025 |
| Knowledge cutoff | — | Apr 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama 3.3 Nemotron Super 49B v1$1.80
- Qwen3-VL 30B-A3B$3.60
- Voxtral Small 24B 2507$1.60
Which should you choose?
Which is better: Llama 3.3 Nemotron Super 49B v1, Qwen3-VL 30B-A3B or Voxtral Small 24B 2507?
It is close. Our weighted score puts them within 1 points (Llama 3.3 Nemotron Super 49B v1 60/100, Qwen3-VL 30B-A3B 59/100, Voxtral Small 24B 2507 55/100), so choose by what matters most for your work: Llama 3.3 Nemotron Super 49B v1 on price. 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, Llama 3.3 Nemotron Super 49B v1, Qwen3-VL 30B-A3B or Voxtral Small 24B 2507?
Llama 3.3 Nemotron Super 49B v1 is cheaper at $0.15 input / $0.15 output per million tokens (median across 1 API provider; free on Nvidia). Voxtral Small 24B 2507 costs $0.10 input / $0.30 output per million tokens (official Mistral API price); Qwen3-VL 30B-A3B costs $0.20 input / $0.80 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Llama 3.3 Nemotron Super 49B v1 versus $0.15 for Voxtral Small 24B 2507 (1× as much) and $0.35 for Qwen3-VL 30B-A3B (2.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama 3.3 Nemotron Super 49B v1 has not been scored yet, Qwen3-VL 30B-A3B has not been scored yet and Voxtral Small 24B 2507 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama 3.3 Nemotron Super 49B v1, Qwen3-VL 30B-A3B and Voxtral Small 24B 2507 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?
Llama 3.3 Nemotron Super 49B v1 and Qwen3-VL 30B-A3B have the largest context windows (131,072 and 131,072 tokens), against 32,768 for Voxtral Small 24B 2507. Maximum output per response: Llama 3.3 Nemotron Super 49B v1 up to 131,072, Qwen3-VL 30B-A3B up to 32,768, Voxtral Small 24B 2507 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Llama 3.3 Nemotron Super 49B v1 accepts text; Qwen3-VL 30B-A3B accepts text and images; Voxtral Small 24B 2507 accepts text and audio. Qwen3-VL 30B-A3B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Voxtral Small 24B 2507 is the newest, released Jul 15, 2025. Llama 3.3 Nemotron Super 49B v1 came out Apr 7, 2025; Qwen3-VL 30B-A3B came out Apr 2025. Knowledge cutoff: Qwen3-VL 30B-A3B Apr 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.
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