GPT-4o vs Llama 3.2 90B Instruct
GPT-4o leads the LLM Stats Score 14.3 to 5.3. Llama 3.2 90B Instruct is 12.1x cheaper per token.
OpenAI · Meta · Updated for 2026
Which is better?
GPT-4o leads the overall LLM Stats Score 14.3 to 5.3, ranking #226 overall.
In the 8 individual benchmarks reported for both models, GPT-4o wins 7; this is a narrower head-to-head signal than the composite indexes.
On price, Llama 3.2 90B Instruct is roughly 12.1x 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 GPT-4o
- overall performance matters — it scores 14.3 and ranks #226 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 7 of 8 exact shared results
Choose Llama 3.2 90B Instruct
- cost matters — it's about 12.1x cheaper per token
- you want the most recent training data — it shipped Sep 2024
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
38 reported for GPT-4o · 13 for Llama 3.2 90B Instruct
GPT-4o outperforms in 7 benchmarks (AI2D, ChartQA, DocVQA, GPQA, MathVista, MMMU, MMMU-Pro), while Llama 3.2 90B Instruct is better at 1 benchmark (MMLU).
GPT-4o significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-4o ($2.50/1M tokens) is 7.1x more expensive than Llama 3.2 90B Instruct ($0.35/1M tokens).
For output processing, GPT-4o ($10.00/1M tokens) is 25.0x more expensive than Llama 3.2 90B Instruct ($0.40/1M tokens).
In conclusion, GPT-4o is more expensive than Llama 3.2 90B Instruct.*
* Using a 3:1 ratio of input to output tokens
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 GPT-4o is limited to 16,384 tokens.
Input capabilities
Documented input modalities across available providers
Both GPT-4o and Llama 3.2 90B Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-4o
Llama 3.2 90B Instruct
License
Usage and distribution terms
GPT-4o is licensed under a proprietary license, while Llama 3.2 90B Instruct uses Llama 3.2.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Llama 3.2
Open weights
Release Timeline
When each model was launched
GPT-4o was released on 2024-08-06, while Llama 3.2 90B Instruct was released on 2024-09-25.
Llama 3.2 90B Instruct is 2 months newer than GPT-4o.
Aug 6, 2024
2.1 years ago
Sep 25, 2024
1.9 years ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GPT-4o is available from Azure, OpenAI. Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic.
GPT-4o
Llama 3.2 90B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4o and Llama 3.2 90B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4o vs Llama 3.2 90B Instruct.