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Comparison · 2 models · Updated Oct 4, 2026

Llama 3.3 Nemotron Super 49B v1 vs Phi-4-mini

Too close to call on our weighted score (Llama 3.3 Nemotron Super 49B v1 60, Phi-4-mini 58). The right pick depends on what you value most.

  1. NVIDIA

    Llama 3.3 Nemotron Super 49B v1

    Released Apr 7, 2025Deprecated

    60/100
    • ECI—
    • Price$0.15 / $0.15
    • Context131K
    Full specs Remove Llama 3.3 Nemotron Super 49B v1

  2. Microsoft

    Phi-4-mini

    Released Dec 11, 2024

    58/100
    • ECI—
    • Price$0.075 / $0.30
    • Context128K
    Full specs Remove Phi-4-mini

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Too close to call

It is close. Our weighted score puts them within 2 points (Llama 3.3 Nemotron Super 49B v1 60/100, Phi-4-mini 58/100), 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 inputsLlama 3.3 Nemotron Super 49B v1: Text · Phi-4-mini: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightLlama 3.3 Nemotron Super 49B v1Phi-4-mini
Price50%8992
Inputs & features30%3525
Context window20%2424
Overall100%60/10058/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Llama 3.3 Nemotron Super 49B v1 vs Phi-4-mini specifications side by side
SpecificationLlama 3.3 Nemotron Super 49B v1NVIDIAPhi-4-miniMicrosoft
Capability
Capabilities Index (ECI)——
ECI rank——
Price per million tokens
Input$0.15$0.075 (best)
Output$0.15 (best)$0.30
Cached input——
Blended (3:1)$0.15$0.131 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 1 providersOfficial Azure API
Limits
Context window131,072 tokens (best)128,000 tokens
Max output131,072 tokens (best)4,096 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesNo
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpen
API model IDnvidia/llama-3.3-nemotron-super-49b-v1phi-4-mini
API providers2 (best)1
ReleasedApr 7, 2025Dec 11, 2024
Knowledge cutoff—Oct 2023
03 — Cost

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
  • Phi-4-mini$1.35
04 — Questions

Which should you choose?

Which is better: Llama 3.3 Nemotron Super 49B v1 or Phi-4-mini?

It is close. Our weighted score puts them within 2 points (Llama 3.3 Nemotron Super 49B v1 60/100, Phi-4-mini 58/100), 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 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; free on Nvidia). 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. Llama 3.3 Nemotron Super 49B v1 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 and Phi-4-mini 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: Llama 3.3 Nemotron Super 49B v1 up to 131,072, 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; Phi-4-mini 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 Apr 7, 2025. Phi-4-mini came out Dec 11, 2024. Knowledge cutoff: Phi-4-mini Oct 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 Oct 4, 2026.

05 — Keep comparing

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