Llama 3.1 8B Turbo vs Cohere Embed v4

Compare Llama 3.1 8B Turbo and Cohere Embed v4: 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 Cohere models What is an LLM API? Python Quickstart What is inference?
Feature Llama 3.1 8B Turbo Cohere Embed v4
CategoryCompactEmbedding
Parameters8B~400M
Context Window128K128K
Input Price$0.01/1M tokens$0.001/1M tokens
Output Price$0.02/1M tokensN/A/1M tokens
Latency~60ms~15ms

Choose Llama 3.1 8B Turbo when:

  • ✓ Intent classification
  • ✓ Content filtering
  • ✓ Simple Q&A
Key Strengths:

Extremely fast, Very low cost, 128K context

Choose Cohere Embed v4 when:

  • ✓ Long document RAG
  • ✓ Multimodal search
  • ✓ Large knowledge bases
Key Strengths:

128K context, Multimodal embedding, Matryoshka

Verdict: Llama 3.1 8B Turbo vs Cohere Embed v4

For cost efficiency, Cohere Embed v4 wins at $0.001/1M input tokens. For speed, Cohere Embed v4 is faster at ~15ms. Llama 3.1 8B Turbo excels at Intent classification while Cohere Embed v4 is better for Long document RAG. 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 3.1 8B Turbo costs $0.01/1M input tokens and $0.02/1M output tokens. Cohere Embed v4 costs $0.001 input and N/A output. Cohere Embed v4 is 10.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Llama 3.1 8B Turbo has a 128K context window with ~60ms latency. Cohere Embed v4 offers 128K context at ~15ms. Both have identical context windows.

Best For

Llama 3.1 8B Turbo (Compact) is optimized for: Intent classification, Content filtering, Simple Q&A. Cohere Embed v4 (Embedding) works best for: Long document RAG, Multimodal search, Large knowledge bases.

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 3.1 8B Turbo
response_a = client.chat.completions.create(
    model="llama-3-1-8b-turbo",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Cohere Embed v4
response_b = client.chat.completions.create(
    model="embed-v4",
    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 3.1 8B Turbo or Cohere Embed v4?

Llama 3.1 8B Turbo (Compact, 8B) offers Extremely fast. Cohere Embed v4 (Embedding, ~400M) offers 128K context. Choose Llama 3.1 8B Turbo for Intent classification or Cohere Embed v4 for Long document RAG.

How much does Llama 3.1 8B Turbo cost vs Cohere Embed v4?

Llama 3.1 8B Turbo: $0.01/1M input, $0.02/1M output. Cohere Embed v4: $0.001/1M input, N/A/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 3.1 8B Turbo and Cohere Embed v4 by changing the model parameter. No code changes needed.

Related Comparisons

Llama 3.1 8B Turbo vs GPT-4.1 Nano Llama 3.1 8B Turbo vs GPT-4o Mini Llama 3.1 8B Turbo vs Text Embedding 3 Large Llama 3.1 8B Turbo vs Claude Haiku 3.5 Llama 3.1 8B Turbo vs Gemma 3 12B Llama 3.1 8B Turbo vs Gemma 3 4B

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