Gemma 3n 2B (free) vs Llama Guard 7B
Gemma 3n 2B (free) (2025) and Llama Guard 7B (2023) are compact production models from Google DeepMind and AI at Meta. Gemma 3n 2B (free) ships a 8K-token context window, while Llama Guard 7B ships a 2K-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.
Gemma 3n 2B (free) fits 4x more tokens; pick it for long-context work and Llama Guard 7B for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 3n 2B (free) | Llama Guard 7B |
|---|---|---|
| Decision fit | General | Classification and JSON / Tool use |
| Context window | 8K | 2K |
| Cheapest output | - | $0.2/1M tokens |
| Provider routes | 1 tracked | 3 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.
- Llama Guard 7B has broader tracked provider coverage for fallback and procurement flexibility.
- Llama Guard 7B uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama Guard 7B for Classification and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 3n 2B (free)
Unavailable
No complete token price in local provider data
Llama Guard 7B
$210
Cheapest tracked route: Together AI
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 3n 2B (free) and Llama Guard 7B; plan for SDK, billing, or endpoint changes.
- Llama Guard 7B adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama Guard 7B and Gemma 3n 2B (free); plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-03 | 2023-12-07 |
| Context window | 8K | 2K |
| Parameters | — | 7B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | 2024-06 | 2022-09 |
Pricing and availability
| Pricing attribute | Gemma 3n 2B (free) | Llama Guard 7B |
|---|---|---|
| Input price | - | $0.2/1M tokens |
| Output price | - | $0.2/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 3n 2B (free) | Llama Guard 7B |
|---|---|---|
| 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 |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Llama Guard 7B. 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 Llama Guard 7B has $0.2/1M input tokens. Provider availability is 1 tracked routes versus 3. 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 Llama Guard 7B 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 Llama Guard 7B?
Gemma 3n 2B (free) supports 8K tokens, while Llama Guard 7B supports 2K 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 Llama Guard 7B open source?
Gemma 3n 2B (free) is listed under Open Source. Llama Guard 7B is listed under Open Source. 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 Llama Guard 7B?
Llama Guard 7B 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 Llama Guard 7B?
Gemma 3n 2B (free) is available on NVIDIA NIM. Llama Guard 7B is available on Cloudflare Workers AI, Together AI, and Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3n 2B (free) over Llama Guard 7B?
Gemma 3n 2B (free) fits 4x more tokens; pick it for long-context work and Llama Guard 7B for tighter calls. 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 Llama Guard 7B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.