Introduction to GPT-5

GPT-5 is OpenAI’s flagship model for the second half of 2025. It’s built for deeper reasoning, better coding, and agentic workflows, and it adds two controls that matter in practice: verbosity and reasoning effort. It runs with a 400,000-token total context and can emit up to 128,000 tokens per response.

If you’re new to large language models, skim my plain-English explainer on how GPT-style LLMs work. You’ll prompt better after.

Ready? Let’s ship your first GPT-5 request.

Create an account to get your GPT-5 API key

1. Create an account or sign in.

2. Confirm your email address.
3. Log in.
4. Open the Billing overview page and add credit or a payment method so your keys work right away. (The free-credit program ended mid-2024.)
5. Generate your first API key for GPT-5. Keys are shown once; paste it into a password manager immediately.

Got your key? Great. Time to hit the API.

How to make your first request to GPT-5

OpenAI’s Responses API is the modern endpoint. Chat Completions still works, but start with Responses unless you have a hard reason not to.

macOS and Linux (Responses API):

curl -s https://api.openai.com/v1/responses \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5",
    "input": [
      { "role": "user", "content": [{ "type": "input_text", "text": "Hello!" }] }
    ],
    "verbosity": "medium",
    "reasoning_effort": "minimal",
    "max_output_tokens": 200
  }'

Windows (one-liner, Chat Completions still fine):

curl -X POST -H "Content-Type: application/json" -H "Authorization: Bearer %OPENAI_API_KEY%" https://api.openai.com/v1/chat/completions -d "{ \"model\": \"gpt-5\", \"messages\": [{\"role\":\"user\",\"content\":\"Hello!\"}], \"verbosity\":\"medium\", \"reasoning_effort\":\"minimal\", \"max_output_tokens\":200 }"

Pro tip: Use gpt-5 to track the latest GPT-5 snapshot. If you need strict reproducibility, pin a snapshot in your stack.

Token budget: a single call supports up to 400,000 tokens total. Max output is 128,000 tokens. Your rate-limit tier must be high enough to feed that much TPM; check your org’s quotas before long prompts.

Rock-solid JSON with Structured Outputs (Responses API)

In Responses API, JSON lives under text.format. If you send response_format here, you’ll get the exact error you saw. Use this shape:

curl -s https://api.openai.com/v1/responses \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5",
    "input": [
      { "role": "system", "content": [{ "type": "input_text", "text": "Return compact JSON only." }] },
      { "role": "user",   "content": [{ "type": "input_text", "text": "Solve 8x + 31 = 2." }] }
    ],
    "text": {
      "format": {
        "type": "json_schema",
        "name": "equation_solution",
        "schema": {
          "type": "object",
          "properties": {
            "steps": { "type": "array", "items": { "type": "string" } },
            "final_answer": { "type": "string" }
          },
          "required": ["steps", "final_answer"],
          "additionalProperties": false
        },
        "strict": true
      }
    }
  }'

That’s the Responses-API-correct way to enforce a schema. For Chat Completions, you still use response_format.

Vision and multimodal (quick-start)

GPT-5 accepts text and images in one request. With the Responses API, set image parts as { "type": "input_image", "image_url": "<url or data URL>" }, then put your text after the image for better results.

Supported image formats: PNG, JPEG/JPG, WEBP, non-animated GIF. Size limits: Up to 50 MB total payload per request for image bytes across parts.

Image URL example:

curl -s https://api.openai.com/v1/responses \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5",
    "input": [
      {
        "role": "user",
        "content": [
          { "type": "input_image", "image_url": "https://cdn.example.com/slide.jpg" },
          { "type": "input_text",  "text": "Describe this slide in 5 bullets." }
        ]
      }
    ],
    "max_output_tokens": 250
  }'

Base64 option:

{ "type": "input_image", "image_url": "data:image/jpeg;base64,...." }

Pro tips

• One image per content part unless you’re explicitly comparing; caption each if multiple.
• Prefer URLs in long threads to avoid resending Base64.
• Always cap max_output_tokens so multimodal answers don’t run wild.

Verbosity (new)

What it does: constrains how compact or expansive the answer is without rewriting the prompt. Values: "low", "medium" (default), "high". Set it deliberately.

When to use low: terse assistants, tool-first UX, status replies. When to use high: audits, code reviews, pedagogical explanations.

"verbosity": "low"

Reasoning effort (new)

What it does: controls how much internal reasoning the model does before responding. Values: "minimal", "low", "medium" (default), "high". "minimal" is new and fast for simple tasks.

• Use “minimal” for retrieval, formatting, simple transforms, low-latency UX.
• Use “high” for complex planning, multi-step refactors, ambiguous tradeoffs.

"reasoning_effort": "minimal"

GPT-5 pricing

Prompt-cached input is cheaper; check the official pricing and your model page for cached-input rates.

Output limits: GPT-5 can emit up to 128K tokens per call; GPT-4.1’s max output is ~32K with a ~1.0–1.05M context. If you need the absolute longest context, 4.1 still has the edge; otherwise default to GPT-5.

GPT-5 (full), mini, or nano?

• GPT-5 (full): flagship quality for deep reasoning, complex coding, long-context analysis.
• GPT-5 mini: cost-sensitive apps with crisp prompts.
• GPT-5 nano: ultra-low latency and volume workloads.

There’s also gpt-5-chat-latest if you want a non-reasoning chat flavor.

Did you like this article? Then, keep learning:

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• Step-by-step guide to start using GPT-3.5 Turbo API for beginners
• Learn how to access and use GPT-4 Turbo's API effectively
• Understand how GPT-style AIs like GPT actually work
• Learn to use PHP with OpenAI's API and GPT for practical projects