GPT-5 mini
Text Generation • OpenAIGPT-5 Mini is the lightweight, low-cost variant of GPT-5, well suited to high-volume coding and reasoning tasks.
| Model Info | |
|---|---|
| Context Window ↗ | 128,000 tokens |
| Terms and License | link ↗ |
| More information | link ↗ |
| Pricing | View pricing in the Cloudflare dashboard ↗ |
Usage
const response = await env.AI.run( 'openai/gpt-5-mini', { messages: [{ content: 'What are the three laws of thermodynamics?', role: 'user' }] },)console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/chat/completions \ --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \ --header "Content-Type: application/json" \ --data '{ "model": "openai/gpt-5-mini", "messages": [ { "content": "What are the three laws of thermodynamics?", "role": "user" } ]}'Briefly, the fundamental laws are: - Zeroth law (often counted separately): If system A is in thermal equilibrium with B, and B is in thermal equilibrium with C, then A is in thermal equilibrium with C. This justifies the concept of temperature and allows the use of thermometers. - First law (conservation of energy): Energy is conserved. For a closed system, ΔU = Q − W, where ΔU is the change in internal energy, Q is heat added to the system, and W is work done by the system. - Second law (direction of processes; entropy): Natural processes increase the total entropy of an isolated system. A standard mathematical statement: ΔS ≥ ∫ δQ_rev/T, and for an isolated system ΔS ≥ 0. Equivalent formulations: no cyclic engine can convert all heat into work (Kelvin–Planck), and heat cannot spontaneously flow from a colder to a hotter body (Clausius). - Third law (zero‑temperature limit): As T → 0, the entropy of a perfect crystalline substance approaches a constant (often taken as zero). Consequences include the unattainability principle: absolute zero cannot be reached in a finite number of steps. (Traditionally textbooks list the First, Second and Third laws; the Zeroth law was named later because it underpins the concept of temperature.)
{ "choices": [ { "finish_reason": "stop", "index": 0, "message": { "annotations": [], "content": "Briefly, the fundamental laws are:\n\n- Zeroth law (often counted separately): If system A is in thermal equilibrium with B, and B is in thermal equilibrium with C, then A is in thermal equilibrium with C. This justifies the concept of temperature and allows the use of thermometers.\n\n- First law (conservation of energy): Energy is conserved. For a closed system,\n ΔU = Q − W,\n where ΔU is the change in internal energy, Q is heat added to the system, and W is work done by the system.\n\n- Second law (direction of processes; entropy): Natural processes increase the total entropy of an isolated system. A standard mathematical statement:\n ΔS ≥ ∫ δQ_rev/T,\n and for an isolated system ΔS ≥ 0. Equivalent formulations: no cyclic engine can convert all heat into work (Kelvin–Planck), and heat cannot spontaneously flow from a colder to a hotter body (Clausius).\n\n- Third law (zero‑temperature limit): As T → 0, the entropy of a perfect crystalline substance approaches a constant (often taken as zero). Consequences include the unattainability principle: absolute zero cannot be reached in a finite number of steps.\n\n(Traditionally textbooks list the First, Second and Third laws; the Zeroth law was named later because it underpins the concept of temperature.)", "refusal": null, "role": "assistant" } } ], "created": 1777320124, "gatewayMetadata": { "keySource": "Unified" }, "id": "chatcmpl-DZMRYPDmQAXQ6d6GeOKrgb8jeCPgK", "model": "gpt-5-mini-2025-08-07", "object": "chat.completion", "service_tier": "default", "system_fingerprint": null, "usage": { "completion_tokens": 673, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 384, "rejected_prediction_tokens": 0 }, "prompt_tokens": 15, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 }, "total_tokens": 688 }}Examples
With System Message — Using a system message to set context
const response = await env.AI.run( 'openai/gpt-5-mini', { messages: [ { content: 'You are a helpful coding assistant specializing in Python.', role: 'system' }, { content: 'How do I read a JSON file in Python?', role: 'user' }, ], },)console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/chat/completions \ --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \ --header "Content-Type: application/json" \ --data '{ "model": "openai/gpt-5-mini", "messages": [ { "content": "You are a helpful coding assistant specializing in Python.", "role": "system" }, { "content": "How do I read a JSON file in Python?", "role": "user" } ]}'The built‑in json module is the usual way. Use json.load() to parse a file object into Python objects (dicts, lists, strings, numbers, booleans, None).
