GPT-5 for Developers: Reasoning Effort, Verbosity and Model Sizes
The August 2025 GPT-5 API release offered three model sizes, a minimal reasoning setting, answer-verbosity control and custom tools that could accept plaintext inputs.
Timeline
- August 7, 2025: OpenAI released gpt-5, gpt-5-mini and gpt-5-nano through its API.
- At launch: The API added minimal reasoning effort, verbosity control and custom plaintext tools.
- After launch: Newer model generations arrived, so current integrations require the live API documentation.
OpenAI released three GPT-5 sizes for developers on August 7, 2025: gpt-5, gpt-5-mini and gpt-5-nano. The sizes gave application builders different tradeoffs among performance, latency and cost. The main API model was a reasoning model, while the fast non-reasoning model used inside ChatGPT was separately exposed at launch as gpt-5-chat-latest. [1][2]
The reasoning_effort control determined how much reasoning work the model could perform before answering. GPT-5 added a minimal setting alongside low, medium and high. OpenAI described lower settings as faster and higher settings as potentially improving quality on difficult tasks, while noting that extra reasoning did not produce equal gains on every evaluation or simple retrieval request. [1]
A separate verbosity parameter guided the default amount of detail in the visible response. Developers could choose low, medium or high, with medium as the stated default. Verbosity did not replace explicit instructions: if a prompt required a fixed structure or number of paragraphs, the direct instruction was intended to take precedence over the general length preference. [1]
GPT-5 also introduced custom tools that accepted plaintext rather than requiring every tool input to be valid JSON. Developers could constrain those inputs with a context-free grammar. This design helped with tools that naturally consume code, SQL, commands or other structured text, while ordinary function calling remained useful when an application needed predictable named fields and schema validation. [1]
At launch, OpenAI said all three API sizes accepted up to 272,000 input tokens and could use up to 128,000 tokens for reasoning and output, described as a 400,000-token total context. Large context does not mean every token receives equal attention or that an answer is guaranteed correct; developers still need retrieval design, tests and output verification. [1][2]
OpenAI emphasized coding and agent workflows and reported benchmark gains on software engineering, instruction following and long-context retrieval. Those numbers were produced under disclosed but specific prompts, reasoning settings and evaluation choices, including omitted unreliable cases in one benchmark. They are launch-era vendor results, useful for comparison but not a substitute for testing an application's own repositories, tools and error costs. [1][2]
The practical distinction is that model size, reasoning effort and verbosity controlled different things. Size selected a capability and cost tier; reasoning effort adjusted hidden computation before the answer; verbosity influenced visible detail. Custom tools changed how the model could call external capabilities. Because API names and limits evolve, these details describe August 2025 and should not be treated as current integration guidance. [1][2]