Gemini 1.5 Flash vs Llama 3.2 90B Instruct
Gemini 1.5 Flash and Llama 3.2 90B Instruct are closely matched at 6.2 and 5.4 on the LLM Stats Score. Gemini 1.5 Flash is 1.4x cheaper per token.
Google · Meta · Updated for 2026
Which is better?
Gemini 1.5 Flash and Llama 3.2 90B Instruct are closely matched on the overall LLM Stats Score at 6.2 and 5.4.
In the 6 individual benchmarks reported for both models, Gemini 1.5 Flash wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Gemini 1.5 Flash is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemini 1.5 Flash also accepts a larger context window (1,048,576 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 Flash
- you value its reported benchmark strengths — it wins 4 of 6 exact shared results
- cost matters — it's about 1.4x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
Choose Llama 3.2 90B Instruct
- 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
22 reported for Gemini 1.5 Flash · 13 for Llama 3.2 90B Instruct
Gemini 1.5 Flash outperforms in 4 benchmarks (GPQA, MATH, MathVista, MMMU), while Llama 3.2 90B Instruct is better at 2 benchmarks (MGSM, MMLU).
Gemini 1.5 Flash shows notably better performance in the majority of 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 Flash ($0.15/1M tokens) is 2.3x cheaper than Llama 3.2 90B Instruct ($0.35/1M tokens).
For output processing, Gemini 1.5 Flash ($0.60/1M tokens) is 1.5x more expensive than Llama 3.2 90B Instruct ($0.40/1M tokens).
In conclusion, Llama 3.2 90B Instruct is more expensive than Gemini 1.5 Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Gemini 1.5 Flash accepts 1,048,576 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 Flash is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Both Gemini 1.5 Flash 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 Flash
Llama 3.2 90B Instruct
License
Usage and distribution terms
Gemini 1.5 Flash 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 Flash 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 Flash.
May 1, 2024
2.3 years ago
Sep 25, 2024
1.9 years ago
4mo newerKnowledge Cutoff
When training data ends
Gemini 1.5 Flash 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 Flash'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 Flash is available from Google. Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic.
Gemini 1.5 Flash
Llama 3.2 90B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemini 1.5 Flash and Llama 3.2 90B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemini 1.5 Flash vs Llama 3.2 90B Instruct.