Basic example:
```python
import json
with open("data.json", "r", encoding="utf-8") as f:
data = json.load(f)
print(type(data)) # usually dict or list
print(data)
```
Read from a JSON string:
```python
s = '{"name": "Alice", "age": 30}'
obj = json.loads(s)
```
Common extras
- Error handling:
```python
import json
try:
with open("data.json", "r", encoding="utf-8") as f:
data = json.load(f)
except FileNotFoundError:
print("File not found")
except json.JSONDecodeError as e:
print("Invalid JSON:", e)
```
- Newline-delimited JSON (NDJSON / JSON Lines):
```python
import json
with open("lines.jsonl", "r", encoding="utf-8") as f:
for line in f:
if line.strip():
obj = json.loads(line)
# process obj
```
- Large files (streaming): for very large JSON arrays, use a streaming parser like ijson to avoid loading everything into memory:
```python
import ijson
with open("big.json", "rb") as f:
for item in ijson.items(f, "item"): # "item" is path for array elements
# process item
```
- Compressed JSON:
```python
import gzip, json
with gzip.open("data.json.gz", "rt", encoding="utf-8") as f:
data = json.load(f)
```
- Convert Python back to JSON:
```python
with open("out.json", "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=2)
```
If your JSON maps naturally to tabular data, pandas.read_json can be convenient:
```python
import pandas as pd
df = pd.read_json("data.json")
```
That covers the usual ways — if you share the structure of your JSON or constraints (huge file, NDJSON, custom types), I can give a more specific example. { "choices": [ { "finish_reason": "stop", "index": 0, "message": { "annotations": [], "content": "The built‑in json module is the usual way. Use json.load() to parse a file object into Python objects (dicts, lists, strings, numbers, booleans, None).\n\nBasic example:\n```python\nimport json\n\nwith open(\"data.json\", \"r\", encoding=\"utf-8\") as f:\n data = json.load(f)\n\nprint(type(data)) # usually dict or list\nprint(data)\n```\n\nRead from a JSON string:\n```python\ns = '{\"name\": \"Alice\", \"age\": 30}'\nobj = json.loads(s)\n```\n\nCommon extras\n\n- Error handling:\n```python\nimport json\n\ntry:\n with open(\"data.json\", \"r\", encoding=\"utf-8\") as f:\n data = json.load(f)\nexcept FileNotFoundError:\n print(\"File not found\")\nexcept json.JSONDecodeError as e:\n print(\"Invalid JSON:\", e)\n```\n\n- Newline-delimited JSON (NDJSON / JSON Lines):\n```python\nimport json\n\nwith open(\"lines.jsonl\", \"r\", encoding=\"utf-8\") as f:\n for line in f:\n if line.strip():\n obj = json.loads(line)\n # process obj\n```\n\n- Large files (streaming): for very large JSON arrays, use a streaming parser like ijson to avoid loading everything into memory:\n```python\nimport ijson\n\nwith open(\"big.json\", \"rb\") as f:\n for item in ijson.items(f, \"item\"): # \"item\" is path for array elements\n # process item\n```\n\n- Compressed JSON:\n```python\nimport gzip, json\n\nwith gzip.open(\"data.json.gz\", \"rt\", encoding=\"utf-8\") as f:\n data = json.load(f)\n```\n\n- Convert Python back to JSON:\n```python\nwith open(\"out.json\", \"w\", encoding=\"utf-8\") as f:\n json.dump(data, f, ensure_ascii=False, indent=2)\n```\n\nIf your JSON maps naturally to tabular data, pandas.read_json can be