Llama 4 Scout vs DeepSeek R1

Compare Llama 4 Scout and DeepSeek R1: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.

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

All Meta models All DeepSeek models What is an LLM API? Python Quickstart What is inference?
Feature Llama 4 Scout DeepSeek R1
CategoryOpen SourceReasoning
Parameters109B (17B active)671B
Context Window512K128K
Input Price$0.05/1M tokens$0.08/1M tokens
Output Price$0.08/1M tokens$0.15/1M tokens
Latency~350ms~800ms

Choose Llama 4 Scout when:

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

512K context, MoE efficiency, Strong multilingual

Choose DeepSeek R1 when:

  • ✓ Complex yoga calculations
  • ✓ Dasha analysis
  • ✓ Research-grade analysis
Key Strengths:

Chain-of-thought, Complex calculations, Transparent thinking

Verdict: Llama 4 Scout vs DeepSeek R1

For cost efficiency, Llama 4 Scout 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 R1 is better for Complex yoga calculations. 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 R1 costs $0.08 input and $0.15 output. Llama 4 Scout is 1.6x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Llama 4 Scout has a 512K context window with ~350ms latency. DeepSeek R1 offers 128K context at ~800ms. 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 R1 (Reasoning) works best for: Complex yoga calculations, Dasha analysis, Research-grade analysis.

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 R1
response_b = client.chat.completions.create(
    model="deepseek-r1",
    messages=[{"role": "user", "content": "Your question here"}]
)

Start Building with XALEN

200+ AI models. One API. Pay-as-you-go.

Get API Key Try in Playground

Frequently Asked Questions

Which is better, Llama 4 Scout or DeepSeek R1?

Llama 4 Scout (Open Source, 109B (17B active)) offers 512K context. DeepSeek R1 (Reasoning, 671B) offers Chain-of-thought. Choose Llama 4 Scout for Classical text analysis or DeepSeek R1 for Complex yoga calculations.

How much does Llama 4 Scout cost vs DeepSeek R1?

Llama 4 Scout: $0.05/1M input, $0.08/1M output. DeepSeek R1: $0.08/1M input, $0.15/1M 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 R1 by changing the model parameter. No code changes needed.

Related Comparisons

Llama 4 Scout vs o3 Llama 4 Scout vs o3 Mini Llama 4 Scout vs o4-mini Llama 4 Scout vs Gemma 3 27B Llama 4 Scout vs Llama 4 Maverick Llama 4 Scout vs Llama 3.3 70B

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