Model Comparison

DeepSeek VL2 vs Llama 4 ScoutWhich is better in 2026?

Llama 4 Scout significantly outperforms across most benchmarks.

Verdict: DeepSeek VL2 vs Llama 4 Scout — which is better?

DeepSeek VL2 (by DeepSeek) and Llama 4 Scout (by Meta) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

DeepSeek VL2 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.

Llama 4 Scout also accepts a larger context window (10,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose DeepSeek VL2 if…

  • you are already invested in the DeepSeek ecosystem

Choose Llama 4 Scout if…

  • you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
  • you process long inputs — it offers a 10,000,000 token context window
  • you want the most recent training data — it shipped Apr 2025

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek VL2 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.

Tue Jul 28 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

82.0B diff

Llama 4 Scout has 82.0B more parameters than DeepSeek VL2, making it 303.7% larger.

DeepSeek
DeepSeek VL2
27.0Bparameters
Meta
Llama 4 Scout
109.0Bparameters
27.0B
DeepSeek VL2
109.0B
Llama 4 Scout

Context Window

Maximum input and output token capacity

Llama 4 Scout accepts 10,000,000 input tokens compared to DeepSeek VL2's 129,280 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while DeepSeek VL2 is limited to 129,280 tokens.

DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Meta
Llama 4 Scout
Input10,000,000 tokens
Output10,000,000 tokens
Tue Jul 28 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both DeepSeek VL2 and Llama 4 Scout support multimodal inputs.

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

DeepSeek VL2

Text
Images
Audio
Video

Llama 4 Scout

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 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

deepseek

Open weights

Llama 4 Scout

Llama 4 Community License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 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.

DeepSeek VL2

Dec 13, 2024

1.6 years ago

Llama 4 Scout

Apr 5, 2025

1.3 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

Provider Availability

DeepSeek VL2 is available from Replicate. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.

DeepSeek VL2

replicate logo
Replicate

Llama 4 Scout

deepinfra logo
Deepinfra
Input Price:Input: $0.08/1MOutput Price:Output: $0.30/1M
lambda logo
Lambda
Input Price:Input: $0.08/1MOutput Price:Output: $0.30/1M
novita logo
Novita
Input Price:Input: $0.10/1MOutput Price:Output: $0.50/1M
groq logo
Groq
Input Price:Input: $0.11/1MOutput Price:Output: $0.34/1M
fireworks logo
Fireworks
Input Price:Input: $0.15/1MOutput Price:Output: $0.60/1M
together logo
Together
Input Price:Input: $0.18/1MOutput Price:Output: $0.59/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek

No standout differentiators in the data we have for this pair.

Meta
Larger context window (10,000,000 tokens)
Higher ChartQA score (88.8% vs 86.0%)
Higher DocVQA score (94.4% vs 93.3%)
Higher MathVista score (70.7% vs 62.8%)
Higher MMMU score (69.4% vs 51.1%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek VL2 and Llama 4 Scout side-by-side, then vote on the output you prefer.

DeepSeek VL2
✓ Preferred
Llama 4 Scout
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2
Meta
Llama 4 Scout

FAQ

Common questions about DeepSeek VL2 vs Llama 4 Scout.

Which is better, DeepSeek VL2 or Llama 4 Scout?

Llama 4 Scout significantly outperforms across most benchmarks. DeepSeek VL2 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.

How does DeepSeek VL2 compare to Llama 4 Scout in benchmarks?

DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%. Llama 4 Scout scores DocVQA: 94.4%, MGSM: 90.6%, ChartQA: 88.8%, MMLU: 79.6%, MMLU-Pro: 74.3%.

What are the context window sizes for DeepSeek VL2 and Llama 4 Scout?

DeepSeek VL2 supports 129K 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.

What are the main differences between DeepSeek VL2 and Llama 4 Scout?

Key differences include context window (129K vs 10.0M), licensing (deepseek vs Llama 4 Community License Agreement). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek VL2 and Llama 4 Scout?

DeepSeek VL2 is developed by DeepSeek and Llama 4 Scout is developed by Meta.

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