convenient:\n```python\nimport pandas as pd\ndf = pd.read_json(\"data.json\")\n```\n\nThat covers the usual ways — if you share the structure of your JSON or constraints (huge file, NDJSON, custom types), I can give a more specific example.", "refusal": null, "role": "assistant" } } ], "created": 1777320141, "gatewayMetadata": { "keySource": "Unified" }, "id": "chatcmpl-DZMRprAcSRmcAdqafyZx3VJbKyz1M", "model": "gpt-5-mini-2025-08-07", "object": "chat.completion", "service_tier": "default", "system_fingerprint": null, "usage": { "completion_tokens": 754, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 256, "rejected_prediction_tokens": 0 }, "prompt_tokens": 30, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 }, "total_tokens": 784 }}Multi-turn Conversation — Continuing a conversation with context
const response = await env.AI.run( 'openai/gpt-5-mini', { max_completion_tokens: 8192, messages: [ { content: 'I need help planning a road trip from San Francisco to Los Angeles.', role: 'user', }, { content: "I'd be happy to help! The drive is about 380 miles and takes roughly 5-6 hours. Would you like suggestions for scenic routes or interesting stops along the way?", role: 'assistant', }, { content: 'Yes, what are some good places to stop?', role: 'user' }, ], },)console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/chat/completions \ --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \ --header "Content-Type: application/json" \ --data '{ "model": "openai/gpt-5-mini", "max_completion_tokens": 8192, "messages": [ { "content": "I need help planning a road trip from San Francisco to Los Angeles.", "role": "user" }, { "content": "I'\''d be happy to help! The drive is about 380 miles and takes roughly 5-6 hours. Would you like suggestions for scenic routes or interesting stops along the way?", "role": "assistant" }, { "content": "Yes, what are some good places to stop?", "role": "user" } ]}'Great — here are several good stop options and a few sample itineraries depending on how much time you have and which route you take (PCH/Highway 1 for scenery, US‑101 for a balance of speed and towns, I‑5 if you want the fastest drive). High-level route choices - Pacific Coast Highway (CA‑1 / PCH): Most scenic — cliffs, beaches, Big Sur. Slow, can be winding and has occasional closures/traffic. - US‑101: Faster than CA‑1 and still scenic through coastal towns, wine country, and beaches. - I‑5: Fastest (about 5–6 hours), but mostly inland and not scenic. Good if you want to minimize driving time. Useful tip: check Caltrans for road conditions and CA‑1/Big Sur closures before you go; plan around LA rush hour (avoid entering LA 3–7 pm) and leaving SF early to beat Bay Area traffic. Stops (north → south) — highlights by route Common coastal highlights (CA‑1 & 101 overlap in places) - Half Moon Bay (30–45 min from SF) - Quick beach walk, pastries (Local bakeries), coastal trail views. - Santa Cruz (1–1.5 hr from SF) - Boardwalk, surf spots, downtown restaurants. - Capitola (near Santa Cruz) - Colorful seaside village good for a short stroll and lunch. Monterey/Carmel area (great for families/couples) - Monterey Bay Aquarium (Monterey) - World-class aquarium, Cannery Row dining. - 17‑Mile Drive / Pebble Beach (Carmel/Monterey) - Scenic