DeepSeek R1 vs Jamba 1.5 Mini
Compare DeepSeek R1 and Jamba 1.5 Mini: 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 Mini |
|---|---|---|
| Category | Reasoning | Compact |
| Parameters | 671B | 52B (12B active) |
| Context Window | 128K | 256K |
| Input Price | $0.08/1M tokens | $0.02/1M tokens |
| Output Price | $0.15/1M tokens | $0.04/1M tokens |
| Latency | ~800ms | ~200ms |
Choose DeepSeek R1 when:
- ✓ Complex yoga calculations
- ✓ Dasha analysis
- ✓ Research-grade analysis
Chain-of-thought, Complex calculations, Transparent thinking
Choose Jamba 1.5 Mini when:
- ✓ Long document Q&A
- ✓ Budget apps
- ✓ Summarization
256K context, Low cost, SSM efficiency
Verdict: DeepSeek R1 vs Jamba 1.5 Mini
For cost efficiency, Jamba 1.5 Mini wins at $0.02/1M input tokens. For speed, Jamba 1.5 Mini is faster at ~200ms. DeepSeek R1 excels at Complex yoga calculations while Jamba 1.5 Mini is better for Long document Q&A. 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 Mini costs $0.02 input and $0.04 output. Jamba 1.5 Mini is 4.0x cheaper on input tokens. 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 Mini offers 256K context at ~200ms. Jamba 1.5 Mini has the larger context window.
Best For
DeepSeek R1 (Reasoning) is optimized for: Complex yoga calculations, Dasha analysis, Research-grade analysis. Jamba 1.5 Mini (Compact) works best for: Long document Q&A, Budget apps, Summarization.
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 Mini
response_b = client.chat.completions.create(
model="jamba-1-5-mini",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, DeepSeek R1 or Jamba 1.5 Mini?
DeepSeek R1 (Reasoning, 671B) offers Chain-of-thought. Jamba 1.5 Mini (Compact, 52B (12B active)) offers 256K context. Choose DeepSeek R1 for Complex yoga calculations or Jamba 1.5 Mini for Long document Q&A.
How much does DeepSeek R1 cost vs Jamba 1.5 Mini?
DeepSeek R1: $0.08/1M input, $0.15/1M output. Jamba 1.5 Mini: $0.02/1M input, $0.04/1M 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 Mini 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.