Indic open-weight comparison
Frontier comparisonSarvam 30B vs Sarvam 105B: Complete Comparison
Sarvam 30B and Sarvam 105B are Apache 2.0 MoE chat models from Sarvam AI for Indian-language and English workloads.
Featured · Updated today · Last verified: September 2026 · Score 96
Choose Sarvam 30B when
High-throughput Indic chat, support assistants, and voice-agent backends.
Choose Sarvam 105B when
Flagship Indic reasoning, coding, long-document analysis, and agent workflows.
Short verdict
Sarvam 30B is the efficient Indic MoE. Sarvam 105B is the flagship Indic reasoning model.
Key differences
30B documents 2.4B active parameters and 64K context. 105B documents 128K context and stronger agentic positioning.
Best for
Pick 30B for conversational throughput. Pick 105B when answer quality and long context dominate.
Reasoning fit
Use the same Indic prompt suite with thinking settings tuned per model.
Coding workflow fit
105B for hard repo tasks; 30B for inline assist latency.
Multimodal fit
Text-only here.
Enterprise fit
Both Apache 2.0; procurement may still require security review.
Who should not choose this?
- Do not choose 30B for hardest agent benchmarks without testing 105B.
- Do not choose 105B for lowest-latency voice without cost modeling.
- Do not treat as Sarvam vs DeepSeek—that is a separate page.
Cost considerations
Active parameters drive inference cost more than headline MoE totals.
Limitations
September 2026 verification.
Final recommendation
Default 30B for user-facing chat and voice. Default 105B for back-office analysis and agents.