Llama 4 Scout vs DeepSeek V3

Compare Llama 4 Scout and DeepSeek V3: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.

Updated 2026-05-21 · By Abhishek Raj · Our methodology

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Feature Llama 4 Scout DeepSeek V3
CategoryOpen SourceOpen Source
Parameters109B (17B active)671B (37B active)
Context Window512K128K
Input Price$0.05/1M tokens$0.05/1M tokens
Output Price$0.08/1M tokens$0.09/1M tokens
Latency~350ms~400ms

Choose Llama 4 Scout when:

  • ✓ Classical text analysis
  • ✓ Long content
  • ✓ Multi-turn
Key Strengths:

512K context, MoE efficiency, Strong multilingual

Choose DeepSeek V3 when:

  • ✓ API response generation
  • ✓ High-volume processing
  • ✓ Code
Key Strengths:

MoE efficiency, Strong coding, Good structured output

Verdict: Llama 4 Scout vs DeepSeek V3

For cost efficiency, DeepSeek V3 wins at $0.05/1M input tokens. For speed, Llama 4 Scout is faster at ~350ms. Llama 4 Scout excels at Classical text analysis while DeepSeek V3 is better for API response generation. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.

Detailed Analysis

Pricing Comparison

Llama 4 Scout costs $0.05/1M input tokens and $0.08/1M output tokens. DeepSeek V3 costs $0.05 input and $0.09 output. Both models are similarly priced. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Llama 4 Scout has a 512K context window with ~350ms latency. DeepSeek V3 offers 128K context at ~400ms. Llama 4 Scout has the larger context window.

Best For

Llama 4 Scout (Open Source) is optimized for: Classical text analysis, Long content, Multi-turn. DeepSeek V3 (Open Source) works best for: API response generation, High-volume processing, Code.

Try Both on XALEN

Both models are available through XALEN's OpenAI-compatible API. Switch between them by changing the model parameter:

from xalen import XALEN

client = XALEN(api_key="xln_test_YOUR_KEY")

# Use Llama 4 Scout
response_a = client.chat.completions.create(
    model="llama-4-scout",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use DeepSeek V3
response_b = client.chat.completions.create(
    model="deepseek-v3",
    messages=[{"role": "user", "content": "Your question here"}]
)

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Frequently Asked Questions

Which is better, Llama 4 Scout or DeepSeek V3?

Llama 4 Scout (Open Source, 109B (17B active)) offers 512K context. DeepSeek V3 (Open Source, 671B (37B active)) offers MoE efficiency. Choose Llama 4 Scout for Classical text analysis or DeepSeek V3 for API response generation.

How much does Llama 4 Scout cost vs DeepSeek V3?

Llama 4 Scout: $0.05/1M input, $0.08/1M output. DeepSeek V3: $0.05/1M input, $0.09/1M output. Both available on XALEN with batch processing at 50% discount.

Can I use both models on XALEN?

Yes. XALEN provides 200+ models through a single OpenAI-compatible API. Switch between Llama 4 Scout and DeepSeek V3 by changing the model parameter. No code changes needed.

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Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.