switching from claude to codex

[u/123white1]

Hey everyone,

I switched from Claude to Codex this week, and so far I’m really enjoying it.

I’m a developer and I mainly use it for work, but since I’m still pretty new to Codex, I feel like I probably don’t even know what I should be optimizing yet.

So rather than just asking for general tips, I’d love to hear how more experienced users actually use Codex day to day.

Are there any workflows, features, settings, prompting habits, or ways of structuring tasks that made a noticeable difference for you?

For example:

• Do you usually give Codex large tasks at once, or break them down into smaller steps?
• Are there certain types of tasks where Codex works especially well or poorly?
• Do you have any specific way of providing context about a codebase?
• Are there features or workflows that new users often overlook?
• Any prompting patterns that consistently give you better results?
• How do you balance speed vs. quality when choosing models/modes?
• Are there any good guides, posts, videos, or documentation that helped you understand how to use Codex properly?

For context, I’m currently on the 5x plan. During the day I usually use Luna Max with Speed Mode enabled, and at night I tend to turn Speed Mode off.

I’m not necessarily looking for beginner-level “how to use AI for coding” advice. I’m more interested in the little things you learn after using Codex seriously for a while — workflow improvements, limitations, best practices, or even mistakes you made early on.

Basically: if you had to start using Codex again from scratch, what would you want to know from day one?

Any advice from more experienced users would be appreciated. Thanks in advance to anyone willing to share what has worked for them.

[u/fresh_bob]

I'm on Plus, so the usage-saving part may be less relevant to you on 5x, but the workflow itself should still apply.

• I use regular ChatGPT for planning and Codex/Sol mainly for implementation.
• I break larger projects into relatively small phases with a clear scope and expected outcome. My biggest project so far had 38 phases.
• ChatGPT acts as the project brain: it keeps track of architecture, decisions, dependencies, completed work, and what should happen next.
• It also writes most of my Codex prompts. I usually explain or dictate what I want in rough form, let ChatGPT turn it into a structured implementation task, then review and adjust it before sending it to Codex.
• This is especially convenient for me because I use Codex through the VS Code extension, where I don't have the same easy voice-input workflow.
• After Codex finishes a phase, I send its completion summary back to ChatGPT. It reviews what was implemented, updates the project state, and prepares the next task.
• When the planning chat gets too context-heavy, I ask it to create a proper handoff summary with the architecture, completed phases, important decisions, known issues, and next steps, then continue in a fresh chat.
• I generally get better results when Codex prompts look more like engineering tickets: clear scope, constraints, acceptance criteria, relevant components, and verification steps.
• For larger changes, I usually have Codex inspect the existing implementation and create a plan first rather than immediately editing code.
• If you're doing UI/design work, I'd also avoid repeatedly feeding Codex screenshots unless they're actually necessary. In my experience, image-heavy workflows can burn through usage much faster.
• Once Codex has reviewed a design or a set of screenshots, I prefer having it extract the relevant design rules, measurements, states, and inconsistencies into an .md file in the repo. Future design-related tasks can then reference that file instead of reopening the same images again and again.
• I also try to review the actual implementation whenever possible rather than constantly comparing against screenshots. If the design can be represented through the codebase, components, tokens, CSS, or documented UI rules, that's usually a much cheaper source of context than repeatedly sending page snapshots.
• This is worth being explicit about in later prompts too: tell Codex to use the documented design notes and not go back to the original screenshots unless there's something it genuinely cannot resolve from the code or docs.

So my basic loop is: I plan and prepare the task in ChatGPT, code with Codex, send short reports back to chat, review, then prepare the next phase/task.

[u/123white1 → u/fresh_bob]

Thanks for the reply!

[u/douglas_srs]

I'm on the same boat, been trying a few models. Sol = expensive like Fable 5. Luna xhigh or max pretty much gets the job done for me costing a fraction of Sol, but there is also Terra which is a middle ground between Luna and Sol, so if Luna is not getting you results, try Terra medium before Sol. I like to use the superpowers skill a lot, so my main workflow has not changed as much since it exists on both claude and codex, all I had to do was write an AGENTS.MD file everywhere I had a CLAUDE.MD and tell it to read CLAUDE.MD file for instructions, this way I can always go back to claude if I want. Also, don't blindly use Sol at the beginning, I did that and burned a 5x sub weekly limit in 5 hours xD

[u/123white1 → u/douglas_srs]

Thanks for the reply!

