Llama Guard 4 12B vs Qwen 3.5 9B
Compare AI models side by side on benchmark scores, context window, modalities and capabilities, from the catalog’s own evidence.
Source: inferbase.ai
Compare AI models side by side on benchmark scores, context window, modalities and capabilities, from the catalog’s own evidence.
Meta AI makes Llama Guard 4 12B, an open-source multimodal chat model that takes text and images as input and generates text. It is designed as a safety classifier for tasks like content moderation, identifying violations in prompts and responses against a hazard taxonomy.
Qwen makes Qwen 3.5 9B, an open-source chat model that accepts text and image inputs and returns text outputs. It is designed for conversational tasks and can call tools for web search, code execution, and function calling through its API, and supports structured outputs and extended reasoning.
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Every figure on a comparison comes from the catalog: published specifications, capabilities as deployed, and benchmark scores by suite.
Specifications. Factual attributes published by the model's creator: context window, maximum output, parameters, modalities, license and release date. Open-source LLMs and frontier models compare on the same fields.
Capabilities. What each deployment honours, such as function calling, structured output and extended thinking, is checked against a provider's capability flag or the creator's documentation, and the entry keeps the source. A feature that has not been checked reads as unknown rather than as absent.
Benchmarks. Standardized test scores listed exactly as each source published them, grouped by source because scores from different suites are not comparable with each other. A cell reads as not available when a suite has not measured a model, rather than being filled with an estimate. Inferbase does not score the models itself.
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A comparison sets two to four such columns side by side; a figure missing here is missing there too. Open the entry for every suite and capability.