Generate text with Flatkey

Base URL: https://router.flatkey.ai Text generation runs through POST /v1/chat/completions, which matches the OpenAI Chat Completions API exactly. Any OpenAI-compatible client works after you change the base URL.

No rewrite needed

Keep your existing request code and point base_url at https://router.flatkey.ai/v1.

One key, every model

Switch models by changing the model field. Nothing else changes.

Make your first call

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["FLATKEY_API_KEY"],
    base_url="https://router.flatkey.ai/v1",
)

response = client.chat.completions.create(
    model="claude-sonnet-5",
    messages=[{"role": "user", "content": "Explain vector databases in two sentences."}],
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FLATKEY_API_KEY,
  baseURL: "https://router.flatkey.ai/v1",
});

const response = await client.chat.completions.create({
  model: "claude-sonnet-5",
  messages: [{ role: "user", content: "Explain vector databases in two sentences." }],
});

console.log(response.choices[0].message.content);

curl --fail-with-body -sS https://router.flatkey.ai/v1/chat/completions \
  -H "Authorization: Bearer YOUR_FLATKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-5",
    "messages": [
      { "role": "user", "content": "Explain vector databases in two sentences." }
    ]
  }'

Choose a model

List everything your account can reach and keep the text models:

curl --fail-with-body -sS https://router.flatkey.ai/v1/models \
  -H "Authorization: Bearer YOUR_FLATKEY_API_KEY" \
  | jq '.data[] | select(.type == "text") | .id'

Browse the same list with pricing, context length, and latency on the model directory. A practical starting point:

Need	Try
Everyday coding and reasoning	`claude-sonnet-5`, `gpt-5.6-sol`
Long documents or whole repositories	`deepseek-v4-pro`, `kimi-k3`
High volume at low cost	`deepseek-v4-flash`, `gemini-2.5-flash-lite`
Agentic coding	`glm-5.3`, `claude-opus-5`

Stream the response

Set stream: true to receive tokens as they are produced:

stream = client.chat.completions.create(
    model="gemini-2.5-flash",
    messages=[{"role": "user", "content": "Write a haiku about routing."}],
    stream=True,
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Each chunk carries a partial delta instead of a complete message.

Call your own functions

Pass tool definitions and the model decides when to invoke them:

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Current weather for a city",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    },
}]

response = client.chat.completions.create(
    model="claude-sonnet-5",
    messages=[{"role": "user", "content": "What is the weather in Paris?"}],
    tools=tools,
)

print(response.choices[0].message.tool_calls)

To finish the loop, append the assistant message, then one message per tool result keyed by tool_call_id, and send the whole thread back.

When a tool fires, finish_reason is tool_calls, not stop. Code that only checks for stop will drop the call silently.

Tool calling is a capability of the model, not of Flatkey. The GPT, Claude, Gemini, Qwen, DeepSeek, and GLM families support it. Image, video, and speech models ignore a tools array.

Force a JSON shape

Use response_format when you need to parse the answer:

response = client.chat.completions.create(
    model="gpt-5.6-sol",
    messages=[{"role": "user", "content": "Extract the city and country."}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "location",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {"city": {"type": "string"}, "country": {"type": "string"}},
                "required": ["city", "country"],
                "additionalProperties": False,
            },
        },
    },
)

{"type": "json_object"} also works when you only need valid JSON without a fixed schema.

Read token usage

Every response carries the counts you are billed on:

"usage": {
  "prompt_tokens": 28,
  "completion_tokens": 22,
  "total_tokens": 50
}

Different model families tokenize differently, so the same text costs a different number of tokens on different models. Use usage rather than estimating from character counts. Per-request cost also appears in Usage logs.

Troubleshooting

No available channel for model ... That model is not routable right now. Pick another id from /v1/models. Retrying the same model does not clear this. The response stops mid-sentence finish_reason is length. Raise max_tokens. Tool calls never fire Confirm the model supports tools, and check that tool_choice is not set to none.

Quick Start

Code

Mainstream AI Tools

Media Generation

Popular models

Dashboard

Reference

Generate text with Flatkey

No rewrite needed

One key, every model

Make your first call

Choose a model

Stream the response

Call your own functions

Force a JSON shape

Read token usage

Troubleshooting

Next steps

API reference

OpenAI SDK guide

No rewrite needed

One key, every model

​Make your first call

​Choose a model

​Stream the response

​Call your own functions

​Force a JSON shape

​Read token usage

​Troubleshooting

​Next steps

API reference

OpenAI SDK guide

Make your first call

Choose a model

Stream the response

Call your own functions

Force a JSON shape

Read token usage

Troubleshooting

Next steps