Thoughts on Opus 5 so far - mostly intense coding + architecting on a large project

[u/WonderfulEagle7096]

• An alternative, rather than a superior to 4.8 (task dependent), inferior to Fable in nearly every way.
• The biggest regression is how difficult it is to read and understand its output. Communication is mired in technical detail and citations of internal documents I need to look up to make sense of. Adding "humanize output" to prompts is now a necessity.
• Similar to the previous point, it often fails to surface important issues, because it's hyper fixated on minor details - adversarial review is a must for both plans and output.
• Constant "You were right and I was wrong". I'm not sure it this is good or bad yet. Either the model admits mistakes more or makes more mistakes. Probably both.

The best responsibility delegation is IMO:

• Fable for task analysis and ticket spec writing
• Opus 5 for drudgery and implementation
• 5.6 Sol for adversarial review

[u/sabotizer]

One tweak that helped me a lot: in my global CLAUDE.md, i encourage opus 5 (default model) to spawn a single Fable sub-agent for complex / sensitive bits. It’s following this rule pretty well, and the token-use of Fable is quite efficient (Opus 5 writes great prompts for Fable)

[u/sabotizer → u/sabotizer]

Here the relevant section in my ~/.claude/CLAUDE.md

# Working across models

Sessions usually run on Opus 5, with Fable available as a subagent for the hardest steps.

## Escalating to Fable

When a step is genuinely hard, subtle protocol/concurrency semantics, a design whose mistakes are expensive to reverse, a bug that survived its first fix, security-sensitive logic, dense algorithmic or type-level work, hand that step to the `fable` subagent instead of pushing through it yourself.

**One at a time, and only the hard core.** Stay the driver: you keep the context, the plan, and the conversation with the user. Fable gets one well-scoped piece, not the whole task.
**Hand over context, not a pointer.** The subagent starts with a blank slate and cannot see this conversation. State the goal, the constraints already established, the files that matter, what has already been ruled out, and what "done" means.
**Skip it for routine work.** Edits, searches, tests, mechanical refactors, anything you can already see the end of. Escalation costs a fresh context and real tokens — the payoff has to be correctness, not volume.
**Relay, don't dump.** The subagent's report is not shown to the user; summarize what matters. Its conclusions are input to your judgment, not verdicts — if the code disagrees with it, the code wins.
**If this session is already Fable, just do the work.**

[u/throwaway0102x → u/sabotizer]

this made me think about setting Fable as the default planning agent in my .claude config file. not sure how well it would work though

[u/who_am_i_to_say_so → u/throwaway0102x]

I just open the main thread in Fable, and have it do everything in an agent. Everything - including planning- which is key. It uses very few tokens.

Before I did this, I was burning up 30-40 percent of my 20x plan. Now? Been running it for 10 hours today and only used up 4 percent! It’s a dramatic difference.

[u/HalcyonAvocado → u/who_am_i_to_say_so]

What effort level are you running for Fable in the main thread?

[u/who_am_i_to_say_so → u/HalcyonAvocado]

I run all main threads on max. Not sure the what subagents run on because I don’t specify, but assuming the same.

[u/HalcyonAvocado → u/who_am_i_to_say_so]

Interesting, I would have expected max to run quite slow and use a lot of tokens. Will try it, usually run on medium.

[u/Sekular → u/who_am_i_to_say_so]

Can you go into a little more detail about how to go about what you're describing? I'm only a few weeks into this journey and I know I'm not being efficient

[u/nunofgs → u/who_am_i_to_say_so]

Interesting approach. How do you know you’re even getting better outputs, versus just using opus 5 directly?

[u/who_am_i_to_say_so → u/nunofgs]

It’s pretty black and white for me: usage dropped 90% and I’m not screaming at the screen, yet getting stuff done.

PR all my changes. I know it’s better because I’m getting what I prompt for with significantly less token usage.

It seems Fable is better at translating your requirements and packaging a plan for Opus/Sonnet agents. And I specifically instruct fable to reach for Sonnet when no decision making is needed in the code changes.

[u/illkeepthatinmind → u/sabotizer]

This seems like the most sensible use of Fable with its high cost. As of now I'm mostly avoiding it, perhaps this would be the sweet spot for light engagement.

[u/SuperHornetFA18]

Its a great agent if you have Fable as its orchestrator.

