Sarvam 30B vs ShieldGemma 9B
Sarvam 30B (2026) and ShieldGemma 9B (2024) are compact production models from Sarvam.ai and Google DeepMind. Sarvam 30B ships a 65.5k-token context window, while ShieldGemma 9B ships a 8K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
Sarvam 30B fits 8x more tokens; pick it for long-context work and ShieldGemma 9B for tighter calls.
Decision scorecard
Local evidence first| Signal | Sarvam 30B | ShieldGemma 9B |
|---|---|---|
| Decision fit | Agents and JSON / Tool use | Classification |
| Context window | 65.5k | 8K |
| Cheapest output | - | - |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Sarvam 30B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Sarvam 30B uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags Sarvam 30B for Agents and JSON / Tool use.
- ShieldGemma 9B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags ShieldGemma 9B for Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Sarvam 30B
Unavailable
No complete token price in local provider data
ShieldGemma 9B
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 Sarvam 30B and ShieldGemma 9B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
- No overlapping tracked provider route is sourced for ShieldGemma 9B and Sarvam 30B; plan for SDK, billing, or endpoint changes.
- Sarvam 30B adds Function calling and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-22 | 2024-07-01 |
| Context window | 65.5k | 8K |
| Parameters | 30B (2.4B active) | 9B |
| Architecture | moe | decoder only |
| License | Apache 2.0 | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Sarvam 30B | ShieldGemma 9B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Sarvam 30B | ShieldGemma 9B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | No | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: Sarvam 30B and tool use: Sarvam 30B. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Sarvam 30B has no token price sourced yet and ShieldGemma 9B has no token price sourced yet. Provider availability is 0 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Sarvam 30B when long-context analysis and larger context windows are central to the workload. Choose ShieldGemma 9B when provider fit and broader provider choice 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, Sarvam 30B or ShieldGemma 9B?
Sarvam 30B supports 65.5k tokens, while ShieldGemma 9B supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Sarvam 30B or ShieldGemma 9B open source?
Sarvam 30B is listed under Apache 2.0. ShieldGemma 9B is listed under 1. 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, Sarvam 30B or ShieldGemma 9B?
Sarvam 30B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, Sarvam 30B or ShieldGemma 9B?
Sarvam 30B has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Sarvam 30B and ShieldGemma 9B?
Sarvam 30B is available on the tracked providers still being sourced. ShieldGemma 9B is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Sarvam 30B over ShieldGemma 9B?
Sarvam 30B fits 8x more tokens; pick it for long-context work and ShieldGemma 9B for tighter calls. If your workload also depends on long-context analysis, start with Sarvam 30B; if it depends on provider fit, run the same evaluation with ShieldGemma 9B.
Continue comparing
Last reviewed: 2026-05-01. Data sourced from public model cards and provider documentation.