Mistral Embed vs Jamba 1.5 Large

Compare Mistral Embed and Jamba 1.5 Large: 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 Mistral Embed Jamba 1.5 Large
CategoryEmbeddingEnterprise
Parameters~200M398B (94B active)
Context Window8K256K
Input Price$0.001/1M tokens$0.08/1M tokens
Output PriceN/A/1M tokens$0.14/1M tokens
Latency~15ms~500ms

Choose Mistral Embed when:

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

Fast, Low cost, Good quality

Choose Jamba 1.5 Large when:

  • ✓ Full text processing
  • ✓ Comprehensive reports
  • ✓ Long analysis
Key Strengths:

256K context, SSM-Transformer hybrid, Good summarization

Verdict: Mistral Embed vs Jamba 1.5 Large

For cost efficiency, Mistral Embed wins at $0.001/1M input tokens. For speed, Mistral Embed is faster at ~15ms. Mistral Embed excels at RAG pipelines while Jamba 1.5 Large is better for Full text processing. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.

Detailed Analysis

Pricing Comparison

Mistral Embed costs $0.001/1M input tokens and N/A/1M output tokens. Jamba 1.5 Large costs $0.08 input and $0.14 output. Mistral Embed is 80.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Mistral Embed has a 8K context window with ~15ms latency. Jamba 1.5 Large offers 256K context at ~500ms. Jamba 1.5 Large has the larger context window.

Best For

Mistral Embed (Embedding) is optimized for: RAG pipelines, Semantic search, Document clustering. Jamba 1.5 Large (Enterprise) works best for: Full text processing, Comprehensive reports, Long analysis.

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 Mistral Embed
response_a = client.chat.completions.create(
    model="mistral-embed",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Jamba 1.5 Large
response_b = client.chat.completions.create(
    model="jamba-1-5-large",
    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, Mistral Embed or Jamba 1.5 Large?

Mistral Embed (Embedding, ~200M) offers Fast. Jamba 1.5 Large (Enterprise, 398B (94B active)) offers 256K context. Choose Mistral Embed for RAG pipelines or Jamba 1.5 Large for Full text processing.

How much does Mistral Embed cost vs Jamba 1.5 Large?

Mistral Embed: $0.001/1M input, N/A/1M output. Jamba 1.5 Large: $0.08/1M input, $0.14/1M 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 Mistral Embed and Jamba 1.5 Large 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.