People are constantly debating which LLM is better for writing Elixir code, so I decided to compare the three SoA models from Google, OpenAI, and Antropic to see which one would be better in designing a medium-size feature for a medium-size project.

The project is ReqLLM, a wonderful new LLM library by @mikehostetler, and the feature is adding image generation support, a first part of Add Image Generation and Audio Transcription Support · Issue #14 · agentjido/req_llm · GitHub.

I’ve used Gemini 3 Pro, GPT 5.2, and Claude Opus 4.5 in gemini, codex, and claude code clis, respectively, with the same prompt. After each model wrote a plan, I asked each (in a separate session) to compare the three plans. The results are here: ReqLLM image support plans · GitHub

Bottom line:

• My ranking of the plans is GPT 5.2 > Opus 4.5 > Gemin 3 Pro; and each of the three models agreed with this assessments.
• Arguably, GPT’s is the only correct plan; while Opus’s plan works, it essentially introduces a parallel response parsing infrastructure, and would make it hard or impossible to extend the image support going forward, add streaming, etc.
• For some reason, Claude likes to write big implementation chunks as part of its plan
• Gemini’s is the least concrete and least accurate plan (and also uses the wrong image generation endpoints, for some reason).

This matches my experience working with Claude Code and Codex daily: while Claude Code has a nicer output, more features (like parallel/background execution), and works faster, Codex is much, much more thorough and most often generates higher quality code.

And also, the “/review” function in Codex is underrated. My current workflow is to always run a “/review”, for code written by me, Claude, or another codex. It excells at finding some very subtle edge cases and bugs that were introduced by the latest patch.