Navigating the Model Landscape

Several frontier models are available via API or product, each with different strengths. Choosing wisely among them — and knowing when to use smaller, faster models — has significant cost and quality implications.

The frontier model landscape (2025-2026)

OpenAI (GPT-4o, o1, o3) — strong all-round capability, widest ecosystem, deepest third-party integration. o-series models use chain-of-thought reasoning for complex analytical tasks.

Anthropic (Claude 3.5, Claude 3) — strong performance on long documents, nuanced writing, and tasks requiring careful instruction-following. Focus on safety and reduced hallucination rates.

Google (Gemini 1.5, 2.0) — very long context windows (up to 1M tokens), strong multimodal (text+image+video) capabilities, tight integration with Google Workspace.

Meta (Llama 3, 3.1) — open-weight models that can be run locally or on your own infrastructure. Significant for deployments with data sovereignty requirements or high-volume cost concerns.

Smaller models for production

Frontier models are not always the right choice. Smaller, faster, cheaper models (GPT-4o mini, Claude Haiku, Gemini Flash) perform well for simpler tasks — classification, extraction, summarisation of short documents — at a fraction of the cost. A production system should use the smallest model that reliably meets quality requirements for each task.

Evaluation before selection

Marketing benchmarks are designed to show models in their best light. For any significant deployment, run your own evaluation on representative examples from your actual use case before committing to a model. Model behaviour varies considerably between task types.

Model selection is a continuous process, not a one-time decision. The landscape changes quarterly, and the right model for your use case may change as models and pricing evolve.