ZMIME
Comparison · 2 models · Updated Nov 2, 2026

Mistral Nemo vs Llama-3.2-1B

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

  1. 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

  2. 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

  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 inputsMistral Nemo: Text · Llama-3.2-1B: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightMistral NemoLlama-3.2-1B
CapabilityShared benchmarks50%3024
Price25%89100
Inputs & features15%250
Context window10%2425
Overall100%43/10039/100
02 — Side by side

Every spec in one table

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

Mistral Nemo vs Llama-3.2-1B specifications side by side
SpecificationMistral NemoMistral AILlama-3.2-1BMeta
Capability
Capabilities Index (ECI)118.6—
ECI rank#157 of 165—
GPQA DiamondGraduate-level science questions29.9% (best)23.9%
OTIS Mock AIME 2024–2025Competition mathematics—0.6%
Price per million tokens
Input$0.12$0.027 (best)
Output$0.13 (best)$0.201
Cached input——
Blended (3:1)$0.123$0.07 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 12 providersMedian of 6 providers
Limits
Context window128,000 tokens131,072 tokens (best)
Max output128,000 tokens (best)8,192 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingYesNo
Structured outputNoNo
Availability
WeightsOpenOpenLlama 3.2 Community License
API model ID——
API providers12 (best)6
ReleasedJul 18, 2024Sep 25, 2024
Knowledge cutoffJul 2024Dec 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.

  • Mistral Nemo$1.46
  • Llama-3.2-1B$0.672
04 — Questions

Which should you choose?

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

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, Mistral Nemo or Llama-3.2-1B?

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 Mistral Nemo and Llama-3.2-1B 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: Mistral Nemo up to 128,000, Llama-3.2-1B up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Mistral Nemo accepts text; Llama-3.2-1B 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: Mistral Nemo Jul 2024, Llama-3.2-1B Dec 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 Nov 2, 2026.

05 — Keep comparing

Related comparisons