loop with coastal viewpoints and Lone Cypress. - Carmel-by-the-Sea - Charming village, galleries, beach. Big Sur (must-see if you have time) - Bixby Creek Bridge (iconic photo stop) - Pfeiffer Big Sur State Park (hiking) - Pfeiffer Beach (purple sand, limited parking) - McWay Falls / Julia Pfeiffer Burns State Park (waterfall onto beach) - Note: limited services and cell coverage — fuel up in Monterey or Carmel. San Simeon / Cambria / Hearst Castle - Piedras Blancas elephant seal rookery (near San Simeon) - Hearst Castle tour (advance tickets recommended) - Cambria — quaint village for dinner/overnight. San Luis Obispo / Pismo Beach / Morro Bay - San Luis Obispo (downtown, bubblegum alley) - Pismo Beach (cliffs, monarch butterflies seasonally) - Morro Bay (Morro Rock, kayaking) Santa Ynez Valley / Solvang (inland detour off PCH/101) - Danish-style Solvang, wineries in Santa Ynez and Los Olivos — great for wine tasting and a slower afternoon. Santa Barbara (the “American Riviera”) - State Street, waterfront, mission, good dining and beaches. Last stretch into Los Angeles (via US‑101 or PCH) - Ventura (surf, harbor) - Malibu (beaches, Zuma, Point Dume) - Santa Monica / Venice (pier, boardwalk, restaurants) before heading to downtown LA or other neighborhoods. If you take I‑5 (fastest) — practical stops - Gilroy (garlic/food if you want a quick break) - Kettleman City or Harris Ranch (famous steakhouse stop) - Tejon Ranch outlets near the Grapevine (shopping, quick break) - Pyramid Lake/ghost-town stops are possible but mostly freeway views Sample itineraries 1) One-day drive, scenic highlights (long day) - Early SF departure (6–7 am) → Santa Cruz (coffee, 1 hr) → Monterey (lunch, Aquarium optional, 1.5–2 hr) → Big Sur (Bixby Bridge & viewpoints, 1–1.5 hr) → San Simeon (elephant seals) → Santa Barbara arrival late evening. Expect 10–12+ hours including stops. 2) Two-day relaxed coastal trip (recommended) Day 1: SF → Half Moon Bay → Santa Cruz → Monterey/Carmel (overnight) Day 2: Carmel → Big Sur (Pfeiffer Beach, McWay Falls) → San Simeon or Pismo → Santa Barbara → LA - Overnight options: Carmel, Big Sur (if available), or San Luis Obispo/Santa Barbara to split driving times. 3) Three-day scenic + wine-country Day 1: SF → Santa Cruz → Monterey/Carmel (explore 17‑Mile Drive) Day 2: Carmel → Big Sur → San Simeon → Pismo/Morro Bay (overnight) Day 3: Pismo → Solvang (wine tasting) → Santa Barbara → LA Practical tips - Time: Without stops SF→LA on PCH/101 is 8–10 hours; I‑5 ~5–6 hours. - Fuel and services: Fill up before Big Sur; limited gas and few restaurants in stretches. - Reservations: Hotels in Big Sur and popular towns fill up fast — book ahead. Hearst Castle requires timed tickets. - Weather/season: Summer fog near SF; Big Sur can have wind and cooler temps. CA‑1 sometimes closes after storms/landslides. - Parking: Popular spots (Pfeiffer Beach, Bixby turnout, McWay Falls) can have limited parking — arrive early. - Safety: Drive carefully on winding sections, keep an eye on fuel, cell coverage is spotty in Big Sur. If you tell me: - how many days you have, - whether you prefer beaches, hiking, wine, food stops, or quick travel, I’ll draft a detailed day‑by‑day plan with drive times, restaurants and where to stay.
