Knowledge Base

Choosing the Right OpenAI API Model

By
John P. Martin
October 16, 2025
API Models

Selecting the Best Model for Research and Administrative Work

Picking the right model affects speed, accuracy, and cost. Columbia users have access to multiple OpenAI models under our Education license.

This article will show what each model does best, when to use them, and how to reduce costs.

Models

  • Most capable model
  • Text, images, audio
  • Strong reasoning and long-context handling
  • Good speed and value for complex work

Best for

  • Research synthesis and complex analysis
  • Extracting data from PDFs, tables, figures
  • Long reports or grant proposals
  • Meeting and lecture transcripts
  • Strong writing and reasoning
  • Large context (~128k)
  • Balanced cost and quality
     

Best for

  • Research summaries, proposals, reports
  • Detailed administrative documentation
  • Consistent, structured output
  • Text, image, audio
  • Near real-time interaction
  • Great for visual and meeting workflows
     

Best for

  • Analyzing charts, images, slide decks
  • Transcribing and summarizing recordings
  • Voice-based assistants or live note-taking
  • Fast and low-cost
  • Best for text-only tasks
     

Best for

  • Email and memo drafting
  • Short summaries
  • Routine administrative communication
  • text-embedding-3-small and text-embedding-3-large
  • Turn text into vectors for search and retrieval
  • Core to Retrieval-Augmented Generation (RAG)
     

Best for

  • Searchable document libraries
  • Knowledge bases that cite sources
  • Feeding only relevant context to GPT-4/GPT-5

Model Comparison

Choosing the Right Model

Best Practices

  • Group similar prompts in one request when possible.
  • Example: summarize five short reports in one call.
  • Cuts network overhead and setup tokens.

OpenAI charges for every token sent or received. Tokens are small chunks of text (about four characters each). Fewer tokens means lower cost. You can view current pricing on the OpenAI API Pricing page.

How to control it

  • Keep prompts short and focused.
  • Remove repeated instructions.
  • Use max_tokens to cap output length.
  • Track average tokens per request and drive it down over time.

Example

Each API call uses 3,000 tokens. You make 100 calls → 300,000 tokens billed.
Reduce usage by 25% → each call uses 2,250 tokens → total 225,000 tokens.
You save 75,000 tokens — about a 25% cost reduction for the same number of calls.

  • Don’t use GPT-5 for simple emails.
  • GPT-3.5 for routine drafts.
  • GPT-4 Turbo for higher-stakes writing.
  • GPT-5 for hard reasoning and multimodal work.
  • Set temperature low (0.2–0.4) for factual tasks.
  • Use higher values for brainstorming.
  • Store common prompts and responses.
  • Avoid paying for the same work twice.
  • Embed documents once.
  • Retrieve top matches at query time.
  • Feed small, relevant chunks to the model.

Optimization and Tuning

For deeper gains, use OpenAI’s model optimization methods. See Model Optimization.

  • Train on your examples for style and domain language.
  • Great for repeated templates and consistent tone.
  • Often reduces prompt size and total tokens.
  • Teach a smaller model using outputs from a larger one.
  • Keep quality while lowering cost and latency.
  • Define what “good” looks like: accuracy, consistency, structure.
  • Measure results, compare variants, catch drift.
  1. Start with prompting + RAG.
  2. Collect strong input/output pairs.
  3. Build evals and track metrics.
  4. Fine-tune or distill if prompt edits plateau.
  5. Monitor cost and quality over time.

Example Workflows

  1. Embed papers with text-embedding-3-large.
  2. Retrieve the top 5 sections.
  3. Send those sections to GPT-5 for synthesis.
  4. Return a concise, referenced summary.
  1. Use GPT-3.5 Turbo for bulk drafts or notes.
  2. Batch prompts where possible.
  3. Limit length with max_tokens.
  4. Switch to GPT-4 Turbo when tone and nuance matter.

Recommendations

  • GPT-5: default for complex research and multimodal tasks.
  • GPT-4 Turbo: structured writing and reports.
  • GPT-4o: visual or real-time work.
  • GPT-3.5 Turbo: high-volume administrative tasks.
  • Embeddings: search, retrieval, and lower token use.
     

Need API access? Request access on CUIT’s ChatGPT for Education page.

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Tags
Generative AI Artificial Intelligence