Jamba 1.5 Large vs DBRX
Compare Jamba 1.5 Large and DBRX: 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 | Jamba 1.5 Large | DBRX |
|---|---|---|
| Category | Enterprise | Enterprise |
| Parameters | 398B (94B active) | 132B (36B active) |
| Context Window | 256K | 32K |
| Input Price | $0.08/1M tokens | $0.04/1M tokens |
| Output Price | $0.14/1M tokens | $0.08/1M tokens |
| Latency | ~500ms | ~300ms |
Choose Jamba 1.5 Large when:
- ✓ Full text processing
- ✓ Comprehensive reports
- ✓ Long analysis
256K context, SSM-Transformer hybrid, Good summarization
Choose DBRX when:
- ✓ Data pipelines
- ✓ Analytics
- ✓ Enterprise workflows
MoE efficient, Good for data, Enterprise-grade
Verdict: Jamba 1.5 Large vs DBRX
For cost efficiency, DBRX wins at $0.04/1M input tokens. For speed, DBRX is faster at ~300ms. Jamba 1.5 Large excels at Full text processing while DBRX is better for Data pipelines. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.
Detailed Analysis
Pricing Comparison
Jamba 1.5 Large costs $0.08/1M input tokens and $0.14/1M output tokens. DBRX costs $0.04 input and $0.08 output. DBRX is 2.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Jamba 1.5 Large has a 256K context window with ~500ms latency. DBRX offers 32K context at ~300ms. Jamba 1.5 Large has the larger context window.
Best For
Jamba 1.5 Large (Enterprise) is optimized for: Full text processing, Comprehensive reports, Long analysis. DBRX (Enterprise) works best for: Data pipelines, Analytics, Enterprise workflows.
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 Jamba 1.5 Large
response_a = client.chat.completions.create(
model="jamba-1-5-large",
messages=[{"role": "user", "content": "Your question here"}]
)
# Use DBRX
response_b = client.chat.completions.create(
model="dbrx",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Jamba 1.5 Large or DBRX?
Jamba 1.5 Large (Enterprise, 398B (94B active)) offers 256K context. DBRX (Enterprise, 132B (36B active)) offers MoE efficient. Choose Jamba 1.5 Large for Full text processing or DBRX for Data pipelines.
How much does Jamba 1.5 Large cost vs DBRX?
Jamba 1.5 Large: $0.08/1M input, $0.14/1M output. DBRX: $0.04/1M input, $0.08/1M 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 Jamba 1.5 Large and DBRX 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.