Pixtral Large vs Cohere Embed v4

Compare Pixtral Large 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 Pixtral Large Cohere Embed v4
CategoryVisionEmbedding
Parameters124B~400M
Context Window128K128K
Input Price$0.06/1M tokens$0.001/1M tokens
Output Price$0.10/1M tokensN/A/1M tokens
Latency~450ms~15ms

Choose Pixtral Large when:

  • ✓ Image analysis
  • ✓ Document understanding
  • ✓ Chart reading
Key Strengths:

Strong vision, Good reasoning, Multilingual

Choose Cohere Embed v4 when:

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

128K context, Multimodal embedding, Matryoshka

Verdict: Pixtral Large 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. Pixtral Large excels at 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

Pixtral Large 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

Pixtral Large has a 128K context window with ~450ms latency. Cohere Embed v4 offers 128K context at ~15ms. Both have identical context windows.

Best For

Pixtral Large (Vision) is optimized for: Image analysis, Document understanding, Chart reading. 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 Pixtral Large
response_a = client.chat.completions.create(
    model="pixtral-large",
    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"}]
)

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Get API Key Try in Playground

Frequently Asked Questions

Which is better, Pixtral Large or Cohere Embed v4?

Pixtral Large (Vision, 124B) offers Strong vision. Cohere Embed v4 (Embedding, ~400M) offers 128K context. Choose Pixtral Large for Image analysis or Cohere Embed v4 for Long document RAG.

How much does Pixtral Large cost vs Cohere Embed v4?

Pixtral Large: $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 Pixtral Large 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.