{ "choices": [ { "finish_reason": "stop", "index": 0, "message": { "annotations": [], "content": "Great — here are several good stop options and a few sample itineraries depending on how much time you have and which route you take (PCH/Highway 1 for scenery, US‑101 for a balance of speed and towns, I‑5 if you want the fastest drive).\n\nHigh-level route choices\n- Pacific Coast Highway (CA‑1 / PCH): Most scenic — cliffs, beaches, Big Sur. Slow, can be winding and has occasional closures/traffic.\n- US‑101: Faster than CA‑1 and still scenic through coastal towns, wine country, and beaches.\n- I‑5: Fastest (about 5–6 hours), but mostly inland and not scenic. Good if you want to minimize driving time.\n\nUseful tip: check Caltrans for road conditions and CA‑1/Big Sur closures before you go; plan around LA rush hour (avoid entering LA 3–7 pm) and leaving SF early to beat Bay Area traffic.\n\nStops (north → south) — highlights by route\n\nCommon coastal highlights (CA‑1 & 101 overlap in places)\n- Half Moon Bay (30–45 min from SF)\n - Quick beach walk, pastries (Local bakeries), coastal trail views.\n- Santa Cruz (1–1.5 hr from SF)\n - Boardwalk, surf spots, downtown restaurants.\n- Capitola (near Santa Cruz)\n - Colorful seaside village good for a short stroll and lunch.\n\nMonterey/Carmel area (great for families/couples)\n- Monterey Bay Aquarium (Monterey)\n - World-class aquarium, Cannery Row dining.\n- 17‑Mile Drive / Pebble Beach (Carmel/Monterey)\n - Scenic loop with coastal viewpoints and Lone Cypress.\n- Carmel-by-the-Sea\n - Charming village, galleries, beach.\n\nBig Sur (must-see if you have time)\n- Bixby Creek Bridge (iconic photo stop)\n- Pfeiffer Big Sur State Park (hiking)\n- Pfeiffer Beach (purple sand, limited parking)\n- McWay Falls / Julia Pfeiffer Burns State Park (waterfall onto beach)\n- Note: limited services and cell coverage — fuel up in Monterey or Carmel.\n\nSan Simeon / Cambria / Hearst Castle\n- Piedras Blancas elephant seal rookery (near San Simeon)\n- Hearst Castle tour (advance tickets recommended)\n- Cambria — quaint village for dinner/overnight.\n\nSan Luis Obispo / Pismo Beach / Morro Bay\n- San Luis Obispo (downtown, bubblegum alley)\n- Pismo Beach (cliffs, monarch butterflies seasonally)\n- Morro Bay (Morro Rock, kayaking)\n\nSanta Ynez Valley / Solvang (inland detour off PCH/101)\n- Danish-style Solvang, wineries in Santa Ynez and Los Olivos — great for wine tasting and a slower afternoon.\n\nSanta Barbara (the “American Riviera”)\n- State Street, waterfront, mission, good dining and beaches.\n\nLast stretch into Los Angeles (via US‑101 or PCH)\n- Ventura (surf, harbor)\n- Malibu (beaches, Zuma, Point Dume)\n- Santa Monica / Venice (pier, boardwalk, restaurants) before heading to downtown LA or other neighborhoods.\n\nIf you take I‑5 (fastest) — practical stops\n- Gilroy (garlic/food if you want a quick break)\n- Kettleman City or Harris Ranch (famous steakhouse stop)\n- Tejon Ranch outlets near the Grapevine (shopping, quick break)\n- Pyramid Lake/ghost-town stops are possible but mostly freeway views\n\nSample itineraries\n\n1) One-day drive, scenic highlights (long day)\n- Early SF departure (6–7 am) → Santa Cruz (coffee, 1 hr) → Monterey (lunch, Aquarium optional, 1.5–2 hr) → Big Sur (Bixby Bridge & viewpoints, 1–1.5 hr) → San Simeon (elephant seals) → Santa Barbara arrival late evening. Expect 10–12+ hours including stops.\n\n2) Two-day relaxed coastal trip (recommended)\nDay 1: SF → Half Moon Bay → Santa Cruz → Monterey/Carmel (overnight)\nDay 2: Carmel → Big Sur (Pfeiffer Beach, McWay Falls) → San Simeon or Pismo → Santa Barbara → LA\n- Overnight options: Carmel, Big Sur (if available), or San Luis Obispo/Santa Barbara to split driving times.