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Gemma 3n 2B (free) vs Llama Guard 3 8B

Gemma 3n 2B (free) (2025) and Llama Guard 3 8B (2024) are compact production models from Google DeepMind and AI at Meta. Gemma 3n 2B (free) ships a 8K-token context window, while Llama Guard 3 8B ships a 8K-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) is safer overall; choose Llama Guard 3 8B when provider fit matters.

Specs

Specification
Released2025-04-032024-07-23
Context window8K8K
Parameters8B
Architecturedecoder onlydecoder only
LicenseOpen SourceOpen Source
Knowledge cutoff--

Pricing and availability

Pricing attributeGemma 3n 2B (free)Llama Guard 3 8B
Input price-$0.2/1M tokens
Output price-$0.2/1M tokens
Providers

Capabilities

CapabilityGemma 3n 2B (free)Llama Guard 3 8B
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsNoYes
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on structured outputs: Llama Guard 3 8B. 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 3 8B has $0.2/1M input tokens. Provider availability is 1 tracked routes versus 4. 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 Llama Guard 3 8B 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 3 8B?

Gemma 3n 2B (free) supports 8K tokens, while Llama Guard 3 8B 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 Llama Guard 3 8B open source?

Gemma 3n 2B (free) is listed under Open Source. Llama Guard 3 8B 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 3 8B?

Llama Guard 3 8B 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 3 8B?

Gemma 3n 2B (free) is available on NVIDIA NIM. Llama Guard 3 8B is available on Microsoft Foundry, OpenRouter, Fireworks AI, and Replicate API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Gemma 3n 2B (free) over Llama Guard 3 8B?

Gemma 3n 2B (free) is safer overall; choose Llama Guard 3 8B when provider fit matters. If your workload also depends on provider fit, start with Gemma 3n 2B (free); if it depends on provider fit, run the same evaluation with Llama Guard 3 8B.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.