Saba vs Llama 3.3 Nemotron Super 49B v1
Llama 3.3 Nemotron Super 49B v1 comes out ahead, 58 to 46 on our weighted score, and it is the cheaper option too.
Mistral AISaba
46/100- ECI—
- Price$0.20 / $0.60
- Context33K
- Our pick
NVIDIALlama 3.3 Nemotron Super 49B v1
58/100- ECI—
- Price$0.15 / $0.15
- Context131K
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Llama 3.3 Nemotron Super 49B v1 is our pick
Llama 3.3 Nemotron Super 49B v1 is the better all-round choice, scoring 58/100 against Saba (46). It leads on price 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 · Saba $0.30 per 1M tokens (3:1 blend)
- Longest contextLlama 3.3 Nemotron Super 49B v1Llama 3.3 Nemotron Super 49B v1 131,072 · Saba 32,768 tokens
- Widest inputsSabaSaba: Text, PDFs · Llama 3.3 Nemotron Super 49B v1: Text
- Self-hostingLlama 3.3 Nemotron Super 49B v1Publishes downloadable weights
| Measure | Weight | Saba | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|---|
| Price | 50% | 72 | 85 |
| Inputs & features | 30% | 35 | 35 |
| Context window | 20% | 0 | 25 |
| Overall | 100% | 46/100 | 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 | SabaMistral AI | Llama 3.3 Nemotron Super 49B v1NVIDIA |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.20 | $0.15 (best) |
| Output | $0.60 | $0.15 (best) |
| Cached input | — | — |
| Blended (3:1) | $0.30 | $0.15 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 1 providers |
| Limits | ||
| Context window | 32,768 tokens | 131,072 tokens (best) |
| Max output | 26,214 tokens | 131,072 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | Yes | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | — | — |
| API providers | 4 (best) | 1 |
| Released | Feb 17, 2025 | Mar 18, 2025 |
| Knowledge cutoff | Sep 30, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Saba$3.20
- Llama 3.3 Nemotron Super 49B v1$1.80
Which should you choose?
Which is better: Saba or Llama 3.3 Nemotron Super 49B v1?
Llama 3.3 Nemotron Super 49B v1 is the better all-round choice, scoring 58/100 against Saba (46). It leads on price 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, Saba 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). Saba costs $0.20 input / $0.60 output per million tokens (median across 4 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 $0.30 for Saba (2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Saba 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 Saba 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?
Llama 3.3 Nemotron Super 49B v1 has the largest context window at 131,072 tokens, against 32,768 for Saba. Maximum output per response: Saba up to 26,214, Llama 3.3 Nemotron Super 49B v1 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Saba accepts text and PDFs; Llama 3.3 Nemotron Super 49B v1 accepts text. Saba handles the widest range of inputs.
Are any of these open source?
Llama 3.3 Nemotron Super 49B v1 publishes its weights and can be self-hosted; Saba is proprietary.
Which is newer?
Llama 3.3 Nemotron Super 49B v1 is the newest, released Mar 18, 2025. Saba came out Feb 17, 2025. Knowledge cutoff: Saba Sep 30, 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 Nov 2, 2026.
