Executive take
Quick answer
GPT-4o from OpenAI is fast, reliable at following structured output formats, and handles images and files well. It is the default choice for most coding assistance, data extraction, and tasks where you need consistent formatting. Strong for customer-facing content and anything that requires broad general knowledge.
Perspective
Business leader
Using one AI tool for everything is like using one supplier for every category. The right model for the job saves time and produces better outputs.
Why this matters for this role
- Model choice affects output quality, cost, and data handling.
- Defaulting to one provider is a procurement habit, not a strategy.
What this role should do
- Map your three most frequent AI tasks to the model best suited for them.
- Ask your technology lead for a simple one-page model guide for the team.
Watchouts
- Vendor lock-in is real in AI tooling.
- Do not assume the most expensive model is always the best for your specific use case.
GPT-4o - Speed, structure, and multimodal tasks
GPT-4o from OpenAI is fast, reliable at following structured output formats, and handles images and files well. It is the default choice for most coding assistance, data extraction, and tasks where you need consistent formatting. Strong for customer-facing content and anything that requires broad general knowledge.
Claude - Long documents, nuance, and careful reasoning
Anthropic's Claude handles very long contexts better than most models, which makes it strong for summarising long reports, contracts, and research papers. It is also noticeably more careful about hedging claims and flagging uncertainty. Use it when accuracy and nuance matter more than speed.
Gemini - Google Workspace and multimodal depth
Google's Gemini is the strongest choice when you are deep in Google Workspace and want native integration with Docs, Sheets, and Drive. Gemini Ultra handles complex reasoning tasks competitively with the top tier. It is also strong on very long video and multimodal analysis.
Llama and open source - Control and cost at scale
Meta's Llama and other open models can be run locally or self-hosted, which matters when you have strict data residency, privacy, or cost requirements. Performance is now competitive for many tasks. The trade-off is infrastructure overhead and the need to evaluate and update models yourself.
How to choose
Map your tasks into four buckets: writing and editing, research and analysis, coding and technical work, and sensitive or long-document work. Then assign a primary model to each. Most teams only need two or three models. The goal is a simple policy your team can actually follow.
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