ZMIME
Comparison · 2 models · Updated Nov 2, 2026

Llama-3.2-1B vs Mistral Nemo

Mistral Nemo comes out ahead, 43 to 39 on our weighted score, though Llama-3.2-1B is 43% cheaper per token.

  1. Meta

    Llama-3.2-1B

    Released Sep 25, 2024

    39/100
    • ECI—
    • Price$0.027 / $0.201
    • Context131K
    Full specs Remove Llama-3.2-1B
  2. Our pick

    Mistral AI

    Mistral Nemo

    Released Jul 18, 2024

    43/100
    • ECI118.6
    • Price$0.12 / $0.13
    • Context128K
    Full specs Remove Mistral Nemo

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Mistral Nemo is our pick

Mistral Nemo is the better all-round choice, scoring 43/100 against Llama-3.2-1B (39). It leads on capability and inputs & features. Llama-3.2-1B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond), because Llama-3.2-1B has no Capabilities Index score yet.

  • CapabilityMistral NemoShared benchmarks: Mistral Nemo 29.9% · Llama-3.2-1B 23.9%
  • Lowest priceLlama-3.2-1BLlama-3.2-1B $0.07 · Mistral Nemo $0.123 per 1M tokens (3:1 blend)
  • Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Mistral Nemo 128,000 tokens
  • Widest inputsSame inputsLlama-3.2-1B: Text · Mistral Nemo: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightLlama-3.2-1BMistral Nemo
CapabilityShared benchmarks50%2430
Price25%10089
Inputs & features15%025
Context window10%2524
Overall100%39/10043/100
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 Mistral Nemo specifications side by side
SpecificationLlama-3.2-1BMetaMistral NemoMistral AI
Capability
Capabilities Index (ECI)—118.6
ECI rank—#157 of 165
GPQA DiamondGraduate-level science questions23.9%29.9% (best)
OTIS Mock AIME 2024–2025Competition mathematics0.6%—
Price per million tokens
Input$0.027 (best)$0.12
Output$0.201$0.13 (best)
Cached input——
Blended (3:1)$0.07 (best)$0.123
Long-context rateSame rateSame rate
Price sourceMedian of 6 providersMedian of 12 providers
Limits
Context window131,072 tokens (best)128,000 tokens
Max output8,192 tokens128,000 tokens (best)
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingNoYes
Structured outputNoNo
Availability
WeightsOpenLlama 3.2 Community LicenseOpen
API model ID——
API providers612 (best)
ReleasedSep 25, 2024Jul 18, 2024
Knowledge cutoffDec 2023Jul 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-3.2-1B$0.672
  • Mistral Nemo$1.46
04 — Questions

Which should you choose?

Which is better: Llama-3.2-1B or Mistral Nemo?

Mistral Nemo is the better all-round choice, scoring 43/100 against Llama-3.2-1B (39). It leads on capability and inputs & features. Llama-3.2-1B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond), because Llama-3.2-1B has no Capabilities Index score yet.

Which is cheaper, Llama-3.2-1B or Mistral Nemo?

Llama-3.2-1B is cheaper at $0.027 input / $0.201 output per million tokens (median across 6 API providers). Mistral Nemo costs $0.12 input / $0.13 output per million tokens (median across 12 API providers). At a typical mix of three input tokens to one output token, that is $0.07 per million tokens for Llama-3.2-1B versus $0.123 for Mistral Nemo (1.7× as much).

Which scores higher on benchmarks?

Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond): Mistral Nemo 29.9% and Llama-3.2-1B 23.9%. On individual benchmarks: GPQA Diamond — Mistral Nemo 29.9%, Llama-3.2-1B 23.9%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama-3.2-1B and Mistral Nemo yet, so there is no like-for-like coding score. On overall capability, Mistral Nemo leads, which tends to carry over to coding, but test on your own codebase. 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 has the largest context window at 131,072 tokens, against 128,000 for Mistral Nemo. Maximum output per response: Llama-3.2-1B up to 8,192, Mistral Nemo up to 128,000 tokens.

Which can read images, PDFs, audio or video?

Llama-3.2-1B accepts text; Mistral Nemo accepts text. They handle the same number of input types.

Are any of these open source?

Yes, both publish their weights (Llama 3.2 Community License), so you can self-host them.

Which is newer?

Llama-3.2-1B is the newest, released Sep 25, 2024. Mistral Nemo came out Jul 18, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Mistral Nemo Jul 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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