Choosing the Right AI Model: Why One Size Doesn't Fit All

No single model is best for every task. Enterprise automation works best when the right model is used for the right job — balancing cost, accuracy, speed, and where your data can run (cloud vs on-premise). Here is a practical decision framework and how we apply it.

When to use which model

Coding and tool-heavy tasks: GPT-5.3-Codex excels at long-running agentic coding, debugging, and tool use with interactive steering. Use it where execution and iteration over code or scripts are central.

Long-context reasoning: Claude Opus 4.6’s 1M-token context and adaptive thinking suit deep analysis over large documents (contracts, compliance, due diligence). Use it when a single decision needs the full context in one place.

Vision and multimodal: Google Vision API and Vertex AI Vision (and Gemini where relevant) are the go-to for image and video understanding — defect detection, shelf analysis, document capture, occupancy. Use them when the primary input is pixels, not text.

On-premise and open-weight: When data cannot leave your environment, we use open-weight models hosted on your infrastructure, often sourced and evaluated via HuggingFace. Trade-off is typically cost and control vs frontier capability.

Cost vs accuracy vs speed

Larger frontier models (GPT-5.3-Codex, Opus 4.6) offer the best quality and reasoning but at higher cost per token. Smaller or specialised models (e.g. Codex-Spark, smaller open-source) are faster and cheaper but may need more guardrails or human checks. Our platform routes by task type: simple extraction or classification can use a smaller model; complex reasoning or long context uses a frontier model. That keeps quality high where it matters and cost down where it doesn’t.

How ConvertToAI’s intelligence layer works

Our orchestration layer doesn’t fix a single model for everything. We maintain a model router that selects the best model for each task based on input type (text, image, document), required context length, latency requirements, and your compliance constraints (e.g. on-prem only). So one workflow might use Vision for document ingestion, Opus 4.6 for contract analysis, and a smaller model for routing and logging. You get the right tool per step. For more on the architecture, see our platform overview.