Llama 3.2 90B Instruct vs Pixtral-12B
Llama 3.2 90B Instruct leads the LLM Stats Score 5.3 to -1.4. Pixtral-12B is 2.4x cheaper per token.
Meta · Mistral AI · Updated for 2026
Which is better?
Llama 3.2 90B Instruct leads the overall LLM Stats Score 5.3 to -1.4, ranking #288 overall.
In the 7 individual benchmarks reported for both models, Llama 3.2 90B Instruct wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Pixtral-12B is roughly 2.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Llama 3.2 90B Instruct
- overall performance matters — it scores 5.3 and ranks #288 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 7 exact shared results
- you want the most recent training data — it shipped Sep 2024
Choose Pixtral-12B
- cost matters — it's about 2.4x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
13 reported for Llama 3.2 90B Instruct · 12 for Pixtral-12B
Llama 3.2 90B Instruct outperforms in 4 benchmarks (ChartQA, MATH, MMLU, MMMU), while Pixtral-12B is better at 3 benchmarks (DocVQA, MathVista, VQAv2).
Llama 3.2 90B Instruct has a slight edge in benchmark performance.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Llama 3.2 90B Instruct ($0.35/1M tokens) is 2.3x more expensive than Pixtral-12B ($0.15/1M tokens).
For output processing, Llama 3.2 90B Instruct ($0.40/1M tokens) is 2.7x more expensive than Pixtral-12B ($0.15/1M tokens).
In conclusion, Llama 3.2 90B Instruct is more expensive than Pixtral-12B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Llama 3.2 90B Instruct has 77.6B more parameters than Pixtral-12B, making it 625.8% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 128,000 tokens. Llama 3.2 90B Instruct can generate longer responses up to 128,000 tokens, while Pixtral-12B is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Both Llama 3.2 90B Instruct and Pixtral-12B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Llama 3.2 90B Instruct
Pixtral-12B
License
Usage and distribution terms
Llama 3.2 90B Instruct is licensed under Llama 3.2, while Pixtral-12B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 3.2
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Llama 3.2 90B Instruct was released on 2024-09-25, while Pixtral-12B was released on 2024-09-17.
Llama 3.2 90B Instruct is 0 month newer than Pixtral-12B.
Sep 25, 2024
1.9 years ago
1w newerSep 17, 2024
2.0 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic. Pixtral-12B is available from Mistral AI.
Llama 3.2 90B Instruct
Pixtral-12B
Outputs Comparison
Judge for yourself.
Run your own prompts against Llama 3.2 90B Instruct and Pixtral-12B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Llama 3.2 90B Instruct vs Pixtral-12B.