Model Comparison

DeepSeek VL2 Tiny vs Llama 4 Scout

Llama 4 Scout significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek VL2 Tiny outperforms in 0 benchmarks, while Llama 4 Scout is better at 4 benchmarks (ChartQA, DocVQA, MathVista, MMMU).

Llama 4 Scout significantly outperforms across most benchmarks.

Thu Apr 30 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 30 2026 • llm-stats.com
DeepSeek
DeepSeek VL2 Tiny
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Meta
Llama 4 Scout
Input tokens$0.08
Output tokens$0.30
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

106.0B diff

Llama 4 Scout has 106.0B more parameters than DeepSeek VL2 Tiny, making it 3533.3% larger.

DeepSeek
DeepSeek VL2 Tiny
3.0Bparameters
Meta
Llama 4 Scout
109.0Bparameters
3.0B
DeepSeek VL2 Tiny
109.0B
Llama 4 Scout

Context Window

Maximum input and output token capacity

Only Llama 4 Scout specifies input context (10,000,000 tokens). Only Llama 4 Scout specifies output context (10,000,000 tokens).

DeepSeek
DeepSeek VL2 Tiny
Input- tokens
Output- tokens
Meta
Llama 4 Scout
Input10,000,000 tokens
Output10,000,000 tokens
Thu Apr 30 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both DeepSeek VL2 Tiny and Llama 4 Scout support multimodal inputs.

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

DeepSeek VL2 Tiny

Text
Images
Audio
Video

Llama 4 Scout

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 Tiny is licensed under deepseek, while Llama 4 Scout uses Llama 4 Community License Agreement.

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

DeepSeek VL2 Tiny

deepseek

Open weights

Llama 4 Scout

Llama 4 Community License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 Tiny was released on 2024-12-13, while Llama 4 Scout was released on 2025-04-05.

Llama 4 Scout is 4 months newer than DeepSeek VL2 Tiny.

DeepSeek VL2 Tiny

Dec 13, 2024

1.4 years ago

Llama 4 Scout

Apr 5, 2025

1.1 years ago

3mo 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

DeepSeek
Meta
Larger context window (10,000,000 tokens)
Higher ChartQA score (88.8% vs 81.0%)
Higher DocVQA score (94.4% vs 88.9%)
Higher MathVista score (70.7% vs 53.6%)
Higher MMMU score (69.4% vs 40.7%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2 Tiny
Meta
Llama 4 Scout

FAQ

Common questions about DeepSeek VL2 Tiny vs Llama 4 Scout

Llama 4 Scout significantly outperforms across most benchmarks. DeepSeek VL2 Tiny is made by DeepSeek and Llama 4 Scout is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek VL2 Tiny scores DocVQA: 88.9%, ChartQA: 81.0%, OCRBench: 80.9%, TextVQA: 80.7%, AI2D: 71.6%. Llama 4 Scout scores DocVQA: 94.4%, MGSM: 90.6%, ChartQA: 88.8%, MMLU: 79.6%, MMLU-Pro: 74.3%.
DeepSeek VL2 Tiny supports an unknown number of tokens and Llama 4 Scout supports 10.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (deepseek vs Llama 4 Community License Agreement). See the full comparison above for benchmark-by-benchmark results.
DeepSeek VL2 Tiny is developed by DeepSeek and Llama 4 Scout is developed by Meta.