Gemini 1.5 Pro vs Llama 3.2 90B Instruct
Gemini 1.5 Pro leads the LLM Stats Score 12.2 to 5.4. Llama 3.2 90B Instruct is 12.1x cheaper per token.
Google · Meta · Updated for 2026
Which is better?
Gemini 1.5 Pro leads the overall LLM Stats Score 12.2 to 5.4, ranking #239 overall.
In the 6 individual benchmarks reported for both models, Gemini 1.5 Pro wins 5; 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.
Gemini 1.5 Pro also accepts a larger context window (2,097,152 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Gemini 1.5 Pro
- overall performance matters — it scores 12.2 and ranks #239 on LLM Stats
- you value its reported benchmark strengths — it wins 5 of 6 exact shared results
- you process long inputs — it offers a 2,097,152 token context window
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
23 reported for Gemini 1.5 Pro · 13 for Llama 3.2 90B Instruct
Gemini 1.5 Pro outperforms in 5 benchmarks (GPQA, MATH, MathVista, MGSM, MMMU), while Llama 3.2 90B Instruct is better at 1 benchmark (MMLU).
Gemini 1.5 Pro 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, Gemini 1.5 Pro ($2.50/1M tokens) is 7.1x more expensive than Llama 3.2 90B Instruct ($0.35/1M tokens).
For output processing, Gemini 1.5 Pro ($10.00/1M tokens) is 25.0x more expensive than Llama 3.2 90B Instruct ($0.40/1M tokens).
In conclusion, Gemini 1.5 Pro 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
Gemini 1.5 Pro accepts 2,097,152 input tokens compared to Llama 3.2 90B Instruct's 128,000 tokens. Llama 3.2 90B Instruct can generate longer responses up to 128,000 tokens, while Gemini 1.5 Pro is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Both Gemini 1.5 Pro and Llama 3.2 90B Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Gemini 1.5 Pro
Llama 3.2 90B Instruct
License
Usage and distribution terms
Gemini 1.5 Pro 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
Gemini 1.5 Pro was released on 2024-05-01, while Llama 3.2 90B Instruct was released on 2024-09-25.
Llama 3.2 90B Instruct is 5 months newer than Gemini 1.5 Pro.
May 1, 2024
2.3 years ago
Sep 25, 2024
1.9 years ago
4mo newerKnowledge Cutoff
When training data ends
Gemini 1.5 Pro has a documented knowledge cutoff of 2023-11-01, while Llama 3.2 90B Instruct's cutoff date is not specified.
We can confirm Gemini 1.5 Pro's training data extends to 2023-11-01, but cannot make a direct comparison without Llama 3.2 90B Instruct's cutoff date.
Nov 2023
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Provider Availability
Gemini 1.5 Pro is available from Google. Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic.
Gemini 1.5 Pro
Llama 3.2 90B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemini 1.5 Pro and Llama 3.2 90B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemini 1.5 Pro vs Llama 3.2 90B Instruct.