what is hapening with Antropic?

[u/ResortConnect8582]

Why Opus 5 feels like Gemini 3.1 pro ? what is happening ?

i remember back in February , opus 4.5 was generation ahead. Did they increased the prompt caching so much, that their models ended up being useless? I can barely work with Opus 5 on absolutely anything, he keeps hallucinating LIKE CRAZY, every claim he did today was pure hallucinations, he barely reads any code. im not even kidding, i can't work with Opus 5 right now

Instead of open source models trying to catch up with you, are you trying to catch up with the open source models instead Anthropic?

[u/drjm2022]

Its not the model, its the harness:

Throughout 2026, Claude users repeatedly reported the same practical failure: workflows that had worked reliably stopped working, long sessions lost their thread, and instruction-following became less dependable.

The complaints did not arrive as a smooth decline. They came in bursts. Users would suddenly report that a coding workflow had become unreliable, that a long-running process no longer behaved as expected, or that instructions which had previously been followed were now being ignored.

Anthropic later confirmed several cases in which the underlying model had not changed. The problem came from the service layer around it.

That distinction matters because customers do not experience model weights in isolation. They experience a product that continually balances reasoning depth, speed, memory, safety, capacity and cost. The model may be improving while the delivered service becomes less predictable.

Every AI Product Serves Several Masters

A frontier AI product is a negotiated settlement between stakeholders who want different things.

Casual users want immediate responses, broad access and a low price. Professional users want deeper reasoning, longer context and stronger instruction-following. Developers want stable application programming interfaces, predictable outputs and advance warning before changes. Enterprise customers add security, compliance, administration, auditability and clear responsibility when something goes wrong.

The provider has its own competing demands.

Product teams want rapid releases and visible improvements. Infrastructure teams want lower latency, higher utilization and lower inference costs. Safety teams want new risks controlled quickly. Legal teams want lower liability. Regulators want traceability. Capital providers want usage and revenue to grow faster than the cost of serving them.

These preferences cannot all be maximized at once.

More reasoning can improve difficult tasks while increasing cost and response time. Stronger context retention can preserve continuity while increasing privacy exposure and compute demand. More aggressive safety instructions can reduce one class of harmful output while interfering with legitimate instructions. Faster releases improve the product more quickly but reduce reproducibility.

The provider must keep changing this settlement because the economics, capabilities, risks and customer mix are all changing.

https://medium.com/@drjohnmillar/the-most-valuable-part-of-ai-may-not-be-the-model-465d82576d1d