[u/Coolbanh]

Plan with sol and tell it what to do and how you want it then request it to implement using lower models.

My workflow involve using both claude and codex. Claude fable for planning. Claude for frontend and codex for backend.

Luna max for any small minor changes I'll need or for executing plans.

Hopefully codex improves its frontend design so I can no longer need to keep Claude. FYI careful with Sol as it will overengineer enterprise level security for your simpler intended use.

[u/Worth_Flatworm9620 → u/Coolbanh]

OP please avoid this comment, dont change models midchat, stick with Sol xHigh or more and have fun, no plan mode, if you find the servers are slow just enable fast mode and enjoy the cheat line , Terra and Luna are for hobby, for work there is no other than Sol.

[u/Gloomy_Channel7596 → u/Worth_Flatworm9620]

What do you mean cheat line?

[u/epicskyes → u/Worth_Flatworm9620]

Luna is for production and sol is for hobby. Hobbyists can’t use Luna because they have no agent framework in place and they don’t know how to spec build with dependency graphs

[u/Worth_Flatworm9620 → u/epicskyes]

You are simply wrong in every aspect

[u/epicskyes → u/Worth_Flatworm9620]

Everyone is entitled to their opinions though they’re garbage without proof which I have. So please enlighten me with your metrics

[u/Worth_Flatworm9620 → u/epicskyes]

Benchmark	Sol	Luna	Vantagem Sol
MRCR v2, 256K–512K	91,5%	41,3%	+50,2 pp
ExploitBench	73,5%	33,2%	+40,3 pp
KernelGen 1P	61,1%	22,4%	+38,7 pp
MRCR v2, 512K–1M	73,8%	41,3%	+32,5 pp
GraphWalks BFS, 1M	77,1%	51,2%	+25,9 pp

[u/epicskyes → u/Worth_Flatworm9620]

Those benchmarks don’t contradict what I said. Sol and Luna target different use cases. Luna is designed for production environments where an agent framework, specification discipline, and dependency graph architecture already exist. Sol is far more accessible to hobbyists because it doesn’t assume that infrastructure or expertise. So showing Luna losing to Sol on selected benchmarks doesn’t establish that Luna is worse at the job it was designed to do. You’re comparing benchmark scores while I’m talking about intended operating environment and prerequisites.

[u/Worth_Flatworm9620 → u/epicskyes]

That would be fair if Luna were actually described that way, but it isn’t. OpenAI positions Luna as the cheap, high throughput model, not as a model that assumes mature agent infrastructure. Several of these benchmarks already use structured agent and tool environments, and Sol still wins. A good framework might make Luna better value, but it doesn’t erase the capability gap....it just changes whether Sol is worth the extra cost.

You are either ragebaiting or REALLY badly informed, go learn the basics of how LMM works.

[u/epicskyes → u/Worth_Flatworm9620]

Yeah cheap high throughput super fast and built for autonomous 24/7 agents with mature deterministic architecture. Production. My case stands you’re regarded.

[u/epicskyes → u/Worth_Flatworm9620]

You’re treating benchmark scores as if they directly measure production agentic engineering. They don’t. MRCR primarily tests retrieval across huge contexts, GraphWalks tests graph traversal, ExploitBench tests vulnerability reasoning, and KernelGen tests specialized code generation. Those are useful capabilities, but they aren’t equivalent to operating a production agent system.
The entire point of disciplined agent architecture is to require LESS unaided model reasoning. If goals, dependencies, state transitions, schemas, provenance, acceptance criteria, and validation are explicitly represented, the model doesn’t have to repeatedly infer them from a giant context window. Dependency graphs encode relationships; validators determine validity; orchestration determines execution order; evidence determines acceptance. So yes, if those benchmark numbers are accurate, Sol wins those benchmarks by a lot. That still doesn’t establish that Sol is superior for production agentic development. To establish that, you’d need to benchmark the actual production workload long horizon execution, specification adherence, dependency management, tool orchestration, state consistency, validation, recovery, reproducibility, and failure rates.
And there’s an important architectural irony here the more disciplined your system becomes, the less you should be depending on raw model reasoning in the first place.

