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I gave both models the same complex task inside Cowork. Nothing loose. A clear prompt, full context, the constraints spelled out. The kind of brief that leaves little room for confusion.
Fable 5 read it and came back with questions.
Not once. This happened several times across different jobs. The task was already defined. The steps were there. But instead of working through the problem, it paused and asked me to confirm things I had already told it. Then it did part of the work. Then it stopped again before the issue was fully solved.
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Where Fable 5 stalled
The pattern was the same each time. It handled the easy parts and hesitated on the hard ones. When a task had a few moving pieces, it broke the flow to check with me instead of pushing through. So a job that should have been one clean pass turned into a back and forth. And the final result still left gaps I had to close myself.
That is the part that cost me time. Not the questions alone. The unfinished work behind the questions.
What GPT 5.5 did with the same brief
I gave GPT 5.5 the exact same prompt and the same instructions. No extra hand holding. It read the task and started working. It made reasonable calls on the small unknowns instead of stopping to ask. It carried the job to the end and solved the issue in one go.
Same input. One finished the work. The other kept checking in.
Why this matters for real work
Most serious tasks are not one step. They are five steps with small decisions inside each one. A model that stops at every fork is not saving you from mistakes. It is handing the work back to you in pieces. When you are moving fast across a stack of tasks, that friction adds up quickly.
What you want from an agent is judgment. Make the small calls. Flag only the ones that truly need me. Finish the job. GPT 5.5 did that for me. Fable 5, in Cowork, did not.
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What each model actually is
GPT 5.5 came out on April 23, 2026. OpenAI built it for exactly this kind of work. You hand it a messy multi part task and trust it to plan, use tools, check its work, deal with ambiguity, and keep going until the task is finished. The focus was agentic coding, computer use, and knowledge work.
Fable 5 is the safety tuned version of Anthropic's Mythos 5. It shares the same base model, but Anthropic added extra guardrails around biology, cybersecurity, and AI research work. Some queries on those topics get quietly routed to Opus 4.8 instead. That caution is built in on purpose.
Speed and cost
GPT 5.5 holds the same per token latency as GPT 5.4 while running at a higher level. It also uses fewer tokens to finish the same coding tasks. So it is faster to a finished result, not just faster per word. Fable 5 leans careful, and careful costs time when a task has many steps.
How they handle a hard task
This is where my experience lines up with the design. GPT 5.5 is built to make the small calls itself and only stop when it truly needs you. Fable 5, with its heavier safety tuning, pauses more. On sensitive work that caution is the point. On a normal complex job with clear instructions, it turns into extra questions and half finished passes.
Where each one fits
Reach for GPT 5.5 when the task is heavy, the brief is already clear, and you want it done in one run. That covers most of what I do in Cowork every week. Fable 5 still earns a place on work where you want the model to slow down and flag risk, especially around security or biology topics. Different tools for different jobs.
One fair note
This is my experience, not a lab test. The extra questions may come from Fable 5 being tuned to play it safe. On simpler tasks the gap may close. But for the complex jobs I actually run every week, the difference was real and it repeated.
For my daily Cowork load, the faster finisher wins.
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