Llama 3.1 405B vs Mistral Embed

Compare Llama 3.1 405B and Mistral Embed: 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 Mistral models What is an LLM API? Python Quickstart What is inference?
Feature Llama 3.1 405B Mistral Embed
CategoryOpen SourceEmbedding
Parameters405B~200M
Context Window128K8K
Input Price$0.08/1M tokens$0.001/1M tokens
Output Price$0.14/1M tokensN/A/1M tokens
Latency~600ms~15ms

Choose Llama 3.1 405B when:

  • ✓ Premium tasks
  • ✓ Research
  • ✓ Fine-tuning base
Key Strengths:

Largest open model, Highest open-source quality

Choose Mistral Embed when:

  • ✓ RAG pipelines
  • ✓ Semantic search
  • ✓ Document clustering
Key Strengths:

Fast, Low cost, Good quality

Verdict: Llama 3.1 405B vs Mistral Embed

For cost efficiency, Mistral Embed wins at $0.001/1M input tokens. For speed, Mistral Embed is faster at ~15ms. Llama 3.1 405B excels at Premium tasks while Mistral Embed is better for RAG pipelines. 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 405B costs $0.08/1M input tokens and $0.14/1M output tokens. Mistral Embed costs $0.001 input and N/A output. Mistral Embed is 80.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Llama 3.1 405B has a 128K context window with ~600ms latency. Mistral Embed offers 8K context at ~15ms. Llama 3.1 405B has the larger context window.

Best For

Llama 3.1 405B (Open Source) is optimized for: Premium tasks, Research, Fine-tuning base. Mistral Embed (Embedding) works best for: RAG pipelines, Semantic search, Document clustering.

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 405B
response_a = client.chat.completions.create(
    model="llama-3-1-405b",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Mistral Embed
response_b = client.chat.completions.create(
    model="mistral-embed",
    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 405B or Mistral Embed?

Llama 3.1 405B (Open Source, 405B) offers Largest open model. Mistral Embed (Embedding, ~200M) offers Fast. Choose Llama 3.1 405B for Premium tasks or Mistral Embed for RAG pipelines.

How much does Llama 3.1 405B cost vs Mistral Embed?

Llama 3.1 405B: $0.08/1M input, $0.14/1M output. Mistral Embed: $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 405B and Mistral Embed by changing the model parameter. No code changes needed.

Related Comparisons

Llama 3.1 405B vs Text Embedding 3 Large Llama 3.1 405B vs Gemma 3 27B Llama 3.1 405B vs Llama 4 Scout Llama 3.1 405B vs Llama 4 Maverick Llama 3.1 405B vs Llama 3.3 70B Llama 3.1 405B vs Llama 3.1 70B Turbo

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