Sarvam-105B vs Sarvam-30B: Which Model to Use

Sarvam-105B vs Sarvam-30B: Why This Choice Actually Matters

Sarvam AI shipped two Mixture-of-Experts models trained from scratch on Indian compute — Sarvam-105B and Sarvam-30B — and both replaced the older Sarvam-M (24B), which is now deprecated on the API. That leaves a real decision: which one goes into production.

This is not a "bigger is always better" call. Sarvam-30B activates only 2.4B parameters per token and runs on Apple Silicon. Sarvam-105B activates roughly 10B per token and is built for server-grade reasoning and agentic workloads. Picking wrong means either overpaying for latency you did not need, or shipping an agent that cannot hold a multi-step tool-use loop together.

🎯 Quick Answer (30-Second Read)

• Main solution: Use sarvam-30b for latency-sensitive, high-volume workloads — voice pipelines, real-time chat, on-device inference. Use sarvam-105b for complex reasoning, coding, and agentic tool-use tasks.
• When to use each: 30B for cost-per-request efficiency at scale, 105B when output quality on hard reasoning tasks matters more than speed or price.
• Main benefit: Both are Apache 2.0, open-weight, and trained natively on Indic languages — no translation layer needed for either.
• Limitation: 30B trails on raw intelligence benchmarks; 105B costs more per token and carries higher latency.
• Recommendation: Default to 30B, upgrade specific calls to 105B only where your evals show the smaller model actually fails.

Choosing Between Sarvam-105B and Sarvam-30B

Architecture and Benchmark Differences

Scale and attention mechanism

Sarvam-30B has 32B total parameters with 2.4B active per token, trained on 16T tokens, using Grouped Query Attention (GQA) to keep KV-cache memory low. Sarvam-105B has 106B total parameters with roughly 10B active per token, trained on 12T tokens, and swaps in Multi-head Latent Attention (MLA) — a compressed attention format built specifically to keep long-context inference cheap on server hardware.

Both share the same backbone: a 128-expert sparse MoE Transformer, RoPE for positional encoding, and RMSNorm for stability. They differ in expert capacity and routing, which is how the 105B scales total parameters without blowing up per-token compute.

Where each one actually wins

On the Artificial Analysis Intelligence Index, Sarvam-105B scores 18 and Sarvam-30B scores 12 — both trail same-class peers like GLM-4.5-Air and Qwen3, which matters if you are benchmarking against global frontier models rather than Indic-specific ones. Where Sarvam-105B pulls ahead is agentic tasks: it posts a Tau2 score of 68.3, the highest among its comparison set, and an agentic index of 25 versus 20-21 for similarly sized peers. On Indic-language pairwise evaluation, both models perform close to each other — 105B wins 90% of comparisons overall, 30B wins 89% — which tells you the language quality gap between them is small. The real gap shows up in reasoning depth and tool-use reliability, not basic fluency.

The Better Way vs The Worst Way

The worst way: routing every request through Sarvam-105B by default because "it is the flagship," then discovering your voice pipeline latency doubled and your per-token cost went from ₹2.5 to ₹4 on input and ₹10 to ₹16 on output for tasks that never needed reasoning depth.

The better way: default every call to Sarvam-30B, run evals on your actual task set, and escalate to Sarvam-105B only for the specific call types — multi-step agent loops, complex coding, long-document reasoning — where 30B's pairwise win rate actually drops.

My Take

The deep reason this split exists is that Sarvam made an explicit compute-efficiency bet: instead of one model trying to do everything, they trained two models on different attention mechanisms — GQA for cheap high-throughput serving, MLA for long-context reasoning quality — and let developers route by task instead of forcing one model to compromise on both. Best case: teams building Indic voice agents and chat products get near-105B language quality at 30B latency and cost, because the pairwise win-rate gap between the two is tiny on basic fluency. Worst case: teams default to 105B everywhere out of flagship bias and pay 60%+ more per token for reasoning capacity they never use. Right now the open-weight Indic LLM space is still catching up to global frontier models on raw intelligence benchmarks, even as it closes the gap fast on language-specific tasks — that gap is the honest tradeoff of training entirely on Indian compute versus the much larger clusters frontier labs use. Where this is heading: expect Sarvam to keep shipping this two-tier pattern rather than one giant model, and expect more Indic-first teams to build routing logic between small and large models as a default architecture decision, not an afterthought.

Sarvam-105B vs Sarvam-30B: Spec Comparison

Real Developer Use Case

A team building a Hindi and Tamil voice agent for telephone banking needed sub-second response times on live calls. They started with Sarvam-105B for every turn and hit latency spikes during multi-turn conversations under call-center load.

The fix: route standard conversational turns — balance checks, FAQ, intent classification — through Sarvam-30B, and reserve Sarvam-105B for one specific step: when the intent classifier flags a complex dispute case that needs multi-step reasoning across transaction history. That single routing change cut average response latency while keeping the accuracy needed on the calls that actually required it.

Frequently Asked Questions

Is Sarvam-M still usable for new projects?
No. Sarvam-M (24B) is deprecated and no longer available through the Chat Completions API — both Sarvam-30B and Sarvam-105B outperform it and are the only supported chat models going forward.

Can Sarvam-30B run on-device?
Yes. With only 2.4B active parameters, Sarvam-30B runs locally on Apple Silicon via MXFP4 quantization and gets 3x–6x throughput gains on H100 GPUs, making it viable for edge and low-latency deployments.

Which model is better for agentic tool-use loops?
Sarvam-105B. It scores 68.3 on Tau2, a benchmark for long-horizon agentic reasoning, ahead of similarly sized peers — Sarvam-30B was not built with agentic tool-use as its primary target.

How different is the language quality between the two models?
Closer than the intelligence benchmarks suggest. Sarvam-105B wins 90% of pairwise Indic-language comparisons versus Sarvam-30B's 89%, so basic fluency and script correctness are nearly identical — the gap shows up in reasoning tasks, not language output.

What model name do I pass in the API?
Use model="sarvam-30b" or model="sarvam-105b" in the chat completions call. Both accept the same api-subscription-key header and OpenAI-compatible Authorization: Bearer format.

Conclusion

Default to Sarvam-30B for real-time, high-volume, cost-sensitive workloads — voice agents, chat, on-device inference. Reserve Sarvam-105B for the specific calls in your pipeline that need deep reasoning or multi-step agentic tool use. The teams that get this wrong almost always default to the bigger model everywhere; the ones that get it right route by task and let evals decide where the upgrade is actually worth the cost.

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