Mistral Small 3.2 24B vs Llama 3.3 Nemotron Super 49B v1
Mistral Small 3.2 24B comes out ahead, 65 to 58 on our weighted score.
- Our pick
Mistral AIMistral Small 3.2 24B
65/100- ECI—
- Price$0.10 / $0.305
- Context256K
NVIDIALlama 3.3 Nemotron Super 49B v1
58/100- ECI—
- Price$0.15 / $0.15
- Context131K
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Make it a three-way comparison.
Mistral Small 3.2 24B is our pick
Mistral Small 3.2 24B is the better all-round choice, scoring 65/100 against Llama 3.3 Nemotron Super 49B v1 (58). It leads on inputs & features and context window. 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 v1Llama 3.3 Nemotron Super 49B v1 $0.15 · Mistral Small 3.2 24B $0.151 per 1M tokens (3:1 blend)
- Longest contextMistral Small 3.2 24BMistral Small 3.2 24B 256,000 · Llama 3.3 Nemotron Super 49B v1 131,072 tokens
- Widest inputsMistral Small 3.2 24BMistral Small 3.2 24B: Text, Images · Llama 3.3 Nemotron Super 49B v1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Small 3.2 24B | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|---|
| Price | 50% | 85 | 85 |
| Inputs & features | 30% | 50 | 35 |
| Context window | 20% | 36 | 25 |
| Overall | 100% | 65/100 | 58/100 |
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 Small 3.2 24BMistral AI | Llama 3.3 Nemotron Super 49B v1NVIDIA |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.10 (best) | $0.15 |
| Output | $0.305 | $0.15 (best) |
| Cached input | — | — |
| Blended (3:1) | $0.151 | $0.15 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 8 providers | Median of 1 providers |
| Limits | ||
| Context window | 256,000 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens | 131,072 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | — | — |
| API providers | 8 (best) | 1 |
| Released | Jun 20, 2025 | Mar 18, 2025 |
| Knowledge cutoff | Oct 31, 2023 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mistral Small 3.2 24B$1.61
- Llama 3.3 Nemotron Super 49B v1$1.80
Which should you choose?
Which is better: Mistral Small 3.2 24B or Llama 3.3 Nemotron Super 49B v1?
Mistral Small 3.2 24B is the better all-round choice, scoring 65/100 against Llama 3.3 Nemotron Super 49B v1 (58). It leads on inputs & features and context window. 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 Small 3.2 24B or Llama 3.3 Nemotron Super 49B v1?
Llama 3.3 Nemotron Super 49B v1 is cheaper at $0.15 input / $0.15 output per million tokens (median across 1 API provider). Mistral Small 3.2 24B costs $0.10 input / $0.305 output per million tokens (median across 8 API providers). 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 $0.151 for Mistral Small 3.2 24B (1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Mistral Small 3.2 24B has not been scored yet and Llama 3.3 Nemotron Super 49B v1 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Small 3.2 24B and Llama 3.3 Nemotron Super 49B v1 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Mistral Small 3.2 24B has the largest context window at 256,000 tokens, against 131,072 for Llama 3.3 Nemotron Super 49B v1. Maximum output per response: Mistral Small 3.2 24B up to 16,384, Llama 3.3 Nemotron Super 49B v1 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Mistral Small 3.2 24B accepts text and images; Llama 3.3 Nemotron Super 49B v1 accepts text. Mistral Small 3.2 24B handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
Mistral Small 3.2 24B is the newest, released Jun 20, 2025. Llama 3.3 Nemotron Super 49B v1 came out Mar 18, 2025. Knowledge cutoff: Mistral Small 3.2 24B Oct 31, 2023.
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 Nov 2, 2026.
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