Llama 3.3 Nemotron Super 49B v1 vs Llama 3.1 Nemotron Ultra 253B vs Voxtral Small 24B 2507
Llama 3.1 Nemotron Ultra 253B comes out ahead, 65 to 60 and 55 on our weighted score, and it is the cheaper option too.
NVIDIA
Llama 3.3 Nemotron Super 49B v1
60/100- ECI—
- Price$0.15 / $0.15
- Context131K
- Our pick
NVIDIA
Llama 3.1 Nemotron Ultra 253B
65/100- ECI—
- PriceFree / Free
- Context128K
Mistral AI
Voxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
Llama 3.1 Nemotron Ultra 253B is our pick
Llama 3.1 Nemotron Ultra 253B is the better all-round choice, scoring 65/100 against Llama 3.3 Nemotron Super 49B v1 (60) and Voxtral Small 24B 2507 (55). It leads 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 3.1 Nemotron Ultra 253BLlama 3.1 Nemotron Ultra 253B Free · Llama 3.3 Nemotron Super 49B v1 $0.15 · Voxtral Small 24B 2507 $0.15 per 1M tokens (3:1 blend)
- Longest contextLlama 3.3 Nemotron Super 49B v1Llama 3.3 Nemotron Super 49B v1 131,072 · Llama 3.1 Nemotron Ultra 253B 128,000 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsVoxtral Small 24B 2507Llama 3.3 Nemotron Super 49B v1: Text · Llama 3.1 Nemotron Ultra 253B: Text · 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 | Llama 3.1 Nemotron Ultra 253B | Voxtral Small 24B 2507 |
|---|---|---|---|---|
| Price | 50% | 89 | 100 | 89 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 60/100 | 65/100 | 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 | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | Free (best) | $0.10 |
| Output | $0.15 | Free (best) | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 | Free (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Nvidia API | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 32,768 tokens |
| Max output | 131,072 tokens (best) | 8,192 tokens | 32,768 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 | 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 | nvidia/llama-3.1-nemotron-ultra-253b-v1 | voxtral-small-latest |
| API providers | 2 | 1 | 7 (best) |
| Released | Apr 7, 2025 | Apr 7, 2025 | Jul 15, 2025 |
| 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.
Llama 3.3 Nemotron Super 49B v1$1.80
Llama 3.1 Nemotron Ultra 253BFree
Voxtral Small 24B 2507$1.60
Which should you choose?
Which is better: Llama 3.3 Nemotron Super 49B v1, Llama 3.1 Nemotron Ultra 253B or Voxtral Small 24B 2507?
Llama 3.1 Nemotron Ultra 253B is the better all-round choice, scoring 65/100 against Llama 3.3 Nemotron Super 49B v1 (60) and Voxtral Small 24B 2507 (55). It leads 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, Llama 3.1 Nemotron Ultra 253B or Voxtral Small 24B 2507?
Llama 3.1 Nemotron Ultra 253B is cheaper at Free input / Free output per million tokens (official Nvidia API price). Llama 3.3 Nemotron Super 49B v1 costs $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). Llama 3.1 Nemotron Ultra 253B is listed as free.
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, Llama 3.1 Nemotron Ultra 253B 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, Llama 3.1 Nemotron Ultra 253B 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 has the largest context window at 131,072 tokens, against 128,000 for Llama 3.1 Nemotron Ultra 253B and 32,768 for Voxtral Small 24B 2507. Maximum output per response: Llama 3.3 Nemotron Super 49B v1 up to 131,072, Llama 3.1 Nemotron Ultra 253B up to 8,192, 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; Llama 3.1 Nemotron Ultra 253B accepts text; Voxtral Small 24B 2507 accepts text and audio. Voxtral Small 24B 2507 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; Llama 3.1 Nemotron Ultra 253B came out Apr 7, 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.