Llama 3.2 90B Vision vs Mistral Embed

Compare Llama 3.2 90B Vision 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

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Feature Llama 3.2 90B Vision Mistral Embed
CategoryVisionEmbedding
Parameters90B~200M
Context Window128K8K
Input Price$0.06/1M tokens$0.001/1M tokens
Output Price$0.10/1M tokensN/A/1M tokens
Latency~500ms~15ms

Choose Llama 3.2 90B Vision when:

  • ✓ Chart image analysis
  • ✓ Document scanning
  • ✓ Visual Q&A
Key Strengths:

Vision + language, Open weights, Good reasoning

Choose Mistral Embed when:

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

Fast, Low cost, Good quality

Verdict: Llama 3.2 90B Vision vs Mistral Embed

For cost efficiency, Mistral Embed wins at $0.001/1M input tokens. For speed, Mistral Embed is faster at ~15ms. Llama 3.2 90B Vision excels at Chart image analysis 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 3.2 90B Vision costs $0.06/1M input tokens and $0.10/1M output tokens. Mistral Embed costs $0.001 input and N/A output. Mistral Embed is 60.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Llama 3.2 90B Vision has a 128K context window with ~500ms latency. Mistral Embed offers 8K context at ~15ms. Llama 3.2 90B Vision has the larger context window.

Best For

Llama 3.2 90B Vision (Vision) is optimized for: Chart image analysis, Document scanning, Visual Q&A. 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 3.2 90B Vision
response_a = client.chat.completions.create(
    model="llama-3-2-90b-vision",
    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

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Get API Key Try in Playground

Frequently Asked Questions

Which is better, Llama 3.2 90B Vision or Mistral Embed?

Llama 3.2 90B Vision (Vision, 90B) offers Vision + language. Mistral Embed (Embedding, ~200M) offers Fast. Choose Llama 3.2 90B Vision for Chart image analysis or Mistral Embed for RAG pipelines.

How much does Llama 3.2 90B Vision cost vs Mistral Embed?

Llama 3.2 90B Vision: $0.06/1M input, $0.10/1M 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 3.2 90B Vision and Mistral Embed 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.