Gemma 3n 2B (free) vs Nemotron 4 340B
Gemma 3n 2B (free) (2025) and Nemotron 4 340B (2025) are compact production models from Google DeepMind and NVIDIA AI. Gemma 3n 2B (free) ships a 8k-token context window, while Nemotron 4 340B ships a 4k-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.
Gemma 3n 2B (free) is safer overall; choose Nemotron 4 340B when provider fit matters.
Decision scorecard
Local evidence first| Signal | Gemma 3n 2B (free) | Nemotron 4 340B |
|---|---|---|
| Best for | general production evaluation | provider-routed production |
| Decision fit | General | Classification and JSON / Tool use |
| Context window | 8k | 4k |
| Cheapest output | - | $4.20/1M tokens |
| Provider routes | 1 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 3n 2B (free) has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Nemotron 4 340B has broader tracked provider coverage for fallback and procurement flexibility.
- Nemotron 4 340B uniquely exposes Structured outputs in local model data.
- Local decision data tags Nemotron 4 340B for Classification and JSON / Tool use.
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
Nemotron 4 340B
$4,410
Cheapest tracked route/tier: DeepInfra
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.
- Nemotron 4 340B adds Structured outputs in local capability data.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-03 | 2025-02-27 |
| Context window | 8k | 4k |
| Parameters | 5B (2B effective active) | 340B |
| Architecture | decoder only | decoder only |
| License | Gemma | NVIDIA Open Model |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2024-06 | - |
Pricing and availability
| Pricing attribute | Gemma 3n 2B (free) | Nemotron 4 340B |
|---|---|---|
| Input price | - | $4.20/1M tokens |
| Output price | - | $4.20/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 3n 2B (free) | Nemotron 4 340B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| 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 differs most on structured outputs: Nemotron 4 340B. 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: Gemma 3n 2B (free) has no token price sourced yet and Nemotron 4 340B has $4.20/1M input tokens. Provider availability is 1 tracked routes versus 2. 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 long-context analysis and larger context windows are central to the workload. Choose Nemotron 4 340B 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, Gemma 3n 2B (free) or Nemotron 4 340B?
Gemma 3n 2B (free) supports 8k tokens, while Nemotron 4 340B supports 4k 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 Nemotron 4 340B open source?
Gemma 3n 2B (free) is listed under Gemma. Nemotron 4 340B is listed under NVIDIA Open Model. 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 structured outputs, Gemma 3n 2B (free) or Nemotron 4 340B?
Nemotron 4 340B has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Gemma 3n 2B (free) and Nemotron 4 340B?
Gemma 3n 2B (free) is available on NVIDIA NIM. Nemotron 4 340B is available on NVIDIA NIM and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3n 2B (free) over Nemotron 4 340B?
Gemma 3n 2B (free) is safer overall; choose Nemotron 4 340B when provider fit matters. If your workload also depends on long-context analysis, start with Gemma 3n 2B (free); if it depends on provider fit, run the same evaluation with Nemotron 4 340B.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.