\n\n3) Three-day scenic + wine-country\nDay 1: SF → Santa Cruz → Monterey/Carmel (explore 17‑Mile Drive)\nDay 2: Carmel → Big Sur → San Simeon → Pismo/Morro Bay (overnight)\nDay 3: Pismo → Solvang (wine tasting) → Santa Barbara → LA\n\nPractical tips\n- Time: Without stops SF→LA on PCH/101 is 8–10 hours; I‑5 ~5–6 hours.\n- Fuel and services: Fill up before Big Sur; limited gas and few restaurants in stretches.\n- Reservations: Hotels in Big Sur and popular towns fill up fast — book ahead. Hearst Castle requires timed tickets.\n- Weather/season: Summer fog near SF; Big Sur can have wind and cooler temps. CA‑1 sometimes closes after storms/landslides.\n- Parking: Popular spots (Pfeiffer Beach, Bixby turnout, McWay Falls) can have limited parking — arrive early.\n- Safety: Drive carefully on winding sections, keep an eye on fuel, cell coverage is spotty in Big Sur.\n\nIf you tell me:\n- how many days you have,\n- whether you prefer beaches, hiking, wine, food stops, or quick travel,\nI’ll draft a detailed day‑by‑day plan with drive times, restaurants and where to stay.", "refusal": null, "role": "assistant" } } ], "created": 1777420669, "gatewayMetadata": { "keySource": "BYOK" }, "id": "chatcmpl-DZmbFd2Ooyi5zmVdbSPxGyjtrwUUO", "model": "gpt-5-mini-2025-08-07", "object": "chat.completion", "service_tier": "default", "system_fingerprint": null, "usage": { "completion_tokens": 1926, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 704, "rejected_prediction_tokens": 0 }, "prompt_tokens": 76, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 }, "total_tokens": 2002 }}Creative Writing — Longer completion for creative output
const response = await env.AI.run( 'openai/gpt-5-mini', { max_completion_tokens: 8192, messages: [ { content: 'Write a short story opening about a detective finding an unusual clue.', role: 'user', }, ], },)console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/chat/completions \ --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \ --header "Content-Type: application/json" \ --data '{ "model": "openai/gpt-5-mini", "max_completion_tokens": 8192, "messages": [ { "content": "Write a short story opening about a detective finding an unusual clue.", "role": "user" } ]}'The stairwell smelled like old coffee and lemon oil; the landlord kept the cleaner’s bottle on the second landing like a totem. I’d been up and down those stairs enough to know the rhythm of the building—how the pipes sang at night, which door stuck in winter—but the little hallway outside 3B hit a chord I hadn’t heard before: silence so neat it felt deliberate. Her coat was still on the back of a chair, collar up like a small, pale stack of waves. No sign of forced entry, no overturned furniture, just the slow, inevitable disorder of someone who left thinking she’d be right back. I moved to the chair because detectives move where other people don’t: to pockets. Fingers downed in fabric, searching for lint and receipts and the kind of trash that forgets its own story. Folded twice, tucked in the inner pocket, was a drawing on cheap paper—crayon blue and stubborn as truth. A stick figure, two dots for eyes, and across the forehead a jagged line of darker crayon. At the bottom, in a child’s hurried script, the name: Jonah. My Jonah. The line across the forehead was the scar I got when I was eight and dared a chain-link fence like a daredevil out of hindsight. A scar nobody I worked with would know about; a scar I had never told anyone about. The paper smelled faintly of rain and something waxy. I held it up to the single strip of window light and the crayon wax glowed like it had a pulse. The building hummed. Downstairs, someone laughed at nothing. In my chest, something rearranged—an old drawer opened, and a key I’d misplaced years ago slid into my hand. Not a clue so much as an accusation: someone had been in her pockets and had known the exact shape of my face.
