Llama 3.1 Swallow 70B Instruct vs Sarvam 30B
Llama 3.1 Swallow 70B Instruct (2025) and Sarvam 30B (2026) are compact production models from Tokyo Institute of Technology and Sarvam.ai. Llama 3.1 Swallow 70B Instruct ships a 4K-token context window, while Sarvam 30B ships a 65.5k-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.
Sarvam 30B fits 16x more tokens; pick it for long-context work and Llama 3.1 Swallow 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Swallow 70B Instruct | Sarvam 30B |
|---|---|---|
| Decision fit | General | Agents and JSON / Tool use |
| Context window | 4K | 65.5k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 Swallow 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- 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 prices on this page.
Llama 3.1 Swallow 70B Instruct
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 Llama 3.1 Swallow 70B Instruct and Sarvam 30B; plan for SDK, billing, or endpoint changes.
- Sarvam 30B adds Function calling and Tool use in local capability data.
- No overlapping tracked provider route is sourced for Sarvam 30B and Llama 3.1 Swallow 70B Instruct; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2026-03-22 |
| Context window | 4K | 65.5k |
| Parameters | 70B | 30B (2.4B active) |
| Architecture | decoder only | moe |
| License | 1 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 Swallow 70B Instruct | Sarvam 30B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.1 Swallow 70B Instruct | Sarvam 30B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| 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: Llama 3.1 Swallow 70B Instruct 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 Llama 3.1 Swallow 70B Instruct when provider fit and broader provider choice 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.
FAQ
Which has a larger context window, Llama 3.1 Swallow 70B Instruct or Sarvam 30B?
Sarvam 30B supports 65.5k tokens, while Llama 3.1 Swallow 70B Instruct supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.1 Swallow 70B Instruct or Sarvam 30B open source?
Llama 3.1 Swallow 70B Instruct is listed under 1. 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, Llama 3.1 Swallow 70B Instruct 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, Llama 3.1 Swallow 70B Instruct 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 Llama 3.1 Swallow 70B Instruct and Sarvam 30B?
Llama 3.1 Swallow 70B Instruct 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 Llama 3.1 Swallow 70B Instruct over Sarvam 30B?
Sarvam 30B fits 16x more tokens; pick it for long-context work and Llama 3.1 Swallow 70B Instruct for tighter calls. If your workload also depends on provider fit, start with Llama 3.1 Swallow 70B Instruct; if it depends on long-context analysis, run the same evaluation with Sarvam 30B.
Continue comparing
Last reviewed: 2026-05-01. Data sourced from public model cards and provider documentation.