LLM Reference

Marin 8B Base vs Sarvam 30B

Marin 8B Base (2025) and Sarvam 30B (2026) are compact production models from Marin and Sarvam.ai. Marin 8B Base ships a 4k-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 fits 16x more tokens; pick it for long-context work and Marin 8B Base for tighter calls.

Decision scorecard

Local evidence first
SignalMarin 8B BaseSarvam 30B
Best forgeneral production evaluationtool-calling agents
Decision fitGeneralAgents and JSON / Tool use
Context window4k66k
Cheapest output--
Provider routes0 tracked0 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Marin 8B Base when...
  • Use Marin 8B Base when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Choose Sarvam 30B when...
  • 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.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Marin 8B Base

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

Marin 8B Base -> Sarvam 30B
  • No overlapping tracked provider route is sourced for Marin 8B Base and Sarvam 30B; plan for SDK, billing, or endpoint changes.
  • Sarvam 30B adds Function calling and Tool use in local capability data.
Sarvam 30B -> Marin 8B Base
  • No overlapping tracked provider route is sourced for Sarvam 30B and Marin 8B Base; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Function calling and Tool use before moving production traffic.

Specs

Specification
Released2025-05-152026-03-22
Context window4k66k
Parameters8B30B (2.4B active)
Architecturedecoder onlymoe
LicenseApache 2.0(OSI)Apache 2.0(OSI)
OpennessOpen sourceOpen source
Commercial useCommercial use allowedCommercial use allowed
Knowledge cutoff2024-072025-06

Pricing and availability

Pricing attributeMarin 8B BaseSarvam 30B
Input price--
Output price--
Providers--

Pricing not yet sourced for either model.

Capabilities

CapabilityMarin 8B BaseSarvam 30B
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoYes
Tool useNoYes
Structured outputsNoNo
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

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: Marin 8B Base has no token price sourced yet and Sarvam 30B has no token price sourced yet. Provider availability is 0 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Marin 8B Base when provider fit are central to the workload. Choose Sarvam 30B when long-context analysis and larger context windows 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, Marin 8B Base or Sarvam 30B?

Sarvam 30B supports 66k tokens, while Marin 8B Base supports 4k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Marin 8B Base or Sarvam 30B open source?

Marin 8B Base is listed under Apache 2.0. 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, Marin 8B Base or Sarvam 30B?

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, Marin 8B Base or Sarvam 30B?

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.

When should I pick Marin 8B Base over Sarvam 30B?

Sarvam 30B fits 16x more tokens; pick it for long-context work and Marin 8B Base for tighter calls. If your workload also depends on provider fit, start with Marin 8B Base; if it depends on long-context analysis, run the same evaluation with Sarvam 30B.

Continue comparing

Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.