Llama 3.3 Nemotron Super 49B v1 vs Saba vs Phi-4-mini
Too close to call on our weighted score (Llama 3.3 Nemotron Super 49B v1 58, Phi-4-mini 56, Saba 46). The right pick depends on what you value most.
NVIDIALlama 3.3 Nemotron Super 49B v1
58/100- ECI—
- Price$0.15 / $0.15
- Context131K
Mistral AISaba
46/100- ECI—
- Price$0.20 / $0.60
- Context33K
MicrosoftPhi-4-mini
56/100- ECI—
- Price$0.075 / $0.30
- Context128K
Too close to call
It is close. Our weighted score puts them within 2 points (Llama 3.3 Nemotron Super 49B v1 58/100, Phi-4-mini 56/100, Saba 46/100), so choose by what matters most for your work: Phi-4-mini on price and Llama 3.3 Nemotron Super 49B v1 for long inputs. 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 pricePhi-4-miniPhi-4-mini $0.131 · Llama 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 · Phi-4-mini 128,000 · Saba 32,768 tokens
- Widest inputsSabaLlama 3.3 Nemotron Super 49B v1: Text · Saba: Text, PDFs · Phi-4-mini: Text
- Self-hostingLlama 3.3 Nemotron Super 49B v1 and Phi-4-miniPublishes downloadable weights
| Measure | Weight | Llama 3.3 Nemotron Super 49B v1 | Saba | Phi-4-mini |
|---|---|---|---|---|
| Price | 50% | 85 | 72 | 88 |
| Inputs & features | 30% | 35 | 35 | 25 |
| Context window | 20% | 25 | 0 | 24 |
| Overall | 100% | 58/100 | 46/100 | 56/100 |
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 | SabaMistral AI | Phi-4-miniMicrosoft |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | $0.20 | $0.075 (best) |
| Output | $0.15 (best) | $0.60 | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 | $0.30 | $0.131 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 4 providers | Official Azure API |
| Limits | |||
| Context window | 131,072 tokens (best) | 32,768 tokens | 128,000 tokens |
| Max output | 131,072 tokens (best) | 26,214 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | — | phi-4-mini |
| API providers | 1 | 4 (best) | 2 |
| Released | Mar 18, 2025 | Feb 17, 2025 | Feb 26, 2025 |
| Knowledge cutoff | — | Sep 30, 2024 | Jun 2024 |
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
- Saba$3.20
- Phi-4-mini$1.35
Which should you choose?
Which is better: Llama 3.3 Nemotron Super 49B v1, Saba or Phi-4-mini?
It is close. Our weighted score puts them within 2 points (Llama 3.3 Nemotron Super 49B v1 58/100, Phi-4-mini 56/100, Saba 46/100), so choose by what matters most for your work: Phi-4-mini on price and Llama 3.3 Nemotron Super 49B v1 for long inputs. 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, Saba or Phi-4-mini?
Phi-4-mini is cheaper at $0.075 input / $0.30 output per million tokens (official Azure API price). Llama 3.3 Nemotron Super 49B v1 costs $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 $0.131 per million tokens for Phi-4-mini versus $0.15 for Llama 3.3 Nemotron Super 49B v1 (1.1× as much) and $0.30 for Saba (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, Saba has not been scored yet and Phi-4-mini 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, Saba and Phi-4-mini 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 Phi-4-mini and 32,768 for Saba. Maximum output per response: Llama 3.3 Nemotron Super 49B v1 up to 131,072, Saba up to 26,214, Phi-4-mini up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Llama 3.3 Nemotron Super 49B v1 accepts text; Saba accepts text and PDFs; Phi-4-mini accepts text. Saba handles the widest range of inputs.
Are any of these open source?
Llama 3.3 Nemotron Super 49B v1 and Phi-4-mini 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. Phi-4-mini came out Feb 26, 2025; Saba came out Feb 17, 2025. Knowledge cutoff: Saba Sep 30, 2024, Phi-4-mini Jun 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.
