DeepSeek V3 vs Jamba 1.5 Large

Compare DeepSeek V3 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 DeepSeek V3 Jamba 1.5 Large
CategoryOpen SourceEnterprise
Parameters671B (37B active)398B (94B active)
Context Window128K256K
Input Price$0.05/1M tokens$0.08/1M tokens
Output Price$0.09/1M tokens$0.14/1M tokens
Latency~400ms~500ms

Choose DeepSeek V3 when:

  • ✓ API response generation
  • ✓ High-volume processing
  • ✓ Code
Key Strengths:

MoE efficiency, Strong coding, Good structured output

Choose Jamba 1.5 Large when:

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

256K context, SSM-Transformer hybrid, Good summarization

Verdict: DeepSeek V3 vs Jamba 1.5 Large

For cost efficiency, DeepSeek V3 wins at $0.05/1M input tokens. For speed, DeepSeek V3 is faster at ~400ms. DeepSeek V3 excels at API response generation 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

DeepSeek V3 costs $0.05/1M input tokens and $0.09/1M output tokens. Jamba 1.5 Large costs $0.08 input and $0.14 output. DeepSeek V3 is 1.6x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

DeepSeek V3 has a 128K context window with ~400ms latency. Jamba 1.5 Large offers 256K context at ~500ms. Jamba 1.5 Large has the larger context window.

Best For

DeepSeek V3 (Open Source) is optimized for: API response generation, High-volume processing, Code. 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 DeepSeek V3
response_a = client.chat.completions.create(
    model="deepseek-v3",
    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"}]
)

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Get API Key Try in Playground

Frequently Asked Questions

Which is better, DeepSeek V3 or Jamba 1.5 Large?

DeepSeek V3 (Open Source, 671B (37B active)) offers MoE efficiency. Jamba 1.5 Large (Enterprise, 398B (94B active)) offers 256K context. Choose DeepSeek V3 for API response generation or Jamba 1.5 Large for Full text processing.

How much does DeepSeek V3 cost vs Jamba 1.5 Large?

DeepSeek V3: $0.05/1M input, $0.09/1M 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 DeepSeek V3 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.