Vedika Code vs Llama 3.2 1B

Compare Vedika Code and Llama 3.2 1B: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.

Updated 2026-05-21 · By Abhishek Raj · Our methodology

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Feature Vedika Code Llama 3.2 1B
CategoryCodeCompact
Parameters33B1B
Context Window64K128K
Input Price$0.04/1M tokens$0.004/1M tokens
Output Price$0.06/1M tokens$0.008/1M tokens
Latency~250ms~25ms

Choose Vedika Code when:

  • ✓ API integration code
  • ✓ Temple systems
  • ✓ SDK examples
Key Strengths:

Faith-tech code patterns, API integration code, Temple system boilerplate

Choose Llama 3.2 1B when:

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

Smallest footprint, Fastest inference, Classification

Verdict: Vedika Code vs Llama 3.2 1B

For cost efficiency, Llama 3.2 1B wins at $0.004/1M input tokens. For speed, Vedika Code is faster at ~250ms. Vedika Code excels at API integration code while Llama 3.2 1B is better for Intent detection. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.

Detailed Analysis

Pricing Comparison

Vedika Code costs $0.04/1M input tokens and $0.06/1M output tokens. Llama 3.2 1B costs $0.004 input and $0.008 output. Llama 3.2 1B is 10.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Vedika Code has a 64K context window with ~250ms latency. Llama 3.2 1B offers 128K context at ~25ms. Llama 3.2 1B has the larger context window.

Best For

Vedika Code (Code) is optimized for: API integration code, Temple systems, SDK examples. Llama 3.2 1B (Compact) works best for: Intent detection, Routing, Edge classification.

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 Vedika Code
response_a = client.chat.completions.create(
    model="vedika-code",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Llama 3.2 1B
response_b = client.chat.completions.create(
    model="llama-3-2-1b",
    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, Vedika Code or Llama 3.2 1B?

Vedika Code (Code, 33B) offers Faith-tech code patterns. Llama 3.2 1B (Compact, 1B) offers Smallest footprint. Choose Vedika Code for API integration code or Llama 3.2 1B for Intent detection.

How much does Vedika Code cost vs Llama 3.2 1B?

Vedika Code: $0.04/1M input, $0.06/1M output. Llama 3.2 1B: $0.004/1M input, $0.008/1M 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 Vedika Code and Llama 3.2 1B 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.