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Comparison · 3 models · Updated Nov 2, 2026

Gemini 2.5 Flash-Lite vs Llama 3.3 Nemotron Super 49B v1 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. 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

  2. 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

  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-LiteGemini 2.5 Flash-Lite: Text, Images, PDFs, Audio, Video · Llama 3.3 Nemotron Super 49B v1: Text · Phi-4-mini: Text
  • Self-hostingLlama 3.3 Nemotron Super 49B v1 and Phi-4-miniPublishes downloadable weights
How the score is built
MeasureWeightGemini 2.5 Flash-LiteLlama 3.3 Nemotron Super 49B v1Phi-4-mini
Price50%828588
Inputs & features30%1003525
Context window20%612524
Overall100%83/10058/10056/100

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.

Gemini 2.5 Flash-Lite vs Llama 3.3 Nemotron Super 49B v1 vs Phi-4-mini specifications side by side
SpecificationGemini 2.5 Flash-LiteGoogleLlama 3.3 Nemotron Super 49B v1NVIDIAPhi-4-miniMicrosoft
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.10$0.15$0.075 (best)
Output$0.40$0.15 (best)$0.30
Cached input$0.01——
Blended (3:1)$0.175$0.15$0.131 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Google APIMedian of 1 providersOfficial Azure API
Limits
Context window1,048,576 tokens (best)131,072 tokens128,000 tokens
Max output65,536 tokens131,072 tokens (best)4,096 tokens
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsYesNoNo
AudioYesNoNo
VideoYesNoNo
ReasoningYesYesNo
Tool callingYesYesYes
Structured outputYesNoNo
Availability
WeightsProprietaryOpenOpen
API model IDgemini-2.5-flash-lite—phi-4-mini
API providers24 (best)12
ReleasedJun 17, 2025Mar 18, 2025Feb 26, 2025
Knowledge cutoffJan 2025—Jun 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.

  • Gemini 2.5 Flash-Lite$1.80
  • Llama 3.3 Nemotron Super 49B v1$1.80
  • Phi-4-mini$1.35
04 — Questions

Which should you choose?

Which is better: Gemini 2.5 Flash-Lite, Llama 3.3 Nemotron Super 49B v1 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, Gemini 2.5 Flash-Lite, 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); 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. Gemini 2.5 Flash-Lite has not been scored 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 Gemini 2.5 Flash-Lite, 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. 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: Gemini 2.5 Flash-Lite up to 65,536, 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?

Gemini 2.5 Flash-Lite accepts text, images, PDFs, audio and video; Llama 3.3 Nemotron Super 49B v1 accepts text; 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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