GPT-4o vs Jamba 1.5 Large

Compare GPT-4o 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 GPT-4o Jamba 1.5 Large
CategoryFrontierEnterprise
Parameters~1T398B (94B active)
Context Window128K256K
Input Price$0.05/1M tokens$0.08/1M tokens
Output Price$0.15/1M tokens$0.14/1M tokens
Latency~400ms~500ms

Choose GPT-4o when:

  • ✓ Chart image analysis
  • ✓ Multimodal Q&A
  • ✓ Content generation
Key Strengths:

Multimodal, Fast for frontier, Strong reasoning

Choose Jamba 1.5 Large when:

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

256K context, SSM-Transformer hybrid, Good summarization

Verdict: GPT-4o vs Jamba 1.5 Large

For cost efficiency, GPT-4o wins at $0.05/1M input tokens. For speed, GPT-4o is faster at ~400ms. GPT-4o excels at Chart image analysis 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

GPT-4o costs $0.05/1M input tokens and $0.15/1M output tokens. Jamba 1.5 Large costs $0.08 input and $0.14 output. GPT-4o is 1.6x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

GPT-4o 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

GPT-4o (Frontier) is optimized for: Chart image analysis, Multimodal Q&A, Content generation. 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 GPT-4o
response_a = client.chat.completions.create(
    model="gpt-4o",
    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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Frequently Asked Questions

Which is better, GPT-4o or Jamba 1.5 Large?

GPT-4o (Frontier, ~1T) offers Multimodal. Jamba 1.5 Large (Enterprise, 398B (94B active)) offers 256K context. Choose GPT-4o for Chart image analysis or Jamba 1.5 Large for Full text processing.

How much does GPT-4o cost vs Jamba 1.5 Large?

GPT-4o: $0.05/1M input, $0.15/1M 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 GPT-4o 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.