Phi-4-mini vs Llama 3.3 Nemotron Super 49B v1
Too close to call on our weighted score (Llama 3.3 Nemotron Super 49B v1 58, Phi-4-mini 56). The right pick depends on what you value most.
MicrosoftPhi-4-mini
56/100- ECI—
- Price$0.075 / $0.30
- Context128K
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.
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), 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 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 tokens
- Widest inputsSame inputsPhi-4-mini: Text · 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 | Phi-4-mini | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|---|
| Price | 50% | 88 | 85 |
| Inputs & features | 30% | 25 | 35 |
| Context window | 20% | 24 | 25 |
| Overall | 100% | 56/100 | 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 | Phi-4-miniMicrosoft | Llama 3.3 Nemotron Super 49B v1NVIDIA |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.075 (best) | $0.15 |
| Output | $0.30 | $0.15 (best) |
| Cached input | — | — |
| Blended (3:1) | $0.131 (best) | $0.15 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Azure API | Median of 1 providers |
| Limits | ||
| Context window | 128,000 tokens | 131,072 tokens (best) |
| Max output | 4,096 tokens | 131,072 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | 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 | phi-4-mini | — |
| API providers | 2 (best) | 1 |
| Released | Feb 26, 2025 | Mar 18, 2025 |
| Knowledge cutoff | 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.
- Phi-4-mini$1.35
- Llama 3.3 Nemotron Super 49B v1$1.80
Which should you choose?
Which is better: Phi-4-mini or Llama 3.3 Nemotron Super 49B v1?
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), 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, Phi-4-mini or Llama 3.3 Nemotron Super 49B v1?
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). 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).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Phi-4-mini 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 Phi-4-mini 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 128,000 for Phi-4-mini. Maximum output per response: Phi-4-mini up to 4,096, Llama 3.3 Nemotron Super 49B v1 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Phi-4-mini accepts text; Llama 3.3 Nemotron Super 49B v1 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
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. Knowledge cutoff: 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.
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