Gemma 2 27B Instruct vs Llama Guard 3 1B
Gemma 2 27B Instruct (2024) and Llama Guard 3 1B (2024) are compact production models from Google DeepMind and AI at Meta. Gemma 2 27B Instruct ships a 8K-token context window, while Llama Guard 3 1B ships a not-yet-sourced context window. On pricing, Llama Guard 3 1B costs $0.1/1M input tokens versus $0.25/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama Guard 3 1B is ~150% cheaper at $0.1/1M; pay for Gemma 2 27B Instruct only for provider fit.
Specs
| Released | 2024-06-27 | 2024-09-25 |
| Context window | 8K | — |
| Parameters | 27B | 1B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Gemma 2 27B Instruct | Llama Guard 3 1B | |
|---|---|---|
| Input price | $0.25/1M tokens | $0.1/1M tokens |
| Output price | $0.75/1M tokens | $0.1/1M tokens |
| Providers |
Capabilities
| Gemma 2 27B Instruct | Llama Guard 3 1B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Gemma 2 27B Instruct. 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.
For cost, Gemma 2 27B Instruct lists $0.25/1M input and $0.75/1M output tokens, while Llama Guard 3 1B lists $0.1/1M input and $0.1/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama Guard 3 1B lower by about $0.3 per million blended tokens. Availability is 5 providers versus 1, so concentration risk also matters.
Choose Gemma 2 27B Instruct when provider fit and broader provider choice are central to the workload. Choose Llama Guard 3 1B when provider fit and lower input-token cost 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 is cheaper, Gemma 2 27B Instruct or Llama Guard 3 1B?
Llama Guard 3 1B is cheaper on tracked token pricing. Gemma 2 27B Instruct costs $0.25/1M input and $0.75/1M output tokens. Llama Guard 3 1B costs $0.1/1M input and $0.1/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemma 2 27B Instruct or Llama Guard 3 1B open source?
Gemma 2 27B Instruct is listed under Open Source. Llama Guard 3 1B 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 2 27B Instruct or Llama Guard 3 1B?
Gemma 2 27B Instruct 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 2 27B Instruct and Llama Guard 3 1B?
Gemma 2 27B Instruct is available on NVIDIA NIM, OpenRouter, Fireworks AI, Arcee AI, and Replicate API. Llama Guard 3 1B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 2 27B Instruct over Llama Guard 3 1B?
Llama Guard 3 1B is ~150% cheaper at $0.1/1M; pay for Gemma 2 27B Instruct only for provider fit. If your workload also depends on provider fit, start with Gemma 2 27B Instruct; if it depends on provider fit, run the same evaluation with Llama Guard 3 1B.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.