ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Llama-3.2-1B vs Ministral 8B Instruct vs Phi-4-mini

Too close to call on our weighted score (Phi-4-mini 58, Ministral 8B Instruct 57, Llama-3.2-1B 55). The right pick depends on what you value most.

  1. Meta

    Llama-3.2-1B

    Released Sep 25, 2024

    55/100
    • ECI102.0
    • Price$0.064 / $0.15
    • Context131K
    Full specs Remove Llama-3.2-1B

  2. Mistral AI

    Ministral 8B Instruct

    Released Oct 16, 2024

    57/100
    • ECI—
    • Price$0.15 / $0.15
    • Context131K
    Full specs Remove Ministral 8B Instruct

  3. Microsoft

    Phi-4-mini

    Released Dec 11, 2024

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

Too close to call

It is close. Our weighted score puts them within 1 points (Phi-4-mini 58/100, Ministral 8B Instruct 57/100, Llama-3.2-1B 55/100), so choose by what matters most for your work: Llama-3.2-1B 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 priceLlama-3.2-1BLlama-3.2-1B $0.085 · Phi-4-mini $0.131 · Ministral 8B Instruct $0.15 per 1M tokens (3:1 blend)
  • Longest contextLlama-3.2-1B and Ministral 8B InstructLlama-3.2-1B 131,072 · Ministral 8B Instruct 131,072 · Phi-4-mini 128,000 tokens
  • Widest inputsSame inputsLlama-3.2-1B: Text · Ministral 8B Instruct: 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-3.2-1BMinistral 8B InstructPhi-4-mini
Price50%1008992
Inputs & features30%02525
Context window20%242424
Overall100%55/10057/10058/100

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-3.2-1B vs Ministral 8B Instruct vs Phi-4-mini specifications side by side
SpecificationLlama-3.2-1BMetaMinistral 8B InstructMistral AIPhi-4-miniMicrosoft
Capability
Capabilities Index (ECI)102.0——
ECI rank#147 of 148——
GPQA DiamondGraduate-level science questions23.9%27.2% (best)—
OTIS Mock AIME 2024–2025Competition mathematics0.6%——
Price per million tokens
Input$0.064 (best)$0.15$0.075
Output$0.15$0.15 (best)$0.30
Cached input———
Blended (3:1)$0.085 (best)$0.15$0.131
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 2 providersMedian of 1 providersOfficial Azure API
Limits
Context window131,072 tokens (best)131,072 tokens (best)128,000 tokens
Max output8,192 tokens (best)8,192 tokens (best)4,096 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingNoYesYes
Structured outputNoNoNo
Availability
WeightsOpenLlama 3.2 Community LicenseOpenMistral Research LicenseOpen
API model ID——phi-4-mini
API providers2 (best)11
ReleasedSep 25, 2024Oct 16, 2024Dec 11, 2024
Knowledge cutoffDec 2023—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-3.2-1B$0.936
  • Ministral 8B Instruct$1.80
  • Phi-4-mini$1.35
04 — Questions

Which should you choose?

Which is better: Llama-3.2-1B, Ministral 8B Instruct or Phi-4-mini?

It is close. Our weighted score puts them within 1 points (Phi-4-mini 58/100, Ministral 8B Instruct 57/100, Llama-3.2-1B 55/100), so choose by what matters most for your work: Llama-3.2-1B 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-3.2-1B, Ministral 8B Instruct or Phi-4-mini?

Llama-3.2-1B is cheaper at $0.064 input / $0.15 output per million tokens (median across 2 API providers). Phi-4-mini costs $0.075 input / $0.30 output per million tokens (official Azure API price); Ministral 8B Instruct 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 $0.085 per million tokens for Llama-3.2-1B versus $0.131 for Phi-4-mini (1.5× as much) and $0.15 for Ministral 8B Instruct (1.8× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Llama-3.2-1B has an ECI of 102.0, Ministral 8B Instruct 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-3.2-1B, Ministral 8B Instruct and Phi-4-mini yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.

Which has the bigger context window?

Llama-3.2-1B and Ministral 8B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Phi-4-mini. Maximum output per response: Llama-3.2-1B up to 8,192, Ministral 8B Instruct up to 8,192, Phi-4-mini up to 4,096 tokens.

Which can read images, PDFs, audio or video?

Llama-3.2-1B accepts text; Ministral 8B Instruct accepts text; Phi-4-mini accepts text. They handle the same number of input types.

Are any of these open source?

Yes, all three publish their weights (Llama 3.2 Community License and Mistral Research License), so you can self-host them.

Which is newer?

Phi-4-mini is the newest, released Dec 11, 2024. Ministral 8B Instruct came out Oct 16, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, 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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