Which OpenAI Model for Coding Tasks?
ChatGPT can work with GitHub repos now. Which models should I use?
Below is a “cheat sheet” that maps the three OpenAI families you mentioned to the things that matter most when you’re pair-programming or doing repo-wide surgery. Feel free to treat it as a menu—most teams end up mixing two tiers rather than betting on a single model.
1. Quick scorecard
Model Raw coding power Max context* Typical latency $/M tokens (in / out) Best use-case o3 ★★★★☆ (≈ 70 % SWE-bench) 200 K Slow (TTFT ≈ 6-8 s) 10 / 40 USD “Last-mile” debugging & architectural advice o4-mini-high ★★★½ (≈ 68 % SWE-bench) 200 K Medium (TTFT ≈ 4-5 s) 1.1 / 4.4 USD Everyday pair-programming and rapid Q&A GPT-4.1 ★★★☆ (54.6 % SWE-bench) 1 M Medium-slow (varies by tier) 3 / 9 USD† Repo-wide refactors & chat over huge diffs
* “Context” = input tokens you can feed in one shot.
† Pricing shown for GPT-4.1 “standard”; Nano/Mini tiers are cheaper but also weaker.
Sources: o3 & o4-mini context / pricing (OpenAI Help Center, Artificial Analysis); o3 SWE-bench ≈ 70 % (Reddit); o4-mini SWE-bench (Reddit); GPT-4.1 54.6 % SWE-bench and 1 M context (OpenAI, OpenAI).
2. How to choose
🔍 a) Context window
Need the whole repo in a single prompt? → GPT-4.1 is the only OpenAI model that lets you paste hundreds of thousands of lines at once (1 M tokens).
Working file-by-file or diff-by-diff? → 200 K in o3 / o4-mini is already enough for most PR-sized tasks (~150 files ≈ 40-50 K tokens).
🧠 b) Reasoning depth & correctness
o3 still posts the highest verified coding benchmark inside OpenAI’s lineup; when a race-condition bug is hiding three abstraction layers down, o3 is the one most likely to spot it.
o4-mini-high gives you ~95 % of o3’s brains for one-tenth the cost—good default for interactive sessions.
⚡ c) Latency & feedback cadence
Waiting 10 seconds for the first token feels fine when you’re running a one-off “audit this repo” job, but it drags in tight debugging loops. o4-mini-high (or even o4-mini-medium) shaves a couple of seconds off each round-trip.
💸 d) Cost ceilings
A half-hour chat that keeps o3 thinking hard over 50 K tokens of history can run $20-30. The same session on o4-mini-high is a couple of bucks.
GPT-4.1 sits in the middle, but if you actually exploit the 1 M-token context it quickly gets expensive.
🧰 e) Tool integration
All three expose the same function-calling, vision, and code-execution tooling in ChatGPT / the API, so there’s no tool lock-in penalty.
3. Practical playbook for Replit / Cursor / GitHub Copilot Chat
Situation Recommended tier “Why is this promise never resolving?” (one file, quick loop) o4-mini-high “Scan the whole KG branch and flag Postgres calls we missed.” GPT-4.1 (paste directory tree or embed via file-search) “We’ve tried three fixes, the bug persists—what are we missing?” o3 for a one-shot deep dive Daily stand-up helper / code-review bot o4-mini-medium or GPT-4.1 mini for cost control
4. Stacking the models
A workflow many teams like:
Default to o4-mini-high in your IDE chat (cheap + snappy).
When you hit a brick wall, promote the entire conversation thread to o3 (for tricky logic) or GPT-4.1 (when you need more context).
Keep the heavy model session short—once it explains the root cause or drafts a patch, drop back down to mini for incremental edits.
Cursor’s “Ask GPT-4.1” or Replit’s “↓ switch model” (hmm does this even exist?) buttons make the hop frictionless: just paste my diff or root-cause analysis straight into their chat.
Bottom line
o4-mini-high → your economical workhorse for 80 % of pair-programming loops.
o3 → pull it out like a senior engineer for the gnarly 20 %.
GPT-4.1 → the bulldozer for whole-repo context or massive diffs.
Start with mini-high, measure spend vs. unblock speed, and graduate up the ladder only when you really feel the pinch.