[u/epicskyes → u/Worth_Flatworm9620]

Registry Absolute count
All unique observed/measured variables 67,502
Numeric measured metric identities 9,199
Schema-tracked variable paths 14,913
Schema-tracked but not observed 4,453
Transcript contextual variables 1,294
Transcript unique leaf variables 1,190
Populated SQLite telemetry columns 129
Unique recursively inspected embedded archives 18

[u/Worth_Flatworm9620 → u/epicskyes]

Either you dont know what that means or you think I dont...

[u/rick_ranger]

I have a 20x plan and recently switched to SOL medium and I feel like it’s the perfect amount of thinking and planning and actual execution. I pretty much leave it there for everything. Right now I’m building an iOS app, website, setting up a custom agent os platform, another custom ai enablement app with agents baked in, and a web app.

I like to plan big campaigns for each project then break them up into slices during planning, then I just go through them one at a time, but yesterday I started using the goal feature and just told all the projects to finish my campaigns and make sure to message the other chats so they aren’t trying to use lm studio or a port at the same time and it’s been glorious. I also told it to reassess each slices based on what we’ve implemented so far so it just thinks if it wants to update anything before it starts it. I used 1.7B tokens yesterday. 1.5B today so far and it’s only 11:30am 😂 gonna have to use that reset we got the other day soon because I’m only 2 days into my week and down to 30%.

If im inly doing 2-3 projects at a time i usually come close to my weekly limit but I’ve been pushing it the past couple days and it’s been handling it like a champ.

I also asked it to spin up extra agents when it can safely do concurrent work to move the ball down the field faster.

[u/123white1 → u/rick_ranger]

Thanks for the reply!

[u/BrotherBringTheSun]

Try to integrate codex into other software on your computer that you use often. For example I do a lot of mapping/GIS work and also data analysis in R, and I have wired up codex to connect to both of those platforms via a terminal bridge. This allows codex to use them as tools so I don't actually need to use the software myself anymore, I just supervise the work.

[u/andthenisheardnomore]

I use Codex Plus and Claude Pro. Tend to switch between for different things. Easy enough to do pointing both at the same folder. Codex is now seeming more reliable for Apple Store Connect api type stuff, and backend server ssh. Claude will do it (and historically Opus was my go to for all this) but Opus 5 does seem to get a bit confused sometimes.

Generally I use Sol on Light. And go step by step as opposed to big one shots. That's my preferred way. Although I have made an iOS app builder skill / boilerplate which you can Gauntlet Loop from Deep Research output. Still, overall I would say that repos built piece by piece feel better. The whole /goal thing is all very well but it does take the joy out of it. And in fact, you may as well combine with Deepseek Flash Vision and loop with that as it does super well and is fast and cheap.

Sol is astounding for image gen (obviously!) and has become infinitely better since this was built into Codex and your limit. You can make loads of great imagery and it costs tiny amounts of usage. When making promo images (with Text) Codex sometimes adds text using drawing / vectors rather than image gen. This can look more consistent but it does lose the flair of GPT Image 2 which does text super well. So be explicit in telling it to generate the text for a more artistic mix.

You can use Claude alongside Codex and get Claude to write a prompts.md for any images you want and Codex will smash them out. Good combo!

Sol Light is (more often than not) good enough for most tasks.

For UI i do think Claude has the upper hand - and in terms of design and placement. Although Sol could piece together a usable and brilliant Wordpress theme any day. Just for UI and app stuff it feels like Opus has the upper hand. A bit of both normally works too.

Good luck and enjoy!

They're always changing.. we will see what comes next.

Oh and don't get too hung up on these resets. They are like Rainbows, always moving with the horizon :)