Llama 4 Scout vs Yi Large

Compare Llama 4 Scout and Yi Large: 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 Yi Large
CategoryOpen SourceOpen Source
Parameters109B (17B active)300B
Context Window512K200K
Input Price$0.05/1M tokens$0.06/1M tokens
Output Price$0.08/1M tokens$0.12/1M tokens
Latency~350ms~450ms

Choose Llama 4 Scout when:

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

512K context, MoE efficiency, Strong multilingual

Choose Yi Large when:

  • ✓ Long document analysis
  • ✓ Research
  • ✓ Complex tasks
Key Strengths:

200K context, Strong analysis, Good reasoning

Verdict: Llama 4 Scout vs Yi Large

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 Yi Large is better for Long document analysis. 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. Yi Large costs $0.06 input and $0.12 output. Llama 4 Scout is 1.2x 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. Yi Large offers 200K context at ~450ms. 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. Yi Large (Open Source) works best for: Long document analysis, Research, Complex tasks.

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 Yi Large
response_b = client.chat.completions.create(
    model="yi-large",
    messages=[{"role": "user", "content": "Your question here"}]
)

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Frequently Asked Questions

Which is better, Llama 4 Scout or Yi Large?

Llama 4 Scout (Open Source, 109B (17B active)) offers 512K context. Yi Large (Open Source, 300B) offers 200K context. Choose Llama 4 Scout for Classical text analysis or Yi Large for Long document analysis.

How much does Llama 4 Scout cost vs Yi Large?

Llama 4 Scout: $0.05/1M input, $0.08/1M output. Yi Large: $0.06/1M input, $0.12/1M 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 Yi Large 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.