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Comparison · 2 models · Updated Nov 2, 2026

Llama-3.2-90B-Vision-Instruct vs Mistral Large 2.1

Too close to call on our weighted score (Llama-3.2-90B-Vision-Instruct 44, Mistral Large 2.1 42). The right pick depends on what you value most.

  1. Meta

    Llama-3.2-90B-Vision-Instruct

    Released Sep 25, 2024

    44/100
    • ECI125.5
    • Price—
    • Context128K
    Full specs Remove Llama-3.2-90B-Vision-Instruct

  2. Mistral AI

    Mistral Large 2.1

    Released Nov 18, 2024

    42/100
    • 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

Too close to call

It is close. Our weighted score puts them within 2 points (Llama-3.2-90B-Vision-Instruct 44/100, Mistral Large 2.1 42/100), so choose by what matters most for your work: Mistral Large 2.1 for raw capability. The score weighs capability 67%, inputs & features 20%, context window 13%.

  • CapabilityMistral Large 2.1Capabilities Index (ECI): Mistral Large 2.1 128.6 · Llama-3.2-90B-Vision-Instruct 125.5
  • Lowest priceMistral Large 2.1Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend) · Llama-3.2-90B-Vision-Instruct unpriced
  • Longest contextMistral Large 2.1Mistral Large 2.1 131,072 · Llama-3.2-90B-Vision-Instruct 128,000 tokens
  • Widest inputsLlama-3.2-90B-Vision-InstructLlama-3.2-90B-Vision-Instruct: Text, Images · 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-3.2-90B-Vision-InstructMistral Large 2.1
CapabilityCapabilities Index (ECI)67%4650
Inputs & features20%5025
Context window13%2425
Overall100%44/10042/100

Left out because at least one model lacks the data: price. 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-3.2-90B-Vision-Instruct vs Mistral Large 2.1 specifications side by side
SpecificationLlama-3.2-90B-Vision-InstructMetaMistral Large 2.1Mistral AI
Capability
Capabilities Index (ECI)125.5128.6 (best)
ECI rank#150 of 165#142 of 165 (best)
GPQA DiamondGraduate-level science questions41.0%51.3% (best)
OTIS Mock AIME 2024–2025Competition mathematics2.6%7.8% (best)
Price per million tokens
Input—$2.00
Output—$6.00
Cached input——
Blended (3:1)—$3.00
Long-context rate—Same rate
Price source—Median of 2 providers
Limits
Context window128,000 tokens131,072 tokens (best)
Max output4,096 tokens16,384 tokens (best)
Inputs and features
TextYesYes
ImagesYesNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenLlama 3.2 Community LicenseOpen
API model ID——
API providers12 (best)
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-3.2-90B-Vision-Instruct—
  • Mistral Large 2.1$32.00
04 — Questions

Which should you choose?

Which is better: Llama-3.2-90B-Vision-Instruct or Mistral Large 2.1?

It is close. Our weighted score puts them within 2 points (Llama-3.2-90B-Vision-Instruct 44/100, Mistral Large 2.1 42/100), so choose by what matters most for your work: Mistral Large 2.1 for raw capability. The score weighs capability 67%, inputs & features 20%, context window 13%.

Which is cheaper, Llama-3.2-90B-Vision-Instruct or Mistral Large 2.1?

Mistral Large 2.1 is cheaper at $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 $3.00 per million tokens for Mistral Large 2.1 versus . Llama-3.2-90B-Vision-Instruct has no published per-token price.

Which scores higher on benchmarks?

Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.6 (#142 of 165) and Llama-3.2-90B-Vision-Instruct 125.5 (#150 of 165). The confidence ranges of the top two overlap (124.3–130.7 vs 120.3–127.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Llama-3.2-90B-Vision-Instruct 41.0%; OTIS Mock AIME 2024–2025 — Mistral Large 2.1 7.8%, Llama-3.2-90B-Vision-Instruct 2.6%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama-3.2-90B-Vision-Instruct 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. Both support tool calling for agent workflows.

Which has the bigger context window?

Mistral Large 2.1 has the largest context window at 131,072 tokens, against 128,000 for Llama-3.2-90B-Vision-Instruct. Maximum output per response: Llama-3.2-90B-Vision-Instruct up to 4,096, Mistral Large 2.1 up to 16,384 tokens.

Which can read images, PDFs, audio or video?

Llama-3.2-90B-Vision-Instruct accepts text and images; Mistral Large 2.1 accepts text. Llama-3.2-90B-Vision-Instruct handles the widest range of inputs.

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-3.2-90B-Vision-Instruct came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-90B-Vision-Instruct 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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