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Gemma 2 9B Instruct vs Llama Guard 4 12B

Gemma 2 9B Instruct (2024) and Llama Guard 4 12B (2025) are compact production models from Google DeepMind and AI at Meta. Gemma 2 9B Instruct ships a 8K-token context window, while Llama Guard 4 12B ships a 164K-token context window. On pricing, Gemma 2 9B Instruct costs $0.1/1M input tokens versus $0.18/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Gemma 2 9B Instruct is ~80% cheaper at $0.1/1M; pay for Llama Guard 4 12B only for long-context analysis.

Specs

Specification
Released2024-06-272025-04-05
Context window8K164K
Parameters9B
Architecturedecoder onlydecoder only
LicenseOpen SourceOpen Source
Knowledge cutoff--

Pricing and availability

Pricing attributeGemma 2 9B InstructLlama Guard 4 12B
Input price$0.1/1M tokens$0.18/1M tokens
Output price$0.3/1M tokens$0.18/1M tokens
Providers

Capabilities

CapabilityGemma 2 9B InstructLlama Guard 4 12B
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsYesYes
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint is close: both models cover structured outputs. 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.

For cost, Gemma 2 9B Instruct lists $0.1/1M input and $0.3/1M output tokens, while Llama Guard 4 12B lists $0.18/1M input and $0.18/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 2 9B Instruct lower by about $0.02 per million blended tokens. Availability is 5 providers versus 3, so concentration risk also matters.

Choose Gemma 2 9B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Llama Guard 4 12B 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.

FAQ

Which has a larger context window, Gemma 2 9B Instruct or Llama Guard 4 12B?

Llama Guard 4 12B supports 164K tokens, while Gemma 2 9B Instruct supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, Gemma 2 9B Instruct or Llama Guard 4 12B?

Gemma 2 9B Instruct is cheaper on tracked token pricing. Gemma 2 9B Instruct costs $0.1/1M input and $0.3/1M output tokens. Llama Guard 4 12B costs $0.18/1M input and $0.18/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Gemma 2 9B Instruct or Llama Guard 4 12B open source?

Gemma 2 9B Instruct is listed under Open Source. Llama Guard 4 12B 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 9B Instruct or Llama Guard 4 12B?

Both Gemma 2 9B Instruct and Llama Guard 4 12B expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Where can I run Gemma 2 9B Instruct and Llama Guard 4 12B?

Gemma 2 9B Instruct is available on Fireworks AI, NVIDIA NIM, OpenRouter, Chutes AI, and Replicate API. Llama Guard 4 12B is available on NVIDIA NIM, Replicate API, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Gemma 2 9B Instruct over Llama Guard 4 12B?

Gemma 2 9B Instruct is ~80% cheaper at $0.1/1M; pay for Llama Guard 4 12B only for long-context analysis. If your workload also depends on provider fit, start with Gemma 2 9B Instruct; if it depends on long-context analysis, run the same evaluation with Llama Guard 4 12B.

Continue comparing

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