Text Embedding 3 Large vs Llama 4 Scout

Compare Text Embedding 3 Large and Llama 4 Scout: 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 Text Embedding 3 Large Llama 4 Scout
CategoryEmbeddingOpen Source
Parameters~500M109B (17B active)
Context Window8K512K
Input Price$0.002/1M tokens$0.05/1M tokens
Output PriceN/A/1M tokens$0.08/1M tokens
Latency~20ms~350ms

Choose Text Embedding 3 Large when:

  • ✓ Semantic search
  • ✓ Knowledge retrieval
  • ✓ Similarity matching
Key Strengths:

3072 dimensions, Superior semantic quality, Matryoshka support

Choose Llama 4 Scout when:

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

512K context, MoE efficiency, Strong multilingual

Verdict: Text Embedding 3 Large vs Llama 4 Scout

For cost efficiency, Text Embedding 3 Large wins at $0.002/1M input tokens. For speed, Text Embedding 3 Large is faster at ~20ms. Text Embedding 3 Large excels at Semantic search while Llama 4 Scout is better for Classical text 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

Text Embedding 3 Large costs $0.002/1M input tokens and N/A/1M output tokens. Llama 4 Scout costs $0.05 input and $0.08 output. Text Embedding 3 Large is 25.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Text Embedding 3 Large has a 8K context window with ~20ms latency. Llama 4 Scout offers 512K context at ~350ms. Llama 4 Scout has the larger context window.

Best For

Text Embedding 3 Large (Embedding) is optimized for: Semantic search, Knowledge retrieval, Similarity matching. Llama 4 Scout (Open Source) works best for: Classical text analysis, Long content, Multi-turn.

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 Text Embedding 3 Large
response_a = client.chat.completions.create(
    model="text-embedding-3-large",
    messages=[{"role": "user", "content": "Your question here"}]
)

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

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Frequently Asked Questions

Which is better, Text Embedding 3 Large or Llama 4 Scout?

Text Embedding 3 Large (Embedding, ~500M) offers 3072 dimensions. Llama 4 Scout (Open Source, 109B (17B active)) offers 512K context. Choose Text Embedding 3 Large for Semantic search or Llama 4 Scout for Classical text analysis.

How much does Text Embedding 3 Large cost vs Llama 4 Scout?

Text Embedding 3 Large: $0.002/1M input, N/A/1M output. Llama 4 Scout: $0.05/1M input, $0.08/1M 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 Text Embedding 3 Large and Llama 4 Scout 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.