Model Comparison

DeepSeek-V2.5 vs Llama 4 Scout

DeepSeek-V2.5 significantly outperforms across most benchmarks. Llama 4 Scout is 1.3x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V2.5 outperforms in 2 benchmarks (MATH, MMLU), while Llama 4 Scout is better at 0 benchmarks.

DeepSeek-V2.5 significantly outperforms across most benchmarks.

Sun May 31 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 4 Scout costs less

For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 1.8x more expensive than Llama 4 Scout ($0.08/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 1.1x cheaper than Llama 4 Scout ($0.30/1M tokens).

In conclusion, DeepSeek-V2.5 is more expensive than Llama 4 Scout.*

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

Lowest available price from all providers
Sun May 31 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
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

127.0B diff

DeepSeek-V2.5 has 127.0B more parameters than Llama 4 Scout, making it 116.5% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Meta
Llama 4 Scout
109.0Bparameters
236.0B
DeepSeek-V2.5
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-V2.5's 8,192 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Meta
Llama 4 Scout
Input10,000,000 tokens
Output10,000,000 tokens
Sun May 31 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Llama 4 Scout supports multimodal inputs, whereas DeepSeek-V2.5 does not.

Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V2.5

Text
Images
Audio
Video

Llama 4 Scout

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V2.5 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-V2.5

deepseek

Open weights

Llama 4 Scout

Llama 4 Community License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Llama 4 Scout was released on 2025-04-05.

Llama 4 Scout is 11 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.1 years ago

Llama 4 Scout

Apr 5, 2025

1.2 years ago

11mo 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-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.

DeepSeek-V2.5

deepseek logo
DeepSeek
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.70/1MOutput Price:Output: $1.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $2.00/1MOutput Price:Output: $2.00/1M

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

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Key Takeaways

DeepSeek
Less expensive output tokens
Higher MATH score (74.7% vs 50.3%)
Higher MMLU score (80.4% vs 79.6%)
Meta
Larger context window (10,000,000 tokens)
Supports multimodal inputs
Less expensive input tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Meta
Llama 4 Scout

FAQ

Common questions about DeepSeek-V2.5 vs Llama 4 Scout.

Which is better, DeepSeek-V2.5 or Llama 4 Scout?

DeepSeek-V2.5 significantly outperforms across most benchmarks. DeepSeek-V2.5 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-V2.5 compare to Llama 4 Scout in benchmarks?

DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. Llama 4 Scout scores DocVQA: 94.4%, MGSM: 90.6%, ChartQA: 88.8%, MMLU: 79.6%, MMLU-Pro: 74.3%.

Is DeepSeek-V2.5 cheaper than Llama 4 Scout?

Llama 4 Scout is 1.8x cheaper for input tokens. DeepSeek-V2.5 costs $0.14/M input and $0.28/M output via deepseek. Llama 4 Scout costs $0.08/M input and $0.30/M output via deepinfra.

What are the context window sizes for DeepSeek-V2.5 and Llama 4 Scout?

DeepSeek-V2.5 supports 8K 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-V2.5 and Llama 4 Scout?

Key differences include context window (8K vs 10.0M), input pricing ($0.14 vs $0.08/M), multimodal support (no vs yes), licensing (deepseek vs Llama 4 Community License Agreement). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V2.5 and Llama 4 Scout?

DeepSeek-V2.5 is developed by DeepSeek and Llama 4 Scout is developed by Meta.