OpenAI just flipped the switch on GPT-5—and the headline isn’t just “smarter.” The story is that GPT-5 manages its own brainpower. A fast default model, a deeper thinking model, and a real-time router decide when to sprint and when to grind through complex reasoning. Translation: fewer clicks to “switch models,” more focus on outcomes. OpenAI

For ChatGPT, GPT-5 is the default for everyone (with higher usage for Plus, and GPT-5 pro—the extended reasoning tier—on Pro). For businesses, Team has it today, with Enterprise/Edu rolling out next. OpenAI+1

What’s actually new

One unified system. GPT-5 pairs a fast main model with a deeper GPT-5 thinking model and a router that automatically picks the right path based on task complexity, tools, and intent (“think hard” now means something). If you hit limits, it falls back to a mini tier. OpenAI says the goal is to merge this into a single model over time. OpenAI

Less fluff, more truth. OpenAI’s system card shows materially lower hallucinations and reduced sycophancy, plus strong upgrades in writing, coding, and health. In health specifically, GPT-5 cuts high-stakes error modes by large margins versus prior models. OpenAI CDN+2OpenAI CDN+2

Helpful by design, not just safe by refusal. A new safety training approach—safe-completions—optimizes for safe outputs rather than binary “allow/deny” judgments. That means partial, policy-compliant answers on dual-use topics instead of hard refusals that halt your workflow. OpenAI CDN

For builders: knobs that matter

In the API, GPT-5 ships in three sizes—gpt-5, gpt-5-mini, gpt-5-nano—and introduces precise controls: a reasoning_effort setting (including minimal for speed/cost) and a verbosity parameter to shape answer length. There’s also custom tools (plaintext + grammar-constrained) and more stable parallel tool calls. If you want the non-reasoning chat model, it’s exposed as gpt-5-chat-latest. OpenAI

Context for days. GPT-5 supports a 400k token ceiling (≈272k input + 128k reasoning/output), with pricing clearly listed per million tokens. Public pages show $1.25 input / $10 output for gpt-5, with lower tiers for mini and nano. OpenAI+1

Callout: You don’t always need max reasoning. Use minimal reasoning or the chat-latest path for routine tasks; dial up thinking only when the task justifies the latency and spend. OpenAI

Does it actually perform?

On real-world coding and agentic tasks, GPT-5 posts notable gains:

  • SWE-bench Verified: 74.9% (up from o3’s 69.1%), plus fewer tokens and tool calls at high effort. OpenAI
  • τ²-bench (telecom tool-use): ~96–97%, reflecting more reliable multi-step tool chains. OpenAI
  • Factuality: ~80% fewer factual errors than o3 on LongFact/FActScore-based evaluations (LLM-grader with browsing; details in the system card). OpenAIOpenAI CDN
  • Health: Substantial error reductions across high-stakes categories on HealthBench. (Reminder: not a doctor—use for guidance, not diagnosis.) OpenAI CDN+1

For context on those public benchmarks: see the original FActScore paper and LongFact methodology from Google DeepMind, plus GPQA for graduate-level science questions and MMMU for multimodal reasoning depth. These frame why GPT-5’s deltas are meaningful beyond leaderboard chest-thumping. arXiv+3arXiv+3arXiv+3

Safety & governance—substance over theater

OpenAI categorizes GPT-5 thinking as High capability in bio/chem under its Preparedness Framework, triggering stricter safeguards. The safe-completions paper shows that focusing on output safety reduces severity when failures occur, while maintaining (or improving) helpfulness—especially in dual-use queries where blanket refusals undercut legitimate work. This is a pragmatic shift away from “computer says no.” OpenAI CDN+1

Strategy lens: who benefits first?

  • Product teams get fewer model switches, longer context, and more stable agentic workflows—useful for multi-tool automations and week-long tasks. OpenAI
  • Engineering orgs see higher success on repo-scale debugging and frontend generation; better error handling between tool calls should reduce brittle automations. OpenAI
  • Regulated industries gain from safe-completions and lower hallucination rates, but still need verification in high-stakes use. OpenAI CDN+1

Why This Matters:

GPT-5 isn’t just another “bigger brain.” It’s a decision system that manages its own depth of thought, then proves it on coding, health, and tool-use benchmarks. The safety work moves beyond blunt refusals to graded, safer help, which better matches real workflows. If the agent era is coming, GPT-5’s router + reasoning stack is the blueprint for how AI decides what to do—and when to slow down. OpenAIOpenAI CDN

Practical next steps

  • Migrate prompts to treat reasoning as a dial, not a switch. Default to minimal for routine, escalate for complexity. OpenAI
  • Design with 400k in mind: chunk, cite, and retrieve instead of dumping everything; keep outputs concise with verbosity. OpenAI
  • Instrument agent chains: enforce preambles and status updates between tool calls; trap and retry failures. OpenAI
Pro tip: If your team was juggling 4o vs o3, you now get a router that “knows when to think.” Treat GPT-5 as your default and let the controls—effort, verbosity, custom tools—shape behavior per use case. OpenAI+1

Conclusion

GPT-5 reframes the UI of intelligence. Instead of forcing you to pick a model per task, it routes depth on demand—and backs it up with better factuality, safer outputs, and durable tool use. That combination is what moves us from “chat” into competent, continuous agents.

If you want the most out of GPT-5, don’t chase every knob—pick defaults, measure, and iterate. The future isn’t just bigger models; it’s smarter orchestration of the thinking they already can do. Ready to build?