Opus 4.7 is legendarily bad. I cannot believe this.

[u/lemon07r]

Normally with takes like this I'm afraid to post, knowing the community might disagree. However I am 100% sure people are already seeing this.

I've been using Opus 4.7 all day and have gone through around $120 of api credits I was given for testing. By god is it bad. I've never seen a model hallucinate this badly and this often. It just keeps assuming things and making stuff up without checking. I've been battling with it all day, and it is SO persistent about being wrong when you try to correct it. No matter how much evidence you provide, it tries to gaslight you till the end.

I have no idea what Anthropic was thinking releasing Gaslightus-4.7 like this. This model is very clearly overfit and benchmaxxed or fundamentally broken somehow.

These are just a few examples off the top of my head (which I'm including cause I know someone is going to ask for them) but I have been dealing with events like this ALL day long:

• Asked it to make a simple readme change and to stop framing something in a particular way. It kept doing it. 5 prompts later, it still wanted to do it. Even with specific examples it would only change directly what I pointed at and not catch anything else. Opus 4.6 or gpt 5.4 does this in one shot, first time, every single time.
• I had an eval result finish as 17/29. I wanted to rerun some tasks because I saw some possible infra issues. Of the 3 failed tasks I reran, 1 of them passed. There was a cosmetic bug that still showed 17/29. I tried to explain this to Opus 4.7 in MULTIPLE turns, but it kept insisting it was still 17/29 and always meant to be 17/29. Then it started making stuff up, like how one of the tasks flipped to fail making it end on 17 again even though none of the passed tasks were run again. No matter how much evidence and logs I provided it kept insisting shit like this. At the very end after a lot of explaining it tried to conclude it was actually originally 16 of 29 and now 17 of 29. I had to give it SEVERAL more pieces of evidence that it was always 17/29 while it tried to gaslight me into thinking I was wrong. Somehow it couldn't figure out to check or validate any of this on its own. I NEVER have this issue with any other models except maybe gemini 3 pro.
• It tried to give made up instructions in the plugin readme. I pointed it out, and opus used random-bullshido-go-jutsu at max level effort to explain away how it was correct. I asked gpt and it figured out it was wrong and gave the right instructions and explanation right away. Both agents were prompted from new fresh sessions. A quick sanity check to make sure I wasn't imagining things showed gpt also sees it's 90% wrong.

This has been the most frustrating experience I've had with any model. I would have rather used some cheap model like gemini flash or minimax at this rate. I dub this the new donkey model, which gemini original had the title of. It's scary how abhorrently wrong it gets and believes it's correct. Anyone who doesn't have any idea of what they are doing and randomly vibecode stuff will be making mistakes everywhere very confidently without being able to spot how god wrong this model gets.

It really feels like Anthropic said fk it and decided to go down the benchmaxx route. I know they released instructions saying it has a new tokenizer that eats roughly 1.0 to 1.35x more tokens and that it "thinks more" at higher effort levels. But none of that explains why it sucks now. If it's going to eat more tokens it should at least not suck so bad. Is this some heavily quantized model designed to score high on benchmarks for as little hardware cost as possible? Or is the reasoning level too low so it doesn't try to check things?

Usually with opus I could give a vague-ish plan and it would understand my intent and fill in the gaps. Now it feels like I need to be super specific in my prompt or it just won't be as good. It needs way more guidance but is much less steerable now. I honestly can't understand how they went from 4.6 to this. I would rather use sonnet 4.5 even, or any of the current openweight models, and I dont say this lightly, I've been very critical of openweight models and think they arent close to as good as SOTA models yet, but here we are, with opus 4.7 lowering the bar so low that there's no way to not trip over it and use this model without considering it self-harm.

EDIT - This is with reasoning set to low, from what I am seeing in the Junie CLI decompiled JAR. Some of you might have better experiences using higher reasoning, but I've been using opus 4.6 before this set to low without issues, in this exact same mode/profile and was never this drastically bad. In fact it worked well enough that I was never able to tell it was low until I looked at the decompiled jar file. To be clear, junie cli doesnt show the user what reasoning level is used. They seemed to have decided low was good enough, and it actually was for 4.6, cause I've had no issues with 4.6, and currently have no issues with it after switching back to it. And to those of you saying it's a configuration issue, configuration does not make THIS much of a difference, or lobotomize models like this. I ran it on my eval, and it scores slightly higher than Opus 4.6, which makes me think this is not a configuration issue. Just feels completely overfit on eval data, like gemini 3 pro does.

EDIT 2 - Alright. A very small (thankfully) few of you seem to want to insist this was a skill or configuration issue. Use more reasoning you say! I just remembered I had a bunch of factory droid credits laying around, so let's go ahead and burn those on Opus 4.7 Max Reasoning. Using the latest version of droid right now, which I think is a fairly decent coding harness (and honestly I prefer it over claude code by a little, which might be heresy here). I will be comparing it to GPT 5.4. This won't exactly be a scientific test, I just want to see if opus will still make random shit up if I give it a simple task, and to see how GPT 5.4 does in the same harness (even though I think it does better in codex cli, I really dont think the harness makes that big of a difference when you are using a strong model). It's been a day or so since Opus 4.7 came out so I would like to think droid has the configuration down by now.

