Gemini 1.5 Pro vs Llama 3.2 11B Instruct Comparison

Comparing Gemini 1.5 Pro and Llama 3.2 11B Instruct across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

Gemini 1.5 Pro outperforms in 6 benchmarks (GPQA, MATH, MathVista, MGSM, MMLU, MMMU), while Llama 3.2 11B Instruct is better at 0 benchmarks.

Gemini 1.5 Pro significantly outperforms across most benchmarks.

Sun Mar 22 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 3.2 11B Instruct costs less

For input processing, Gemini 1.5 Pro ($2.50/1M tokens) is 50.0x more expensive than Llama 3.2 11B Instruct ($0.05/1M tokens).

For output processing, Gemini 1.5 Pro ($10.00/1M tokens) is 200.0x more expensive than Llama 3.2 11B Instruct ($0.05/1M tokens).

In conclusion, Gemini 1.5 Pro is more expensive than Llama 3.2 11B Instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sun Mar 22 2026 • llm-stats.com
Google
Gemini 1.5 Pro
Input tokens$2.50
Output tokens$10.00
Best providerGoogle
Meta
Llama 3.2 11B Instruct
Input tokens$0.05
Output tokens$0.05
Best providerDeepinfra
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Context Window

Maximum input and output token capacity

Gemini 1.5 Pro accepts 2,097,152 input tokens compared to Llama 3.2 11B Instruct's 128,000 tokens. Llama 3.2 11B Instruct can generate longer responses up to 128,000 tokens, while Gemini 1.5 Pro is limited to 8,192 tokens.

Google
Gemini 1.5 Pro
Input2,097,152 tokens
Output8,192 tokens
Meta
Llama 3.2 11B Instruct
Input128,000 tokens
Output128,000 tokens
Sun Mar 22 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 1.5 Pro and Llama 3.2 11B Instruct support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Gemini 1.5 Pro

Text
Images
Audio
Video

Llama 3.2 11B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 1.5 Pro is licensed under a proprietary license, while Llama 3.2 11B Instruct uses Llama 3.2 Community License.

License differences may affect how you can use these models in commercial or open-source projects.

Gemini 1.5 Pro

Proprietary

Closed source

Llama 3.2 11B Instruct

Llama 3.2 Community License

Open weights

Release Timeline

When each model was launched

Gemini 1.5 Pro was released on 2024-05-01, while Llama 3.2 11B Instruct was released on 2024-09-25.

Llama 3.2 11B Instruct is 5 months newer than Gemini 1.5 Pro.

Gemini 1.5 Pro

May 1, 2024

1.9 years ago

Llama 3.2 11B Instruct

Sep 25, 2024

1.5 years ago

4mo newer

Knowledge Cutoff

When training data ends

Gemini 1.5 Pro has a knowledge cutoff of 2023-11-01, while Llama 3.2 11B Instruct has a cutoff of 2023-12-31.

Llama 3.2 11B Instruct has more recent training data (up to 2023-12-31), making it potentially better informed about events through that date compared to Gemini 1.5 Pro (2023-11-01).

Gemini 1.5 Pro

Nov 2023

Llama 3.2 11B Instruct

Dec 2023

1 mo newer

Provider Availability

Gemini 1.5 Pro is available from Google. Llama 3.2 11B Instruct is available from DeepInfra, Sambanova, Bedrock, Groq, Together, Fireworks. The availability of providers can affect quality of the model and reliability.

Gemini 1.5 Pro

google logo
Google
Input Price:Input: $2.50/1MOutput Price:Output: $10.00/1M

Llama 3.2 11B Instruct

deepinfra logo
Deepinfra
Input Price:Input: $0.05/1MOutput Price:Output: $0.05/1M
sambanova logo
Sambanova
Input Price:Input: $0.15/1MOutput Price:Output: $0.30/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.16/1MOutput Price:Output: $0.16/1M
groq logo
Groq
Input Price:Input: $0.18/1MOutput Price:Output: $0.18/1M
together logo
Together
Input Price:Input: $0.18/1MOutput Price:Output: $0.18/1M
fireworks logo
Fireworks
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Larger context window (2,097,152 tokens)
Higher GPQA score (59.1% vs 32.8%)
Higher MATH score (86.5% vs 51.9%)
Higher MathVista score (68.1% vs 51.5%)
Higher MGSM score (87.5% vs 68.9%)
Higher MMLU score (85.9% vs 73.0%)
Higher MMMU score (65.9% vs 50.7%)
Less expensive input tokens
Less expensive output tokens
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 1.5 Pro
Meta
Llama 3.2 11B Instruct