Model Comparison

Llama 3.3 70B Instruct vs Qwen2.5 VL 7B Instruct

Comparing Llama 3.3 70B Instruct and Qwen2.5 VL 7B Instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Llama 3.3 70B Instruct and Qwen2.5 VL 7B Instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

61.7B diff

Llama 3.3 70B Instruct has 61.7B more parameters than Qwen2.5 VL 7B Instruct, making it 744.4% larger.

Llama 3.3 70B Instruct

70.0Bparameters

Qwen2.5 VL 7B Instruct

8.3Bparameters

70.0B

Llama 3.3 70B Instruct

8.3B

Qwen2.5 VL 7B Instruct

Context Window

Maximum input and output token capacity

Only Llama 3.3 70B Instruct specifies input context (128,000 tokens). Only Llama 3.3 70B Instruct specifies output context (128,000 tokens).

Llama 3.3 70B Instruct

Input128,000 tokens

Output128,000 tokens

Qwen2.5 VL 7B Instruct

Input- tokens

Output- tokens

Tue May 12 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen2.5 VL 7B Instruct supports multimodal inputs, whereas Llama 3.3 70B Instruct does not.

Qwen2.5 VL 7B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

Llama 3.3 70B Instruct

Text

Images

Audio

Video

Qwen2.5 VL 7B Instruct

Text

Images

Audio

Video

License

Usage and distribution terms

Llama 3.3 70B Instruct is licensed under Llama 3.3 Community License Agreement, while Qwen2.5 VL 7B Instruct uses Apache 2.0.

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

Llama 3.3 70B Instruct

Llama 3.3 Community License Agreement

Open weights

Qwen2.5 VL 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Llama 3.3 70B Instruct was released on 2024-12-06, while Qwen2.5 VL 7B Instruct was released on 2025-01-26.

Qwen2.5 VL 7B Instruct is 2 months newer than Llama 3.3 70B Instruct.

Llama 3.3 70B Instruct

Dec 6, 2024

1.4 years ago

Qwen2.5 VL 7B Instruct

Jan 26, 2025

1.3 years ago

1mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

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Key Takeaways

Qwen2.5 VL 7B Instruct

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature	Llama 3.3 70B Instruct	Qwen2.5 VL 7B Instruct

FAQ

Common questions about Llama 3.3 70B Instruct vs Qwen2.5 VL 7B Instruct.

Which is better, Llama 3.3 70B Instruct or Qwen2.5 VL 7B Instruct?

Llama 3.3 70B Instruct (Meta) and Qwen2.5 VL 7B Instruct (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Llama 3.3 70B Instruct compare to Qwen2.5 VL 7B Instruct in benchmarks?

Llama 3.3 70B Instruct scores IFEval: 92.1%, MGSM: 91.1%, HumanEval: 88.4%, MBPP EvalPlus: 87.6%, MMLU: 86.0%. Qwen2.5 VL 7B Instruct scores DocVQA: 95.7%, Android Control Low_EM: 91.4%, MobileMiniWob++_SR: 91.4%, ChartQA: 87.3%, OCRBench: 86.4%.

What are the context window sizes for Llama 3.3 70B Instruct and Qwen2.5 VL 7B Instruct?

Llama 3.3 70B Instruct supports 128K tokens and Qwen2.5 VL 7B Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Llama 3.3 70B Instruct and Qwen2.5 VL 7B Instruct?

Key differences include multimodal support (no vs yes), licensing (Llama 3.3 Community License Agreement vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Llama 3.3 70B Instruct and Qwen2.5 VL 7B Instruct?

Llama 3.3 70B Instruct is developed by Meta and Qwen2.5 VL 7B Instruct is developed by Alibaba Cloud / Qwen Team.