Gemma 3n 2B (free) vs Sarvam-M Multilingual Hybrid
Gemma 3n 2B (free) (2025) and Sarvam-M Multilingual Hybrid (2025) are compact production models from Google DeepMind and Sarvam.ai. Gemma 3n 2B (free) ships a 8k-token context window, while Sarvam-M Multilingual Hybrid ships a 128k-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-M Multilingual Hybrid fits 16x more tokens; pick it for long-context work and Gemma 3n 2B (free) for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 3n 2B (free) | Sarvam-M Multilingual Hybrid |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | Long context |
| Context window | 8k | 128k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Gemma 3n 2B (free) when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Sarvam-M Multilingual Hybrid has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Sarvam-M Multilingual Hybrid for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemma 3n 2B (free)
Unavailable
No complete token price in local provider data
Sarvam-M Multilingual Hybrid
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-03 | 2025-06-01 |
| Context window | 8k | 128k |
| Parameters | 5B (2B effective active) | 24B |
| Architecture | decoder only | decoder only |
| License | Gemma | Proprietary |
| Openness | Open weights | Proprietary |
| Commercial use | Commercial use with conditions | - |
| Knowledge cutoff | 2024-06 | - |
Pricing and availability
| Pricing attribute | Gemma 3n 2B (free) | Sarvam-M Multilingual Hybrid |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Gemma 3n 2B (free) | Sarvam-M Multilingual Hybrid |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover the core production surface. 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: Gemma 3n 2B (free) has no token price sourced yet and Sarvam-M Multilingual Hybrid has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 3n 2B (free) when provider fit are central to the workload. Choose Sarvam-M Multilingual Hybrid 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, Gemma 3n 2B (free) or Sarvam-M Multilingual Hybrid?
Sarvam-M Multilingual Hybrid supports 128k tokens, while Gemma 3n 2B (free) supports 8k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 3n 2B (free) or Sarvam-M Multilingual Hybrid open source?
Gemma 3n 2B (free) is listed under Gemma. Sarvam-M Multilingual Hybrid is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Gemma 3n 2B (free) and Sarvam-M Multilingual Hybrid?
Gemma 3n 2B (free) is available on NVIDIA NIM. Sarvam-M Multilingual Hybrid is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick Gemma 3n 2B (free) over Sarvam-M Multilingual Hybrid?
Sarvam-M Multilingual Hybrid fits 16x more tokens; pick it for long-context work and Gemma 3n 2B (free) for tighter calls. If your workload also depends on provider fit, start with Gemma 3n 2B (free); if it depends on long-context analysis, run the same evaluation with Sarvam-M Multilingual Hybrid.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.