Vedika Pro Ultra vs Llama 4 Scout
Compare Vedika Pro Ultra and Llama 4 Scout: 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 4 Scout |
|---|---|---|
| Category | Domain Specialist | Open Source |
| Parameters | 120B | 109B (17B active) |
| Context Window | 256K | 512K |
| Input Price | $0.12/1M tokens | $0.05/1M tokens |
| Output Price | $0.20/1M tokens | $0.08/1M tokens |
| Latency | ~600ms | ~350ms |
Choose Vedika Pro Ultra when:
- ✓ Kundali matching reports
- ✓ Multi-chart analysis
- ✓ Enterprise platforms
256K context, Deep yoga reasoning, Multi-system comparison
Choose Llama 4 Scout when:
- ✓ Classical text analysis
- ✓ Long content
- ✓ Multi-turn
512K context, MoE efficiency, Strong multilingual
Verdict: Vedika Pro Ultra vs Llama 4 Scout
For cost efficiency, Llama 4 Scout wins at $0.05/1M input tokens. For speed, Llama 4 Scout is faster at ~350ms. Vedika Pro Ultra excels at Kundali matching reports while Llama 4 Scout is better for Classical text analysis. 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 4 Scout costs $0.05 input and $0.08 output. Llama 4 Scout is 2.4x 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 4 Scout offers 512K context at ~350ms. Llama 4 Scout has the larger context window.
Best For
Vedika Pro Ultra (Domain Specialist) is optimized for: Kundali matching reports, Multi-chart analysis, Enterprise platforms. Llama 4 Scout (Open Source) works best for: Classical text analysis, Long content, Multi-turn.
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 4 Scout
response_b = client.chat.completions.create(
model="llama-4-scout",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Vedika Pro Ultra or Llama 4 Scout?
Vedika Pro Ultra (Domain Specialist, 120B) offers 256K context. Llama 4 Scout (Open Source, 109B (17B active)) offers 512K context. Choose Vedika Pro Ultra for Kundali matching reports or Llama 4 Scout for Classical text analysis.
How much does Vedika Pro Ultra cost vs Llama 4 Scout?
Vedika Pro Ultra: $0.12/1M input, $0.20/1M output. Llama 4 Scout: $0.05/1M input, $0.08/1M 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 4 Scout 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.