Introduction#
OpenAI now offers a family of models with different strengths: general chat, high-reasoning, multimodal, and compact variants. Choosing the right model saves money and reduces iteration time. This short guide explains the tradeoffs and gives practical picks for common tasks.
High-level model groups#
- GPT-5 family: Focused on advanced reasoning and large-context tasks. Use when you need deep logic, complex coding, or long documents.
- GPT-4.1 family: Versatile general-purpose models that balance capability and cost. Good for assistants, summarization, and many production workloads.
- GPT-4o family: Multimodal and audio capable. Useful if you need images and audio in the loop.
- Mini and nano variants: Lower cost, lower latency. Great for classification, short summaries, and fast prototyping.
- Open weights like gpt-oss-120b: Downloadable alternatives for fine-tuning or self-hosting when you need full control over weights.
Practical hint: treat the family name as a shorthand for capability and cost. If you are unsure start with a mid-tier model and profile cost and latency.
How to pick a model for common tasks#
- Chatty assistant and customer support: GPT-4.1 mini or standard GPT-4.1 for a balance between fluency and cost.
- Complex reasoning or research code: GPT-5 or GPT-5.2 where available, because they are tuned for heavy reasoning.
- Code generation and transformations: Use codex-branded variants or GPT-5.1 Codex when you need targeted code outputs.
- Image and audio processing: Use GPT-4o where both text and media inputs are required.
- Cost-sensitive batch jobs: Use nano or mini variants; run a small A/B to confirm quality.
Example: I switched a summarization job from a larger model to a nano variant. Latency dropped and cost fell by 80 percent while readability stayed acceptable. That small tradeoff felt risky at first but paid off.
Tips for real-world use#
- Profile on a representative dataset. Quality differences are subtle until you test at scale.
- Use temperature and system prompts to shape behavior rather than over-indexing on model size.
- Combine models. Use a cheap model for filtering and route hard examples to a higher-capacity model.
- Watch token usage carefully. Context-window size matters for long documents.
Conclusion#
Pick models based on the task, not the hype. For heavy reasoning or research pick the newest high-capacity model. For production chat and cost control prefer mid-tier models or mini variants. Start small, measure quality and cost, then route harder examples up the stack. If you want, try a two-model pipeline: cheap filter then high-capacity resolver.
Co-authored with Vishwakarma, Deeps 2nd Brain
