DeepSeek-R1 vs Sarvam-30B Comparison
Comparing DeepSeek-R1 and Sarvam-30B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and Sarvam-30B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek-R1 has 641.0B more parameters than Sarvam-30B, making it 2136.7% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while Sarvam-30B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while Sarvam-30B was released on 2026-03-06.
Sarvam-30B is 14 months newer than DeepSeek-R1.
Jan 20, 2025
1.1 years ago
Mar 6, 2026
1 weeks ago
1.1yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
Sarvam-30B
View detailsSarvam AI
Detailed Comparison
| Feature | | |
|---|