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Code Cushman 001 vs Llama Guard 4 12B

Code Cushman 001 (2021) and Llama Guard 4 12B (2025) are agentic coding models from OpenAI and AI at Meta. Code Cushman 001 ships a not-yet-sourced context window, while Llama Guard 4 12B ships a 164K-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.

Llama Guard 4 12B is safer overall; choose Code Cushman 001 when coding workflow support matters.

Specs

Released2021-11-032025-04-05
Context window—164K
Parameters——
Architecturedecoder onlydecoder only
LicenseProprietaryOpen Source
Knowledge cutoff--

Pricing and availability

Code Cushman 001Llama Guard 4 12B
Input price-$0.18/1M tokens
Output price-$0.18/1M tokens
Providers-
NVIDIA NIMReplicate APIOpenRouter

Capabilities

Code Cushman 001Llama 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 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.

Pricing coverage is uneven: Code Cushman 001 has no token price sourced yet and Llama Guard 4 12B has $0.18/1M input tokens. Provider availability is 0 tracked routes versus 3. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Code Cushman 001 when coding workflow support are central to the workload. Choose Llama Guard 4 12B 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

Is Code Cushman 001 or Llama Guard 4 12B open source?

Code Cushman 001 is listed under Proprietary. 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, Code Cushman 001 or Llama Guard 4 12B?

Llama Guard 4 12B 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 Code Cushman 001 and Llama Guard 4 12B?

Code Cushman 001 is available on the tracked providers still being sourced. 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 Code Cushman 001 over Llama Guard 4 12B?

Llama Guard 4 12B is safer overall; choose Code Cushman 001 when coding workflow support matters. If your workload also depends on coding workflow support, start with Code Cushman 001; if it depends on provider fit, run the same evaluation with Llama Guard 4 12B.

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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.