Vedika Seva Voice vs Gemma 3 12B
Compare Vedika Seva Voice and Gemma 3 12B: 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 Seva Voice | Gemma 3 12B |
|---|---|---|
| Category | Voice | Compact |
| Parameters | Pipeline | 12B |
| Context Window | 30s | 128K |
| Input Price | $0.015/min/1M tokens | $0.015/1M tokens |
| Output Price | $0.02/min/1M tokens | $0.03/1M tokens |
| Latency | ~300ms | ~100ms |
Choose Vedika Seva Voice when:
- ✓ Booking confirmations
- ✓ Queue updates
- ✓ Service info
Clear diction, Service-oriented, Fast response
Choose Gemma 3 12B when:
- ✓ Edge deployments
- ✓ Classification
- ✓ Simple chatbots
Very compact, Fast, Low cost
Verdict: Vedika Seva Voice vs Gemma 3 12B
For cost efficiency, Gemma 3 12B wins at $0.015/1M input tokens. For speed, Gemma 3 12B is faster at ~100ms. Vedika Seva Voice excels at Booking confirmations while Gemma 3 12B is better for Edge deployments. 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 Seva Voice costs $0.015/min/1M input tokens and $0.02/min/1M output tokens. Gemma 3 12B costs $0.015 input and $0.03 output. Both models are similarly priced. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Vedika Seva Voice has a 30s context window with ~300ms latency. Gemma 3 12B offers 128K context at ~100ms. Gemma 3 12B has the larger context window.
Best For
Vedika Seva Voice (Voice) is optimized for: Booking confirmations, Queue updates, Service info. Gemma 3 12B (Compact) works best for: Edge deployments, Classification, Simple chatbots.
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 Seva Voice
response_a = client.chat.completions.create(
model="vedika-seva-voice",
messages=[{"role": "user", "content": "Your question here"}]
)
# Use Gemma 3 12B
response_b = client.chat.completions.create(
model="gemma-3-12b",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Vedika Seva Voice or Gemma 3 12B?
Vedika Seva Voice (Voice, Pipeline) offers Clear diction. Gemma 3 12B (Compact, 12B) offers Very compact. Choose Vedika Seva Voice for Booking confirmations or Gemma 3 12B for Edge deployments.
How much does Vedika Seva Voice cost vs Gemma 3 12B?
Vedika Seva Voice: $0.015/min/1M input, $0.02/min/1M output. Gemma 3 12B: $0.015/1M input, $0.03/1M 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 Seva Voice and Gemma 3 12B 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.