Vedika Pro Ultra vs Gemma 3 27B
Compare Vedika Pro Ultra and Gemma 3 27B: 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 | Gemma 3 27B |
|---|---|---|
| Category | Domain Specialist | Open Source |
| Parameters | 120B | 27B |
| Context Window | 256K | 128K |
| Input Price | $0.12/1M tokens | $0.03/1M tokens |
| Output Price | $0.20/1M tokens | $0.05/1M tokens |
| Latency | ~600ms | ~180ms |
Choose Vedika Pro Ultra when:
- ✓ Kundali matching reports
- ✓ Multi-chart analysis
- ✓ Enterprise platforms
256K context, Deep yoga reasoning, Multi-system comparison
Choose Gemma 3 27B when:
- ✓ Fast chatbots
- ✓ Content moderation
- ✓ Temple kiosks
Fast inference, Reliable output, Strong English/Hindi
Verdict: Vedika Pro Ultra vs Gemma 3 27B
For cost efficiency, Gemma 3 27B wins at $0.03/1M input tokens. For speed, Gemma 3 27B is faster at ~180ms. Vedika Pro Ultra excels at Kundali matching reports while Gemma 3 27B is better for Fast chatbots. 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. Gemma 3 27B costs $0.03 input and $0.05 output. Gemma 3 27B is 4.0x 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. Gemma 3 27B offers 128K context at ~180ms. 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. Gemma 3 27B (Open Source) works best for: Fast chatbots, Content moderation, Temple kiosks.
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 Gemma 3 27B
response_b = client.chat.completions.create(
model="gemma-3-27b",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Vedika Pro Ultra or Gemma 3 27B?
Vedika Pro Ultra (Domain Specialist, 120B) offers 256K context. Gemma 3 27B (Open Source, 27B) offers Fast inference. Choose Vedika Pro Ultra for Kundali matching reports or Gemma 3 27B for Fast chatbots.
How much does Vedika Pro Ultra cost vs Gemma 3 27B?
Vedika Pro Ultra: $0.12/1M input, $0.20/1M output. Gemma 3 27B: $0.03/1M input, $0.05/1M 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 Gemma 3 27B 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.