Vedika Pro Ultra vs Llama 3.2 11B Vision
Compare Vedika Pro Ultra and Llama 3.2 11B Vision: 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 | Vedika Pro Ultra | Llama 3.2 11B Vision |
|---|---|---|
| Category | Domain Specialist | Vision |
| Parameters | 120B | 11B |
| Context Window | 256K | 128K |
| Input Price | $0.12/1M tokens | $0.02/1M tokens |
| Output Price | $0.20/1M tokens | $0.04/1M tokens |
| Latency | ~600ms | ~200ms |
Choose Vedika Pro Ultra when:
- ✓ Kundali matching reports
- ✓ Multi-chart analysis
- ✓ Enterprise platforms
256K context, Deep yoga reasoning, Multi-system comparison
Choose Llama 3.2 11B Vision when:
- ✓ Image classification
- ✓ OCR
- ✓ Simple visual Q&A
Low cost vision, Fast, Compact
Verdict: Vedika Pro Ultra vs Llama 3.2 11B Vision
For cost efficiency, Llama 3.2 11B Vision wins at $0.02/1M input tokens. For speed, Llama 3.2 11B Vision is faster at ~200ms. Vedika Pro Ultra excels at Kundali matching reports while Llama 3.2 11B Vision is better for Image classification. 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 Pro Ultra costs $0.12/1M input tokens and $0.20/1M output tokens. Llama 3.2 11B Vision costs $0.02 input and $0.04 output. Llama 3.2 11B Vision is 6.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Vedika Pro Ultra has a 256K context window with ~600ms latency. Llama 3.2 11B Vision offers 128K context at ~200ms. Vedika Pro Ultra has the larger context window.
Best For
Vedika Pro Ultra (Domain Specialist) is optimized for: Kundali matching reports, Multi-chart analysis, Enterprise platforms. Llama 3.2 11B Vision (Vision) works best for: Image classification, OCR, Simple visual Q&A.
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 Pro Ultra
response_a = client.chat.completions.create(
model="vedika-pro-ultra",
messages=[{"role": "user", "content": "Your question here"}]
)
# Use Llama 3.2 11B Vision
response_b = client.chat.completions.create(
model="llama-3-2-11b-vision",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Vedika Pro Ultra or Llama 3.2 11B Vision?
Vedika Pro Ultra (Domain Specialist, 120B) offers 256K context. Llama 3.2 11B Vision (Vision, 11B) offers Low cost vision. Choose Vedika Pro Ultra for Kundali matching reports or Llama 3.2 11B Vision for Image classification.
How much does Vedika Pro Ultra cost vs Llama 3.2 11B Vision?
Vedika Pro Ultra: $0.12/1M input, $0.20/1M output. Llama 3.2 11B Vision: $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 Vedika Pro Ultra and Llama 3.2 11B Vision 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.