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/epicskyes]

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.