Llama 3.2 1B vs Jamba 1.5 Large

Compare Llama 3.2 1B 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

All Meta models All AI21 models What is an LLM API? Python Quickstart What is inference?
Feature Llama 3.2 1B Jamba 1.5 Large
CategoryCompactEnterprise
Parameters1B398B (94B active)
Context Window128K256K
Input Price$0.004/1M tokens$0.08/1M tokens
Output Price$0.008/1M tokens$0.14/1M tokens
Latency~25ms~500ms

Choose Llama 3.2 1B when:

  • ✓ Intent detection
  • ✓ Routing
  • ✓ Edge classification
Key Strengths:

Smallest footprint, Fastest inference, Classification

Choose Jamba 1.5 Large when:

  • ✓ Full text processing
  • ✓ Comprehensive reports
  • ✓ Long analysis
Key Strengths:

256K context, SSM-Transformer hybrid, Good summarization

Verdict: Llama 3.2 1B vs Jamba 1.5 Large

For cost efficiency, Llama 3.2 1B wins at $0.004/1M input tokens. For speed, Llama 3.2 1B is faster at ~25ms. Llama 3.2 1B excels at Intent detection 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

Llama 3.2 1B costs $0.004/1M input tokens and $0.008/1M output tokens. Jamba 1.5 Large costs $0.08 input and $0.14 output. Llama 3.2 1B is 20.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Llama 3.2 1B has a 128K context window with ~25ms latency. Jamba 1.5 Large offers 256K context at ~500ms. Jamba 1.5 Large has the larger context window.

Best For

Llama 3.2 1B (Compact) is optimized for: Intent detection, Routing, Edge 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 Llama 3.2 1B
response_a = client.chat.completions.create(
    model="llama-3-2-1b",
    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"}]
)

Start Building with XALEN

200+ AI models. One API. Pay-as-you-go.

Get API Key Try in Playground

Frequently Asked Questions

Which is better, Llama 3.2 1B or Jamba 1.5 Large?

Llama 3.2 1B (Compact, 1B) offers Smallest footprint. Jamba 1.5 Large (Enterprise, 398B (94B active)) offers 256K context. Choose Llama 3.2 1B for Intent detection or Jamba 1.5 Large for Full text processing.

How much does Llama 3.2 1B cost vs Jamba 1.5 Large?

Llama 3.2 1B: $0.004/1M input, $0.008/1M 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 Llama 3.2 1B and Jamba 1.5 Large by changing the model parameter. No code changes needed.

Related Comparisons

Llama 3.2 1B vs GPT-4.1 Nano Llama 3.2 1B vs GPT-4o Mini Llama 3.2 1B vs Claude Haiku 3.5 Llama 3.2 1B vs Gemma 3 12B Llama 3.2 1B vs Gemma 3 4B Llama 3.2 1B vs Gemini 2.5 Flash Lite

Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.