Vedika Pro Ultra vs Jamba 1.5 Large
Compare Vedika Pro Ultra 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
| Feature | Vedika Pro Ultra | Jamba 1.5 Large |
|---|---|---|
| Category | Domain Specialist | Enterprise |
| Parameters | 120B | 398B (94B active) |
| Context Window | 256K | 256K |
| Input Price | $0.12/1M tokens | $0.08/1M tokens |
| Output Price | $0.20/1M tokens | $0.14/1M tokens |
| Latency | ~600ms | ~500ms |
Choose Vedika Pro Ultra when:
- ✓ Kundali matching reports
- ✓ Multi-chart analysis
- ✓ Enterprise platforms
256K context, Deep yoga reasoning, Multi-system comparison
Choose Jamba 1.5 Large when:
- ✓ Full text processing
- ✓ Comprehensive reports
- ✓ Long analysis
256K context, SSM-Transformer hybrid, Good summarization
Verdict: Vedika Pro Ultra vs Jamba 1.5 Large
For cost efficiency, Jamba 1.5 Large wins at $0.08/1M input tokens. For speed, Jamba 1.5 Large is faster at ~500ms. Vedika Pro Ultra excels at Kundali matching reports 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
Vedika Pro Ultra costs $0.12/1M input tokens and $0.20/1M output tokens. Jamba 1.5 Large costs $0.08 input and $0.14 output. Jamba 1.5 Large is 1.5x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Vedika Pro Ultra has a 256K context window with ~600ms latency. Jamba 1.5 Large offers 256K context at ~500ms. Both have identical context windows.
Best For
Vedika Pro Ultra (Domain Specialist) is optimized for: Kundali matching reports, Multi-chart analysis, Enterprise platforms. 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 Vedika Pro Ultra
response_a = client.chat.completions.create(
model="vedika-pro-ultra",
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"}]
)Frequently Asked Questions
Which is better, Vedika Pro Ultra or Jamba 1.5 Large?
Vedika Pro Ultra (Domain Specialist, 120B) offers 256K context. Jamba 1.5 Large (Enterprise, 398B (94B active)) offers 256K context. Choose Vedika Pro Ultra for Kundali matching reports or Jamba 1.5 Large for Full text processing.
How much does Vedika Pro Ultra cost vs Jamba 1.5 Large?
Vedika Pro Ultra: $0.12/1M input, $0.20/1M 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 Vedika Pro Ultra and Jamba 1.5 Large by changing the model parameter. No code changes needed.
Related Comparisons
Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.