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Comparison · 3 models · Updated Nov 2, 2026

Llama 3.3 Nemotron Super 49B v1 vs Gemini 2.5 Flash-Lite vs Phi-4-mini

Gemini 2.5 Flash-Lite comes out ahead, 83 to 58 and 56 on our weighted score, though Phi-4-mini is 25% cheaper per token.

  1. NVIDIA

    Llama 3.3 Nemotron Super 49B v1

    Released Mar 18, 2025

    58/100
    • ECI—
    • Price$0.15 / $0.15
    • Context131K
    Full specs Remove Llama 3.3 Nemotron Super 49B v1
  2. Our pick

    Google

    Gemini 2.5 Flash-Lite

    Released Jun 17, 2025

    83/100
    • ECI—
    • Price$0.10 / $0.40
    • Context1.05M
    Full specs Remove Gemini 2.5 Flash-Lite

  3. Microsoft

    Phi-4-mini

    Released Feb 26, 2025

    56/100
    • ECI—
    • Price$0.075 / $0.30
    • Context128K
    Full specs Remove Phi-4-mini
01 — Verdict

Gemini 2.5 Flash-Lite is our pick

Gemini 2.5 Flash-Lite is the better all-round choice, scoring 83/100 against Llama 3.3 Nemotron Super 49B v1 (58) and Phi-4-mini (56). It leads on inputs & features and context window. Phi-4-mini wins on price. 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 · Gemini 2.5 Flash-Lite $0.175 per 1M tokens (3:1 blend)
  • Longest contextGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite 1,048,576 · Llama 3.3 Nemotron Super 49B v1 131,072 · Phi-4-mini 128,000 tokens
  • Widest inputsGemini 2.5 Flash-LiteLlama 3.3 Nemotron Super 49B v1: Text · Gemini 2.5 Flash-Lite: Text, Images, PDFs, Audio, Video · Phi-4-mini: Text
  • Self-hostingLlama 3.3 Nemotron Super 49B v1 and Phi-4-miniPublishes downloadable weights
How the score is built
MeasureWeightLlama 3.3 Nemotron Super 49B v1Gemini 2.5 Flash-LitePhi-4-mini
Price50%858288
Inputs & features30%3510025
Context window20%256124
Overall100%58/10083/10056/100

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 Gemini 2.5 Flash-Lite vs Phi-4-mini specifications side by side
SpecificationLlama 3.3 Nemotron Super 49B v1NVIDIAGemini 2.5 Flash-LiteGooglePhi-4-miniMicrosoft
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.15$0.10$0.075 (best)
Output$0.15 (best)$0.40$0.30
Cached input—$0.01—
Blended (3:1)$0.15$0.175$0.131 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 1 providersOfficial Google APIOfficial Azure API
Limits
Context window131,072 tokens1,048,576 tokens (best)128,000 tokens
Max output131,072 tokens (best)65,536 tokens4,096 tokens
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoYesNo
AudioNoYesNo
VideoNoYesNo
ReasoningYesYesNo
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsOpenProprietaryOpen
API model ID—gemini-2.5-flash-litephi-4-mini
API providers124 (best)2
ReleasedMar 18, 2025Jun 17, 2025Feb 26, 2025
Knowledge cutoff—Jan 2025Jun 2024
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
  • Gemini 2.5 Flash-Lite$1.80
  • Phi-4-mini$1.35
04 — Questions

Which should you choose?

Which is better: Llama 3.3 Nemotron Super 49B v1, Gemini 2.5 Flash-Lite or Phi-4-mini?

Gemini 2.5 Flash-Lite is the better all-round choice, scoring 83/100 against Llama 3.3 Nemotron Super 49B v1 (58) and Phi-4-mini (56). It leads on inputs & features and context window. Phi-4-mini wins on price. 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, Gemini 2.5 Flash-Lite 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); Gemini 2.5 Flash-Lite costs $0.10 input / $0.40 output per million tokens (official Google API price). 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 $0.175 for Gemini 2.5 Flash-Lite (1.3× 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, Gemini 2.5 Flash-Lite 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, Gemini 2.5 Flash-Lite 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?

Gemini 2.5 Flash-Lite has the largest context window at 1,048,576 tokens, against 131,072 for Llama 3.3 Nemotron Super 49B v1 and 128,000 for Phi-4-mini. Maximum output per response: Llama 3.3 Nemotron Super 49B v1 up to 131,072, Gemini 2.5 Flash-Lite up to 65,536, 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; Gemini 2.5 Flash-Lite accepts text, images, PDFs, audio and video; Phi-4-mini accepts text. Gemini 2.5 Flash-Lite 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; Gemini 2.5 Flash-Lite is proprietary.

Which is newer?

Gemini 2.5 Flash-Lite is the newest, released Jun 17, 2025. Llama 3.3 Nemotron Super 49B v1 came out Mar 18, 2025; Phi-4-mini came out Feb 26, 2025. Knowledge cutoff: Gemini 2.5 Flash-Lite Jan 2025, 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.

05 — Keep comparing

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