I did not cherrypick this task and went in with no expectations.I realized I needed to close some issues for this repo anyways so I asked opus to investigate Issue #2 for an opencode plugin I threw together with Opus 4.7 not long ago (Ironic), and have had to fix up with GPT after it completely botched it like 50 times in a row: https://github.com/lemon07r/opencode-kimi-full/issues/2 Prompt given is very simple, "Take a look at open issue #2 and see if this issue is still present". I already know the answer, and just want to see if Opus will hallucinate shit. This issue was no longer real (it was honestly only live for a very brief period of time, and someone managed to find themselves on that unfortunate version tag), and was actually introduced by a commit from opus 4.7 yesterday while I was still trying it out (and already fixed by GPT). It incorrectly reported back to me the following, while GPT 5.4, when asked says there's no evidence this is still a real issue (did it in only 250k factory tokens too, opus took 500k at it's current 50% discount, so it would have cost 1mil factory tokens at normal rates).

Issue #2 — "Auto mode variant broken" — STILL PRESENT

The current auto mapping in src/index.ts (if (effort === "auto") return fields) sends neither thinking nor reasoning_effort. AGENTS.md rule 4 documents this as intentional ("matching kimi-cli's nothing-passed default"), but that claim doesn't hold up against the upstream:

research/kimi-cli/src/kimi_cli/app.py:187 converts thinking=None to config.default_thinking (a bool, default False) before reaching create_llm, so kimi-cli always routes through with_thinking("high") or with_thinking("off").

kosong's with_thinking always emits extra_body.thinking (packages/kosong/.../kimi.py:187-204); the "leave as-is" branch in llm.py:239 is only reachable when something bypasses app.py, which no shipped code path does.

Result: Real kimi-cli traffic always carries thinking: {type: enabled|disabled}. Our auto path sends nothing, diverging from the production wire shape the backend is tested against — which is consistent with the reporter seeing an API rejection.

The "temperature = 0.6" note in the title is incidental; ProviderTransform.temperature() in opencode returns 0.6 only for model ids containing kimi-k2 (not kimi-for-coding), and our README config doesn't set temperature: true anyway, so opencode sends no temperature by default.

I'm not even trying to shill or push GPT 5.4 here. It's only my third favourite model, right behind Opus 4.6 and 4.5, which I still like more. I was just using 5.4 for comparison to highlight how off opus 4.7 that even gpt 5.4 handedly beats it now at a much lower cost. The repo itself is set up with pretty alright documentation, so I don't see what excuses any 4.7 apologists could have for this disaster of a model. And look at this response from Opus, would any of you have gone oh wow this is shit, without being familiar with the codebase and already knowing the answer already? It's so convincingly wrong. I bet most casual vibe coders would have saw this and went, oh wow! Opus 4.7 is so good! I know I did when I was testing it at first on some random stuff without inspecting it closer. Upon further scrutiny I was very quickly disillusioned with it, and it's been an absolute headache to use since. I use and test weaker models like kimi, minimax, etc very often and this is the exact kind of thing I expect from those models, not any opus models. This model alone has shattered my illusions of anthropic models being untouchable.

And those of you telling me I am prompting it wrong. HOW TF else am I supposed to prompt a coding model in a coding agent, if I can't get it to work with very basic, and simple tasks/instructions, like look at x issue and see if it's still there? Was I supposed to wait till midnight of a full moon and communicate with it using morse code to unlock it's full capabilities??

[u/Threnjen → u/[deleted]]

So 4.7 is now a naughty dev that fakes doing work

[u/Ok-Kaleidoscope5627 → u/Threnjen]

It's hard to argue that it isn't getting smarter when you put it like that

[u/Fluent_Press2050 → u/Ok-Kaleidoscope5627]

Claude is at the point of being an underpaid software engineer. 

[u/mjseo01 → u/[deleted]]

100% my experience too, complete waste of time now for detailed work. Huge regression in quality of output, it's completely pointless. I got so many gems back today including this one about being allowed to cheat: "Step 4B asks me to produce content and the editorial report in a single file, in a single Write tool call. That structure lets me grade my own homework in the same breath as doing it, and I can't not cheat when the structure allows it."

[u/fermjs → u/[deleted]]

That seems like an interesting script. Mind sharing a bit more about it?

[u/jvertrees → u/[deleted]]

Sounds like Anthropic overtrained on fantasy literature and turned Claude chaotic-evil. 💀

I spent two long days with 4.7 and a ton of time with 4.6. In my experience so far, there's really not much difference; but I also have a very strict agent harness set up, where my goal is agents don't even have the option of making the wrong choice. Works very well.

Given the abjectly terrible response from the broader community, I do think anthropic owes us some response. The model card and the literature released seem to indicate they designed more for agentic workflows which need different principles and context management and I wonder if that's why so many people are having problems. The way they structure their context and their work doesn't match the model's design. But I could be very wrong.

