Llama 3.1 70B Turbo vs Cohere Rerank 3.5

Compare Llama 3.1 70B Turbo and Cohere Rerank 3.5: 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 70B Turbo Cohere Rerank 3.5
CategoryOpen SourceReranking
Parameters70B~600M
Context Window128K4K
Input Price$0.04/1M tokens$0.002/search/1M tokens
Output Price$0.06/1M tokensN/A/1M tokens
Latency~250ms~25ms

Choose Llama 3.1 70B Turbo when:

  • ✓ Production APIs
  • ✓ Fast generation
  • ✓ General purpose
Key Strengths:

Fast inference, Good quality, Well-tested

Choose Cohere Rerank 3.5 when:

  • ✓ Search reranking
  • ✓ RAG improvement
  • ✓ Result quality
Key Strengths:

Higher quality, Multilingual, Fast

Verdict: Llama 3.1 70B Turbo vs Cohere Rerank 3.5

For cost efficiency, Cohere Rerank 3.5 wins at $0.002/search/1M input tokens. For speed, Llama 3.1 70B Turbo is faster at ~250ms. Llama 3.1 70B Turbo excels at Production APIs while Cohere Rerank 3.5 is better for Search reranking. 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 70B Turbo costs $0.04/1M input tokens and $0.06/1M output tokens. Cohere Rerank 3.5 costs $0.002/search input and N/A output. Cohere Rerank 3.5 is 20.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Llama 3.1 70B Turbo has a 128K context window with ~250ms latency. Cohere Rerank 3.5 offers 4K context at ~25ms. Llama 3.1 70B Turbo has the larger context window.

Best For

Llama 3.1 70B Turbo (Open Source) is optimized for: Production APIs, Fast generation, General purpose. Cohere Rerank 3.5 (Reranking) works best for: Search reranking, RAG improvement, Result quality.

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 70B Turbo
response_a = client.chat.completions.create(
    model="llama-3-1-70b-turbo",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Cohere Rerank 3.5
response_b = client.chat.completions.create(
    model="rerank-v3-5",
    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 70B Turbo or Cohere Rerank 3.5?

Llama 3.1 70B Turbo (Open Source, 70B) offers Fast inference. Cohere Rerank 3.5 (Reranking, ~600M) offers Higher quality. Choose Llama 3.1 70B Turbo for Production APIs or Cohere Rerank 3.5 for Search reranking.

How much does Llama 3.1 70B Turbo cost vs Cohere Rerank 3.5?

Llama 3.1 70B Turbo: $0.04/1M input, $0.06/1M output. Cohere Rerank 3.5: $0.002/search/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 70B Turbo and Cohere Rerank 3.5 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.