Nemotron 3 Nano vs Sarvam 30B
Nemotron 3 Nano (2025) and Sarvam 30B (2026) are compact production models from NVIDIA AI and Sarvam.ai. Nemotron 3 Nano ships a 256k-token context window, while Sarvam 30B ships a 66k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
Sarvam 30B is safer overall; choose Nemotron 3 Nano when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Nemotron 3 Nano | Sarvam 30B |
|---|---|---|
| Best for | tool-calling agents | tool-calling agents |
| Decision fit | RAG, Agents, and Long context | Agents and JSON / Tool use |
| Context window | 256k | 66k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Nemotron 3 Nano has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Nemotron 3 Nano has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Nemotron 3 Nano for RAG, Agents, and Long context.
- Local decision data tags Sarvam 30B for Agents and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Nemotron 3 Nano
Unavailable
No complete token price in local provider data
Sarvam 30B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Nemotron 3 Nano and Sarvam 30B; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Sarvam 30B and Nemotron 3 Nano; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-15 | 2026-03-22 |
| Context window | 256k | 66k |
| Parameters | 3.97B | 30B (2.4B active) |
| Architecture | Mixture of Experts | Mixture of Experts |
| License | NVIDIA Open Model | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Commercial use | Commercial use: permitted | Commercial use: permitted |
| Knowledge cutoff | - | 2025-06 |
Pricing and availability
| Pricing attribute | Nemotron 3 Nano | Sarvam 30B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Nemotron 3 Nano | Sarvam 30B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available for this pair.
Deep dive
The capability footprint is close: both models cover function calling and tool use. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Nemotron 3 Nano has no token price sourced yet and Sarvam 30B has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Nemotron 3 Nano when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Sarvam 30B when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Nemotron 3 Nano or Sarvam 30B?
Nemotron 3 Nano supports 256k tokens, while Sarvam 30B supports 66k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Nemotron 3 Nano or Sarvam 30B open source?
Nemotron 3 Nano is listed under NVIDIA Open Model. Sarvam 30B is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for function calling, Nemotron 3 Nano or Sarvam 30B?
Both Nemotron 3 Nano and Sarvam 30B expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for tool use, Nemotron 3 Nano or Sarvam 30B?
Both Nemotron 3 Nano and Sarvam 30B expose tool use. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Nemotron 3 Nano and Sarvam 30B?
Nemotron 3 Nano is available on NVIDIA NIM. Sarvam 30B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Nemotron 3 Nano over Sarvam 30B?
Sarvam 30B is safer overall; choose Nemotron 3 Nano when long-context analysis matters. If your workload also depends on long-context analysis, start with Nemotron 3 Nano; if it depends on provider fit, run the same evaluation with Sarvam 30B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.