{ "choices": [ { "finish_reason": "stop", "index": 0, "message": { "annotations": [], "content": "The stairwell smelled like old coffee and lemon oil; the landlord kept the cleaner’s bottle on the second landing like a totem. I’d been up and down those stairs enough to know the rhythm of the building—how the pipes sang at night, which door stuck in winter—but the little hallway outside 3B hit a chord I hadn’t heard before: silence so neat it felt deliberate.\n\nHer coat was still on the back of a chair, collar up like a small, pale stack of waves. No sign of forced entry, no overturned furniture, just the slow, inevitable disorder of someone who left thinking she’d be right back. I moved to the chair because detectives move where other people don’t: to pockets. Fingers downed in fabric, searching for lint and receipts and the kind of trash that forgets its own story.\n\nFolded twice, tucked in the inner pocket, was a drawing on cheap paper—crayon blue and stubborn as truth. A stick figure, two dots for eyes, and across the forehead a jagged line of darker crayon. At the bottom, in a child’s hurried script, the name: Jonah. My Jonah. The line across the forehead was the scar I got when I was eight and dared a chain-link fence like a daredevil out of hindsight. A scar nobody I worked with would know about; a scar I had never told anyone about.\n\nThe paper smelled faintly of rain and something waxy. I held it up to the single strip of window light and the crayon wax glowed like it had a pulse. The building hummed. Downstairs, someone laughed at nothing. In my chest, something rearranged—an old drawer opened, and a key I’d misplaced years ago slid into my hand. Not a clue so much as an accusation: someone had been in her pockets and had known the exact shape of my face.", "refusal": null, "role": "assistant" } } ], "created": 1777320157, "gatewayMetadata": { "keySource": "Unified" }, "id": "chatcmpl-DZMS5kOjcgUQY5W2AZnL6aYVk6pbG", "model": "gpt-5-mini-2025-08-07", "object": "chat.completion", "service_tier": "default", "system_fingerprint": null, "usage": { "completion_tokens": 1416, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 1024, "rejected_prediction_tokens": 0 }, "prompt_tokens": 19, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 }, "total_tokens": 1435 }}Streaming Response — Enable streaming for real-time output
const response = await env.AI.run( 'openai/gpt-5-mini', { messages: [{ content: 'Explain the concept of recursion with a simple example.', role: 'user' }], stream: true, stream_options: { include_usage: true }, },)console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/chat/completions \ --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \ --header "Content-Type: application/json" \ --data '{ "model": "openai/gpt-5-mini", "messages": [ { "content": "Explain the concept of recursion with a simple example.", "role": "user" } ], "stream": true, "stream_options": { "include_usage": true }}'Recursion is when a function calls itself to solve a smaller instance of the same problem. Two parts are essential:
- Base case: a simple instance that can be answered directly (stops recursion).
- Recursive case: reduces the problem toward the base case by calling the function again.
Simple example — factorial (n! = n × (n−1) × ... × 1)
Python:
def factorial(n):
if n == 0: # base case
return 1
else: # recursive case
return n * factorial(n - 1)
Trace for factorial(4):
factorial(4)
→ 4 * factorial(3)
→ 4 * (3 * factorial(2))
→ 4 * (3 * (2 * factorial(1)))
→ 4 * (3 * (2 * (1 * factorial(0))))
→ 4 * 3 * 2 * 1 * 1 = 24
Another simple example — sum of a list:
def sum_list(lst):
if not lst: # base case: empty list
return 0
return lst[0] + sum_list(lst[1:]) # recursive case
Notes:
- Always ensure the base case will be reached (otherwise you get infinite recursion and eventually a stack overflow).
- Recursion can make code clearer for problems that naturally break into smaller subproblems (tree traversal, divide-and-conquer). For very deep recursion, consider iterative solutions or tail recursion (if the language optimizes it). 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objectfrequency_penalty
numbermaximum: 2minimum: -2max_completion_tokens
numberexclusiveMinimum: 0max_tokens
numberexclusiveMinimum: 0▶messages[]
arrayrequired▶modalities[]
arraypresence_penalty
numbermaximum: 2minimum: -2response_format
stream
boolean▶stream_options{}
objecttemperature
numbermaximum: 2minimum: 0tool_choice
▶tools[]
arraytop_p
numbermaximum: 1minimum: 0▶choices[]
arraycreated
numberid
stringmodel
stringobject
string▶usage{}
object