Llama 3.3 70B vs Llama Guard 4 12B
Llama 3.3 70B (2025) and Llama Guard 4 12B (2025) are compact production models from AI at Meta. Llama 3.3 70B ships a 8K-token context window, while Llama Guard 4 12B ships a 164K-token context window. On pricing, Llama Guard 4 12B costs $0.18/1M input tokens versus $0.9/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama Guard 4 12B is ~400% cheaper at $0.18/1M; pay for Llama 3.3 70B only for vision-heavy evaluation.
Specs
| Released | 2025-12-09 | 2025-04-05 |
| Context window | 8K | 164K |
| Parameters | 70B | — |
| Architecture | decoder only | decoder only |
| License | True | Open Source |
| Knowledge cutoff | 2024-12 | - |
Pricing and availability
| Llama 3.3 70B | Llama Guard 4 12B | |
|---|---|---|
| Input price | $0.9/1M tokens | $0.18/1M tokens |
| Output price | $0.9/1M tokens | $0.18/1M tokens |
| Providers |
Capabilities
| Llama 3.3 70B | Llama Guard 4 12B | |
|---|---|---|
| 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 vision: Llama 3.3 70B, multimodal input: Llama 3.3 70B, function calling: Llama 3.3 70B, tool use: Llama 3.3 70B, and structured outputs: Llama Guard 4 12B. 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, Llama 3.3 70B lists $0.9/1M input and $0.9/1M 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 Llama Guard 4 12B lower by about $0.72 per million blended tokens. Availability is 1 providers versus 3, so concentration risk also matters.
Choose Llama 3.3 70B when vision-heavy evaluation are central to the workload. Choose Llama Guard 4 12B when long-context analysis, larger context windows, 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.
FAQ
Which has a larger context window, Llama 3.3 70B or Llama Guard 4 12B?
Llama Guard 4 12B supports 164K tokens, while Llama 3.3 70B 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, Llama 3.3 70B or Llama Guard 4 12B?
Llama Guard 4 12B is cheaper on tracked token pricing. Llama 3.3 70B costs $0.9/1M input and $0.9/1M 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 Llama 3.3 70B or Llama Guard 4 12B open source?
Llama 3.3 70B is listed under True. 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 vision, Llama 3.3 70B or Llama Guard 4 12B?
Llama 3.3 70B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Llama 3.3 70B or Llama Guard 4 12B?
Llama 3.3 70B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 3.3 70B and Llama Guard 4 12B?
Llama 3.3 70B is available on Fireworks AI. 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.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.