DeepSeek R1 vs Jamba 1.5 Large
Compare DeepSeek R1 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 | DeepSeek R1 | Jamba 1.5 Large |
|---|---|---|
| Category | Reasoning | Enterprise |
| Parameters | 671B | 398B (94B active) |
| Context Window | 128K | 256K |
| Input Price | $0.08/1M tokens | $0.08/1M tokens |
| Output Price | $0.15/1M tokens | $0.14/1M tokens |
| Latency | ~800ms | ~500ms |
Choose DeepSeek R1 when:
- ✓ Complex yoga calculations
- ✓ Dasha analysis
- ✓ Research-grade analysis
Chain-of-thought, Complex calculations, Transparent thinking
Choose Jamba 1.5 Large when:
- ✓ Full text processing
- ✓ Comprehensive reports
- ✓ Long analysis
256K context, SSM-Transformer hybrid, Good summarization
Verdict: DeepSeek R1 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. DeepSeek R1 excels at Complex yoga calculations 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 R1 costs $0.08/1M input tokens and $0.15/1M output tokens. Jamba 1.5 Large costs $0.08 input and $0.14 output. Both models are similarly priced. XALEN offers batch processing at 50% discount on both models.
Performance & Context
DeepSeek R1 has a 128K context window with ~800ms latency. Jamba 1.5 Large offers 256K context at ~500ms. Jamba 1.5 Large has the larger context window.
Best For
DeepSeek R1 (Reasoning) is optimized for: Complex yoga calculations, Dasha analysis, Research-grade analysis. 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 R1
response_a = client.chat.completions.create(
model="deepseek-r1",
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, DeepSeek R1 or Jamba 1.5 Large?
DeepSeek R1 (Reasoning, 671B) offers Chain-of-thought. Jamba 1.5 Large (Enterprise, 398B (94B active)) offers 256K context. Choose DeepSeek R1 for Complex yoga calculations or Jamba 1.5 Large for Full text processing.
How much does DeepSeek R1 cost vs Jamba 1.5 Large?
DeepSeek R1: $0.08/1M 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 DeepSeek R1 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.