LLM Reference

Magistral Small 2506 vs Sarvam 30B

Magistral Small 2506 (2025) and Sarvam 30B (2026) are frontier reasoning models from MistralAI and Sarvam.ai. Magistral Small 2506 ships a 128k-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 Magistral Small 2506 when reasoning depth matters.

Decision scorecard

Local evidence first
SignalMagistral Small 2506Sarvam 30B
Best forreasoning-heavy appstool-calling agents
Decision fitLong contextAgents and JSON / Tool use
Context window128k66k
Cheapest output--
Provider routes1 tracked0 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Magistral Small 2506 when...
  • Magistral Small 2506 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Magistral Small 2506 has broader tracked provider coverage for fallback and procurement flexibility.
  • Magistral Small 2506 uniquely exposes Reasoning in local model data.
  • Local decision data tags Magistral Small 2506 for Long context.
Choose Sarvam 30B when...
  • 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.

Magistral Small 2506

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

Magistral Small 2506 -> Sarvam 30B
  • No overlapping tracked provider route is sourced for Magistral Small 2506 and Sarvam 30B; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Reasoning before moving production traffic.
  • Sarvam 30B adds Function calling and Tool use in local capability data.
Sarvam 30B -> Magistral Small 2506
  • No overlapping tracked provider route is sourced for Sarvam 30B and Magistral Small 2506; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Function calling and Tool use before moving production traffic.
  • Magistral Small 2506 adds Reasoning in local capability data.

Specs

Specification
Released2025-06-102026-03-22
Context window128k66k
Parameters24B30B (2.4B active)
Architecturedecoder onlymoe
LicenseApache 2.0(OSI)Apache 2.0(OSI)
OpennessOpen sourceOpen source
Commercial useCommercial use allowedCommercial use allowed
Knowledge cutoff2025-062025-06

Pricing and availability

Pricing attributeMagistral Small 2506Sarvam 30B
Input price--
Output price--
Providers-

Pricing not yet sourced for either model.

Capabilities

CapabilityMagistral Small 2506Sarvam 30B
VisionNoNo
MultimodalNoNo
ReasoningYesNo
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 reasoning mode: Magistral Small 2506, 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: Magistral Small 2506 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 Magistral Small 2506 when reasoning depth, 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.

FAQ

Which has a larger context window, Magistral Small 2506 or Sarvam 30B?

Magistral Small 2506 supports 128k 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 Magistral Small 2506 or Sarvam 30B open source?

Magistral Small 2506 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 reasoning mode, Magistral Small 2506 or Sarvam 30B?

Magistral Small 2506 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for function calling, Magistral Small 2506 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, Magistral Small 2506 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.

Where can I run Magistral Small 2506 and Sarvam 30B?

Magistral Small 2506 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.

Continue comparing

Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.