Model Comparison

Gemini 2.0 Flash Thinking vs Llama 3.2 11B Instruct

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 2.0 Flash Thinking outperforms in 2 benchmarks (GPQA, MMMU), while Llama 3.2 11B Instruct is better at 0 benchmarks.

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Mon May 04 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Llama 3.2 11B Instruct specifies input context (128,000 tokens). Only Llama 3.2 11B Instruct specifies output context (128,000 tokens).

Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Meta
Llama 3.2 11B Instruct
Input128,000 tokens
Output128,000 tokens
Mon May 04 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 2.0 Flash Thinking and Llama 3.2 11B Instruct support multimodal inputs.

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

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

Llama 3.2 11B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.0 Flash Thinking 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 2.0 Flash Thinking

Proprietary

Closed source

Llama 3.2 11B Instruct

Llama 3.2 Community License

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash Thinking was released on 2025-01-21, while Llama 3.2 11B Instruct was released on 2024-09-25.

Gemini 2.0 Flash Thinking is 4 months newer than Llama 3.2 11B Instruct.

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.3 years ago

3mo newer
Llama 3.2 11B Instruct

Sep 25, 2024

1.6 years ago

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a knowledge cutoff of 2024-08-01, while Llama 3.2 11B Instruct has a cutoff of 2023-12-31.

Gemini 2.0 Flash Thinking has more recent training data (up to 2024-08-01), making it potentially better informed about events through that date compared to Llama 3.2 11B Instruct (2023-12-31).

Gemini 2.0 Flash Thinking

Aug 2024

8 mo newer
Llama 3.2 11B Instruct

Dec 2023

Outputs Comparison

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Key Takeaways

Google
Higher GPQA score (74.2% vs 32.8%)
Higher MMMU score (75.4% vs 50.7%)
Meta
Larger context window (128,000 tokens)
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 2.0 Flash Thinking
Meta
Llama 3.2 11B Instruct

FAQ

Common questions about Gemini 2.0 Flash Thinking vs Llama 3.2 11B Instruct.

Which is better, Gemini 2.0 Flash Thinking or Llama 3.2 11B Instruct?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks. Gemini 2.0 Flash Thinking is made by Google and Llama 3.2 11B Instruct is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 2.0 Flash Thinking compare to Llama 3.2 11B Instruct in benchmarks?

Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%. Llama 3.2 11B Instruct scores AI2D: 91.1%, DocVQA: 88.4%, ChartQA: 83.4%, VQAv2 (test): 75.2%, MMLU: 73.0%.

What are the context window sizes for Gemini 2.0 Flash Thinking and Llama 3.2 11B Instruct?

Gemini 2.0 Flash Thinking supports an unknown number of tokens and Llama 3.2 11B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 2.0 Flash Thinking and Llama 3.2 11B Instruct?

Key differences include licensing (Proprietary vs Llama 3.2 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 2.0 Flash Thinking and Llama 3.2 11B Instruct?

Gemini 2.0 Flash Thinking is developed by Google and Llama 3.2 11B Instruct is developed by Meta.