Simon Willison’s Weblog

GPT‑5-Codex and upgrades to Codex. OpenAI half-released a new model today: GPT‑5-Codex, a fine-tuned GPT-5 variant explicitly designed for their various AI-assisted programming tools.

Update: OpenAI call it a "version of GPT-5", they don't explicitly describe it as a fine-tuned model. Calling it a fine-tune was my mistake here.

I say half-released because it's not yet available via their API, but they "plan to make GPT‑5-Codex available in the API soon".

I wrote about the confusing array of OpenAI products that share the name Codex a few months ago. This new model adds yet another, though at least "GPT-5-Codex" (using two hyphens) is unambiguous enough not to add to much more to the confusion.

At this point it's best to think of Codex as OpenAI's brand name for their coding family of models and tools.

The new model is already integrated into their VS Code extension, the Codex CLI and their Codex Cloud asynchronous coding agent. I'd been calling that last one "Codex Web" but I think Codex Cloud is a better name since it can also be accessed directly from their iPhone app.

Codex Cloud also has a new feature: you can configure it to automatically run code review against specific GitHub repositories (I found that option on chatgpt.com/codex/settings/code-review) and it will create a temporary container to use as part of those reviews. Here's the relevant documentation.

Some documented features of the new GPT-5-Codex model:

• Specifically trained for code review, which directly supports their new code review feature.
• "GPT‑5-Codex adapts how much time it spends thinking more dynamically based on the complexity of the task." Simple tasks (like "list files in this directory") should run faster. Large, complex tasks should use run for much longer - OpenAI report Codex crunching for seven hours in some cases!
• Increased score on their proprietary "code refactoring evaluation" from 33.9% for GPT-5 (high) to 51.3% for GPT-5-Codex (high). It's hard to evaluate this without seeing the details of the eval but it does at least illustrate that refactoring performance is something they've focused on here.
• "GPT‑5-Codex also shows significant improvements in human preference evaluations when creating mobile websites" - in the past I've habitually prompted models to "make it mobile-friendly", maybe I don't need to do that any more.
• "We find that comments by GPT‑5-Codex are less likely to be incorrect or unimportant" - I originally misinterpreted this as referring to comments in code but it's actually about comments left on code reviews.

The system prompt for GPT-5-Codex in Codex CLI is worth a read. It's notably shorter than the system prompt for other models - here's a diff.

Here's the section of the updated system prompt that talks about comments:

Add succinct code comments that explain what is going on if code is not self-explanatory. You should not add comments like "Assigns the value to the variable", but a brief comment might be useful ahead of a complex code block that the user would otherwise have to spend time parsing out. Usage of these comments should be rare.

Theo Browne has a video review of the model and accompanying features. He was generally impressed but noted that it was surprisingly bad at using the Codex CLI search tool to navigate code. Hopefully that's something that can fix with a system prompt update.

Finally, can it drew a pelican riding a bicycle? Without API access I instead got Codex Cloud to have a go by prompting:

Generate an SVG of a pelican riding a bicycle, save as pelican.svg

Here's the result:

# 15th September 2025, 6:55 pm / code-review, ai, openai, generative-ai, llms, ai-assisted-programming, pelican-riding-a-bicycle, llm-release, coding-agents, async-coding-agents, gpt-5, codex-cli, theo-browne

Coding with LLMs in the summer of 2025 (an update) (via) Salvatore Sanfilippo describes his current AI-assisted development workflow. He's all-in on LLMs for code review, exploratory prototyping, pair-design and writing "part of the code under your clear specifications", but warns against leaning too hard on pure vibe coding:

But while LLMs can write part of a code base with success (under your strict supervision, see later), and produce a very sensible speedup in development (or, the ability to develop more/better in the same time used in the past — which is what I do), when left alone with nontrivial goals they tend to produce fragile code bases that are larger than needed, complex, full of local minima choices, suboptimal in many ways. Moreover they just fail completely when the task at hand is more complex than a given level.

There are plenty of useful tips in there, especially around carefully managing your context:

When your goal is to reason with an LLM about implementing or fixing some code, you need to provide extensive information to the LLM: papers, big parts of the target code base (all the code base if possible, unless this is going to make the context window so large than the LLM performances will be impaired). And a brain dump of all your understanding of what should be done.

Salvatore warns against relying too hard on tools which hide the context for you, like editors with integrated coding agents. He prefers pasting exactly what's needed into the LLM web interface - I share his preference there.

His conclusions here match my experience:

You will be able to do things that are otherwise at the borders of your knowledge / expertise while learning much in the process (yes, you can learn from LLMs, as you can learn from books or colleagues: it is one of the forms of education possible, a new one). Yet, everything produced will follow your idea of code and product, and will be of high quality and will not random fail because of errors and shortcomings introduced by the LLM. You will also retain a strong understanding of all the code written and its design.

# 21st July 2025, 2:42 am / code-review, salvatore-sanfilippo, ai, generative-ai, llms, ai-assisted-programming, vibe-coding

How to Do Code Reviews Like a Human. Some very well thought out advice on giving constructive reviews, including simple but effective language tricks for keeping suggestions positive and helpful.

# 13th October 2017, 4:36 am / code-review

Design and code review requested for Django string signing / signed cookies. Do you know your way around web app security and cryptography (in particular signing things using hmac and sha1)? We’d appreciate your help reviewing the usage of these concepts in Django’s proposed string signing and signed cookie implementations.

# 4th January 2010, 1:24 pm / code-review, cryptography, django, hashing, hmac, python, security, sha1

How to use Google Code’s code review tool. I missed this, but Google Code now has a pleasantly simple code review system built in to the source code browser. You can add comments to any changeset, including annotations attached to individual lines of code.

# 28th August 2008, 4:25 pm / code-review, google-code

Review Board. VMWare release a slick looking Django-powered code review system, with hooks in to Subversion and Perforce.

# 31st May 2007, 8:32 am / code-review, django, perforce, subversion, vmware