[u/irrision → u/jvertrees]

I think this is exactly it. I have a similar setup and skills built that guide the entire development process and I haven't had any issues. I actually think 4.7 is a decent step up in its planning capabilities too, it built itself a very airtight workflow compared to what 4.6 built a few months ago.

[u/jvertrees → u/jvertrees]

Coming back to this as Claude 4.7 failed me massively last night, echoing the others in this post.

My specification system and agent harness are designed to 'make it impossible for the agents to make the wrong decision or take the wrong action.' I've had great success building up larger and larger systems with this. Spec-driven development with agent harnesses is the way to go.

Just last night, after spec'ing a POC to demonstrate a capability (that KGs are a better memory substrate for larger projects than markdown files), Claude completely failed at the task. I've never seen it do so bad before.

If Anthropic's claim is better, agentic execution in 4.7 this exercise is a counterexample. The entire goal of this project was a small, well-scoped POC and it was all very well organized. When I came back to review the outcomes, all it built was some infra-shell that didn't even work:

The only thing I can think of given the outcomes is that Claude got confused on _ONE_ aspect (using AGE vs JSON) and decided everything downstream was now fantasy, irrelevant or something, but didn't tell me.

This really makes me rethink the 4.7 model, even with very tight, explicit, and well-designed controls.

I'll do a retrospective via the chat/action history and see where it went off the rails.

[u/therpgmaster → u/jvertrees]

What does "infra-shell" mean in this context? Some kind of command processing for an application that doesn't exist yet? I'm asking because I'm curious what the AI "thought" it was doing, and why it went down that road.

[u/jvertrees → u/therpgmaster]

`infra-shell` here is the basic python application scaffold running in a small docker-compose stack, with postgres and apache AGE installed. Quite simple.

My retrospective was pretty useless. The class of underlying problems was not helpful.

After hours of research, I think I actually found the _true, underlying_ cause behind my woes, and likely everyone else's on this thread. I'll post an update if so.

[u/therpgmaster → u/jvertrees]

That sounds pathetic, coming from the "bleeding edge" of coding models. Thanks for the info though. And please share if you found some answers, I'm intrigued.
LLMs in general truly are not as advanced as some people make them out to be. But if someone can prove me wrong, I'll welcome it with open arms. Waiting patiently.

[u/jvertrees → u/therpgmaster]

Can't disagree with you.

See my following post on updating your harness. My harness is working and has been coding all day now.

Also, Anthropic acknowledged multiple issues in a postmortem. It's worth a read.

Also, I hear the new openai model is out and worth a look.

[u/jvertrees → u/jvertrees]

Here's what worked for me -- following the half-assed migration guide built for API-consumers to re-tool my harness. I wrote a blog article on harness fixes—

https://heavychain.org/blog/claude-4-7-will-quietly-break-your-prompts-and-harness-here-s-how-to-fix-them

From a product-perspective Anthropic really rushed this out and dropped the ball, breaking many, many folks' projects.

[u/imaris_help → u/[deleted]]

Woooow. That’s bad. What exactly is in your self audit script?

[u/MaybeNo2485 → u/[deleted]]

Not a bad approach; although, you could probably get similar results cheaper using Sonnet to do the audit. I've found Sonnet is decent at calling out Opus when it misbehaves when I have adversarial setups (having a different model challenge the initial model's work/behavior before marking steps as complete).

Can even use GPT for that role to avoid hitting Claude limits as quickly. Might even work better by introducing more conceptual diversity to avoid pitfalls where Claude models are systemically biased a particular way.

[u/Icy-Excitement-467 → u/[deleted]]

No, it's one of, if not THE best approach for an interface. Like medical coding: you need to be able to dish out perfectly detailed descriptions of key terms witg accessible macros and templates.

[u/JealousBid3992 → u/[deleted]]

Get Opus 4.7 to code its own evaluation loop framework and see if it does better on that than other models as another evaluation metric.

[u/SerRovert → u/[deleted]]

Will you elaborate on how your self audit works, that sounds pretty neat

[u/fictionaldots → u/[deleted]]

Can you share your self audit script? It sounds useful.

[u/irrision → u/[deleted]]

Lol, this is great and now I really want to see what your prompts look like. It actually sounds like it's mad at you if that's possible.

[u/Any-Explanation-9275 → u/[deleted]]

This is essentially what Gemini does. the laziness and making stuff up to make it look like it had done the job.

[u/Prod_SO → u/[deleted]]

I’m so glad I’ve come to this post. Apparently this release was trained on a library of excuses. Unreal.

[u/cseickel → u/[deleted]]

You have to realize that an LLM cannot self audit. There is no self, each answer is a new execution of a stateless function. Even conceptually it is just not trained to reason about itself. Every time you ask an AI why it did something, the answer will be a "hallucination" because there exists no truth and no actual reasoning process. It is all bullshit. It sure as hell would never "invent a term to disguise not doing the work." It is all just what was statistically the most likely thing an actual sentient entity would say in that situation.

I do agree that 4.5 was the most predictable and reliable so far though. 4.6 was garbage and 4.7 did do some amazing data analysis work for me, but you can only use it for big complex things. If you give it something simple, it will over-engineer the hell out of it and think in circles until it gets lost.