Phi-4 vs Jamba 1.5 Large
Compare Phi-4 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 | Phi-4 | Jamba 1.5 Large |
|---|---|---|
| Category | Compact | Enterprise |
| Parameters | 14B | 398B (94B active) |
| Context Window | 16K | 256K |
| Input Price | $0.01/1M tokens | $0.08/1M tokens |
| Output Price | $0.02/1M tokens | $0.14/1M tokens |
| Latency | ~100ms | ~500ms |
Choose Phi-4 when:
- ✓ Edge deployments
- ✓ Cost-sensitive apps
- ✓ Classification
Very compact, Strong reasoning for size, Extremely low cost
Choose Jamba 1.5 Large when:
- ✓ Full text processing
- ✓ Comprehensive reports
- ✓ Long analysis
256K context, SSM-Transformer hybrid, Good summarization
Verdict: Phi-4 vs Jamba 1.5 Large
For cost efficiency, Phi-4 wins at $0.01/1M input tokens. For speed, Phi-4 is faster at ~100ms. Phi-4 excels at Edge deployments 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
Phi-4 costs $0.01/1M input tokens and $0.02/1M output tokens. Jamba 1.5 Large costs $0.08 input and $0.14 output. Phi-4 is 8.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Phi-4 has a 16K context window with ~100ms latency. Jamba 1.5 Large offers 256K context at ~500ms. Jamba 1.5 Large has the larger context window.
Best For
Phi-4 (Compact) is optimized for: Edge deployments, Cost-sensitive apps, Classification. 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 Phi-4
response_a = client.chat.completions.create(
model="phi-4",
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, Phi-4 or Jamba 1.5 Large?
Phi-4 (Compact, 14B) offers Very compact. Jamba 1.5 Large (Enterprise, 398B (94B active)) offers 256K context. Choose Phi-4 for Edge deployments or Jamba 1.5 Large for Full text processing.
How much does Phi-4 cost vs Jamba 1.5 Large?
Phi-4: $0.01/1M input, $0.02/1M 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 Phi-4 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.