ZMIME
Comparison · 2 models · Updated Nov 2, 2026

Llama-3.2-1B vs Mistral Large 2.1

Llama-3.2-1B comes out ahead, 34 to 27 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Meta

    Llama-3.2-1B

    Released Sep 25, 2024

    34/100
    • ECI—
    • Price$0.027 / $0.201
    • Context131K
    Full specs Remove Llama-3.2-1B

  2. Mistral AI

    Mistral Large 2.1

    Released Nov 18, 2024

    27/100
    • ECI128.6
    • Price$2.00 / $6.00
    • Context131K
    Full specs Remove Mistral Large 2.1

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Llama-3.2-1B is our pick

Llama-3.2-1B is the better all-round choice, scoring 34/100 against Mistral Large 2.1 (27). It leads on price. Mistral Large 2.1 wins on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.

  • CapabilityMistral Large 2.1Shared benchmarks: Mistral Large 2.1 29.6% · Llama-3.2-1B 12.2%
  • Lowest priceLlama-3.2-1BLlama-3.2-1B $0.07 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameLlama-3.2-1B 131,072 · Mistral Large 2.1 131,072 tokens
  • Widest inputsSame inputsLlama-3.2-1B: Text · Mistral Large 2.1: 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 Large 2.1
CapabilityShared benchmarks50%1230
Price25%10026
Inputs & features15%025
Context window10%2525
Overall100%34/10027/100
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 Large 2.1 specifications side by side
SpecificationLlama-3.2-1BMetaMistral Large 2.1Mistral AI
Capability
Capabilities Index (ECI)—128.6
ECI rank—#142 of 165
GPQA DiamondGraduate-level science questions23.9%51.3% (best)
OTIS Mock AIME 2024–2025Competition mathematics0.6%7.8% (best)
Price per million tokens
Input$0.027 (best)$2.00
Output$0.201 (best)$6.00
Cached input——
Blended (3:1)$0.07 (best)$3.00
Long-context rateSame rateSame rate
Price sourceMedian of 6 providersMedian of 2 providers
Limits
Context window131,072 tokens131,072 tokens
Max output8,192 tokens16,384 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 providers6 (best)2
ReleasedSep 25, 2024Nov 18, 2024
Knowledge cutoffDec 2023Nov 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 Large 2.1$32.00
04 — Questions

Which should you choose?

Which is better: Llama-3.2-1B or Mistral Large 2.1?

Llama-3.2-1B is the better all-round choice, scoring 34/100 against Mistral Large 2.1 (27). It leads on price. Mistral Large 2.1 wins on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.

Which is cheaper, Llama-3.2-1B or Mistral Large 2.1?

Llama-3.2-1B is cheaper at $0.027 input / $0.201 output per million tokens (median across 6 API providers). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 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 $3.00 for Mistral Large 2.1 (43× 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 and OTIS Mock AIME 2024–2025): Mistral Large 2.1 29.6% and Llama-3.2-1B 12.2%. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Llama-3.2-1B 23.9%; OTIS Mock AIME 2024–2025 — Mistral Large 2.1 7.8%, Llama-3.2-1B 0.6%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama-3.2-1B and Mistral Large 2.1 yet, so there is no like-for-like coding score. On overall capability, Mistral Large 2.1 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 and Mistral Large 2.1 share the same 131,072-token context window. Maximum output per response: Llama-3.2-1B up to 8,192, Mistral Large 2.1 up to 16,384 tokens.

Which can read images, PDFs, audio or video?

Llama-3.2-1B accepts text; Mistral Large 2.1 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?

Mistral Large 2.1 is the newest, released Nov 18, 2024. Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Mistral Large 2.1 Nov 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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