Choosing the right large language model should not require browsing five different provider dashboards. A complete model catalog gives developers a single view of available capabilities, from general-purpose chat models to specialized code, vision, and audio systems. Without it, teams waste cycles on integration fragmentation instead of shipping features.
What Defines a Complete Model Catalog
A useful catalog is more than a model list. It maps architectures to tasks, exposes context windows, and clarifies how each endpoint behaves under load. Developers need to know whether a model supports function calling, JSON mode, or vision input before they write the first line of client code. The best catalogs also standardize access, so switching from a 32B parameter model to a 671B mixture-of-experts system does not require rewriting your client.
Categories to Look For
Modern inference platforms organize offerings into clear categories. At minimum, a comprehensive catalog should cover these seven areas:
- LLMs and reasoning: General chat, multilingual reasoning, and deep chain-of-thought systems. Examples include Qwen 3 32B, Llama 3.3 70B, DeepSeek R1 671B MoE, GPT-Oss 120B, DeepSeek V4 Flash, Kimi K2.6, Kimi K2.5, Kimi K2 Thinking, GLM 5, and Minimax M2.5.
- Code: Models tuned for completion and agentic coding, such as Qwen 3 Coder 30B, DeepSeek Coder, and Oxlo.ai Coder Fast.
- Vision: Multimodal models that accept image input, including Gemma 3 27B and Kimi VL A3B.
- Image generation: Text-to-image endpoints like Oxlo.ai Image Pro, Oxlo.ai Image Ultra, Flux.1, SDXL, and Stable Diffusion 3.5.
- Audio: Speech-to-text and text-to-speech models such as Whisper Large v3, Whisper Turbo, Whisper Medium, and Kokoro 82M.
- Embeddings: Dense retrieval models including BGE-Large and E5-Large.
- Object detection: Computer vision models like YOLOv9 and YOLOv11.
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