GPT‑5.4 - OpenAI's most efficient model: less tokens, more clarity
by•
GPT-5.4 Thinking delivers deeper web research, stronger context retention on long tasks, and 33% fewer factual errors than its predecessor. You can now interrupt the model mid-response and redirect it. No need to start over. Same intelligence. More control. Less token burn by default.
Replies
Excited to hunt GPT-5.4 today!
This is OpenAI's most capable reasoning model yet and it's not just an incremental bump. GPT-5.4 merges the coding power of GPT-5.3-Codex with serious knowledge work and native computer-use capabilities into one model. Less back and forth, more actual output.
What stands out:
-Native computer use: the model can operate a desktop, click, type, navigate apps
-Matches or beats industry professionals on 83% of real-world knowledge tasks (GDPval)
-33% fewer factual errors compared to GPT-5.2
-Tool search cuts token usage by 47% in large tool ecosystems
-1M context window support in Codex
-Significantly better at spreadsheets, presentations, and documents
It's not trying to wow you with a feature list. It's trying to actually finish the work you give it. Faster, with fewer mistakes, and with less hand-holding.
The computer use benchmark result alone (75% on OSWorld-Verified, surpassing human performance at 72.4%) is the kind of number that makes you stop and think.
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Told
@byalexai The mid-response interrupt is the feature I didn't know I needed until I spent way too many tokens watching a model confidently go down the wrong path before I could stop it. That alone changes how I use this in workflows where context shifts mid-task. The 33% fewer factual errors claim is bold — curious how that holds up on domain-specific prompts versus general knowledge, because that gap tends to widen fast in niche areas. The efficiency angle is smart positioning too; token cost is a real friction point for anyone building on top of these APIs at scale.
Impressive numbers! Though benchmarking against your own previous models is a bit like winning a race you organized, against yourself. Would love to see how it stacks up against the rest of the field. Either way, excited to try it in Codex!
The mid-response interruption feature is honestly what I've been waiting for. So many times I realize halfway through a response that I asked the wrong thing and just have to sit there watching tokens burn. 33% fewer factual errors is a big claim too, curious how that holds up on more niche technical domains.
git-lrc
Two models in the span of 24-48 hours, crazy!
Reddit
Told
The mid-response interrupt is the feature I didn't know I needed until I spent way too many tokens watching a model confidently go down the wrong path before I could stop it. That alone changes how I use this in workflows where context shifts mid-task. The 33% fewer factual errors claim is bold — curious how that holds up on domain-specific prompts versus general knowledge, because that gap tends to widen fast in niche areas. The efficiency angle is smart positioning too; token cost is a real friction point for anyone building on top of these APIs at scale.
The 47% token reduction from tool search is the number that actually matters for anyone building agents. Every token you save on tool calls is compute you can spend on reasoning. In agentic workflows where the model is making dozens of tool calls per task, that compounds fast.
The computer use benchmark surpassing human performance is interesting but the real question for production is consistency, not peak performance. Can it operate a desktop reliably across 1000 consecutive runs without random failures? Benchmark numbers on curated tasks are great. Agents in the wild face a totally different distribution of inputs.
The "less hand-holding" framing is the right one though. Every time a human has to review or correct an AI output, you're paying for that human. The models that reduce human supervision without reducing quality are the ones that actually unlock the economics of AI labor.