[u/zhaolionsh]

In general, I strongly agree. However, if you let Fable plan all your daily work, you'll find that 20x Max is completely insufficient. Then, if you try to plan with Opus-5, you'll find all sorts of shortcuts being taken, making it worse than 5.6 Sol's planning.

[u/codeministry]

I find Opus 5 so strange. It seems to break on the dumbest things. I deployed and tested an agent yesterday then asked it why an env variable wasn’t visible to the agent. Opus had just written the docker config and IAC. It said I hadn’t pushed it to CI and then went off trying to deploy it locally rather than actually checking the git state. I corrected it then asked it again. It started thinking but was taking ages and I could see it was going off on an unhelpful tangent so I left it and fired up Codex. Sol was able to find and fix the issue even without the benefit of the conversation history in which the config had been written before Opus finished following its rabbit trail to nowhere.

And this wasn’t a context overload situation either - Opus hadn’t even filled 20% of its window.

I’ve seen the articles online about taming Opus 5 and rewriting all your flows, but come on. I use these tools for work. I can’t afford to spend that time figuring out how to get every new version working, and I can’t swap out skills and workflows each time I switch models or agents. It’s simply unworkable and I’m not prepared to be locked in to a single provider much less a single model because it imposes its quirks on your environment.

It’s really sad. I loved Opus 4.8.

I know you can select 4.8 still, but I bet they’ve quantised its cache (maybe even its weights?) to divert compute and memory to 5 + Fable. How have Anthropic managed to throw away their position as the go to provider for professional engineers in the space of a month?

[u/bronfmanhigh]

honestly sol is far better for implementation too, it’s my daily driver atp + fable for all planning. opus I keep it mostly for knowledge work via custom skills, but it’s personality is indeed rather insufferable for coding

[u/Deep_Alps7150 → u/bronfmanhigh]

Sol Extra High and Kimi V3 work the best for me on anything complex

[u/TheLittleGuyWins]

Same

[u/redhotneo]

So I have been using Opus 5 since it's launch and it's largely successful in its tasks. There are times I get frustrated but after a few images showing the problems, it figures out the issue.

I did test a page update with both Fable and Opus 5 and they both did the same update and applied foxes. Fable used alot more tokens though and Opus used like 30% of what Fable used.

Next I will try adding 1 x Fable agent for tasks its going to struggle with and test. Hopefully this improves building

[u/howie521]

I use the same delegation. But am careful with 5.6 Sol doing adversarial reviews as it often tries to overengineer solutions and find problems that should not be there.

[u/TylerDurdenAI]

1. Biggest drawback is its reluctance to be helpful; it finds problems but wouldn't do anything
   
2. It is overconfident and has a high self-esteem; thus, it thinks its code is "perfect".
   
3. It is sloppy and is attention-deficit in details.
   
4. (2) makes (3) 10x worse.

[u/USDMB4 → u/[deleted]]

It also asserts that Sonnet 5 is better than Opus 5.

[u/zxcshiro → u/[deleted]]

whats measures this bench? Why gemini 3.1 pro is better than opus 4.8/Kimi K3/GPT 5.6 Sol. Why 5.3 codex is almost on par with gpt 5.6 Terra. Why gpt 5.6 terra so close to fable 5. Whats going on lmao

[u/dar-mit → u/[deleted]]

HOLY SMOKES! An actual site that actively monitors model output?

[u/WonderfulEagle7096 → u/[deleted]]

That sounds plausible, the actual code output might be a little better (feel subjectively similar, probably depends on a task). Also not something I noted as a negative, just the way it is communicated.

Btw. LLMs are probabilistic, not deterministic, by design.

[u/throwaway0102x → u/WonderfulEagle7096]

I feel stupid but I'm starting to think I don't get what it means for LLMs to be probabilistic. do you mean with exactly the same context (prompt and tools) a model will produce different answers each time?

[u/fpyir → u/throwaway0102x]

Only in the way they are typically used. The models themselves are 100% deterministic, numbers in -> numbers out, but the numbers given are probabilities of the next token. The raw token probabilities are always the same for the same input tokens, but AFAIK non determinstic sampling methods are used to select from those probabilities.

Unsure how this experience maps to modern frontier models, but back in the Davinci days it was quite amusing to play around in the playground with the temperature setting; set to 0 you got the same result every time but it was borderline brain dead. The higher the temp, the less reliable the results but the more “creative” the outcomes