On August 7, 2025, the company rolled out GPT-5 to ChatGPT users and to the API, with a router that decides when to “think” longer, new model sizes, and pricing aimed at production use. The product page also advertises a 400K token context with 128K maximum output tokens for the new lineup.
OpenAI presented bar charts intended to illustrate improvements in GPT-5’s deception and benchmark performance, only to have them visually contradict the very numbers they displayed. One chart labeled “coding deception” showed GPT-5 (with thinking) at 50 percent but depicted it with a noticeably shorter bar than OpenAI’s o3 at 47.4 percent, even though the written figure for GPT-5 was later corrected to 16.5 percent in the blog post. In another slide, values of 69.1 percent and 30.8 percent were drawn as bars of equal height, while a 52.8 percent figure appeared taller, flipping the intended ranking and misleading the viewer.
The company’s API surface has consolidated around the Responses API. Introduced in March and expanded in May, it is the primary primitive for “agentic” apps, combining multimodal prompting with built-in tools. OpenAI’s update details direct access to image generation, Code Interpreter, improved file search, and remote Model Context Protocol (MCP) servers from a single request. It also adds background mode for long-running tasks, reasoning summaries, and encrypted reasoning items.
OpenAI asserted that for broader ML-engineering tasks such as MLE-Bench and Kaggle-like GPU workloads, the ChatGPT agent (the routed product system) scores highest with a 9 percent bronze pass rate on a curated subset. On SWE-Lancer, which is composed of end-to-end, E2E-tested full-stack tasks, the ChatGPT agent is likewise the best performer. These results suggest GPT-5’s reasoning model is strongest on code-centric debugging and replication, while the routed agent does better on long-horizon, multi-skill workloads.
By opening GPT-5 to everyone immediately, OpenAI locks in massive network effects. New users flock in, existing users upgrade en masse, and ChatGPT’s market pull intensifies. They will spend more to service the use of their most powerful model. GPT-5 is priced at $1.25 per million input tokens and $10 per million output tokens, about half the input cost of GPT-4o. The upside is that they will continue to grow the number of new users integrating ChatGPT into their daily lives. – Reid Hoffman
According to OpenAI, model reliability also improved materially. Against open-ended factuality sets such as LongFact and FActScore, GPT-5 models show substantially lower hallucination rates than OpenAI o3 and prior baselines, with gpt-5-thinking producing over five times fewer factual errors in both browse-on and browse-off settings across the three benchmarks. METR’s autonomy review concludes it is unlikely GPT-5 would increase AI R&D by a factor of ten, conduct strategic sandbagging, or achieve rogue replication. The observed 50 percent time-horizon is approximately two hours and fifteen minutes.
Structured outputs have matured. OpenAI’s cookbook shows strict JSON Schema enforcement with a single flag, which makes it realistic to guarantee shape for downstream systems without post-hoc validators and brittle regex fallbacks. This pairs well with function calling for tool use and reduces glue code in extraction, enrichment, and integration pipelines.
For teams building agent workflows, the surrounding ecosystem matters as much as the base model. OpenAI’s Agents SDK provides orchestration and tracing, and it speaks MCP to connect models to tools hosted on remote servers such as CRM, payment, or support systems. Because MCP is an open protocol, you can standardize tool access across vendors or swap models without rewriting every integration. This is important for portability planning and for containing long-run switching costs.
“Good progress on many fronts but still part of the pack, not a giant leap forward.. lots of questions TBD about real-world performance, obviously not AGI.” – Gary Marcus
User response on Reddit has been volatile since the GPT-5 rollout. Threads in r/ChatGPT report disappointment with perceived changes in tone, stricter rate limits, and the removal of older models. Posts titled “GPT5 is horrible” and “GPT-5 launch” drew heavy discussion. Tech press aggregated the reaction and reported that OpenAI restored GPT-4o as an option following the backlash. For teams shipping against the ChatGPT runtime, the episode is a reminder to instrument user sentiment channels, preserve feature flags for model routing, and plan reversibility in rollout playbooks.
Developers looking to learn more can refer to the GPT-5 system card and follow other recent OpenAI coverage on InfoQ.
