Llama 3.2 90B Vision vs Cohere Embed v4

Compare Llama 3.2 90B Vision 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

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Feature Llama 3.2 90B Vision Cohere Embed v4
CategoryVisionEmbedding
Parameters90B~400M
Context Window128K128K
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 Cohere Embed v4 when:

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

128K context, Multimodal embedding, Matryoshka

Verdict: Llama 3.2 90B Vision 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 3.2 90B Vision excels at Chart image analysis 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 3.2 90B Vision costs $0.06/1M input tokens and $0.10/1M output tokens. Cohere Embed v4 costs $0.001 input and N/A output. Cohere Embed v4 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. Cohere Embed v4 offers 128K context at ~15ms. Both have identical context windows.

Best For

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

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Get API Key Try in Playground

Frequently Asked Questions

Which is better, Llama 3.2 90B Vision or Cohere Embed v4?

Llama 3.2 90B Vision (Vision, 90B) offers Vision + language. Cohere Embed v4 (Embedding, ~400M) offers 128K context. Choose Llama 3.2 90B Vision for Chart image analysis or Cohere Embed v4 for Long document RAG.

How much does Llama 3.2 90B Vision cost vs Cohere Embed v4?

Llama 3.2 90B Vision: $0.06/1M input, $0.10/1M 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 3.2 90B Vision and Cohere Embed v4 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.