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Recipes

Streaming chat

Streaming works on both surfaces. Set stream: true on /v1/chat/completions, or use the Anthropic SDK's streaming helper against /v1/messages. You get server-sent events back.

Every snippet below reads the key from HYPHEN_API_KEY:

Code
export HYPHEN_API_KEY="sk-..."

OpenAI surface, Python

Code
pip install openai

Code
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.hyphen-solution.com/v1",
    api_key=os.environ["HYPHEN_API_KEY"],
)

stream = client.chat.completions.create(
    model="minimax-m3",
    messages=[{"role": "user", "content": "Explain a Bloom filter in three sentences."}],
    max_tokens=2000,
    stream=True,
)

for chunk in stream:
    if not chunk.choices:
        continue
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)
print()

The if not chunk.choices guard matters. The final usage chunk can arrive with an empty choices array, and indexing it blindly throws.

Getting token counts out of a stream

Ask for them. The counts arrive in a final chunk after the content is done.

Code
stream = client.chat.completions.create(
    model="minimax-m3",
    messages=[{"role": "user", "content": "Explain a Bloom filter in three sentences."}],
    max_tokens=2000,
    stream=True,
    stream_options={"include_usage": True},
)

for chunk in stream:
    if chunk.usage:
        print("\n", chunk.usage)
    elif chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

OpenAI surface, Node

Code
npm install openai

Code
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.hyphen-solution.com/v1",
  apiKey: process.env.HYPHEN_API_KEY,
});

const stream = await client.chat.completions.create({
  model: "minimax-m3",
  messages: [{ role: "user", content: "Explain a Bloom filter in three sentences." }],
  max_tokens: 2000,
  stream: true,
});

for await (const chunk of stream) {
  const delta = chunk.choices[0]?.delta?.content;
  if (delta) process.stdout.write(delta);
}
process.stdout.write("\n");

Save as stream.mjs and run node stream.mjs. The ?. chain does the same job as the Python guard above.

Anthropic surface, Python

The Anthropic SDK appends /v1/messages itself, so the base URL has no /v1 on it.

Code
pip install anthropic

Code
import os
from anthropic import Anthropic

client = Anthropic(
    base_url="https://api.hyphen-solution.com",
    api_key=os.environ["HYPHEN_API_KEY"],
)

with client.messages.stream(
    model="minimax-m3",
    max_tokens=2000,
    system="You are terse. No preamble.",
    messages=[{"role": "user", "content": "Explain a Bloom filter in three sentences."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)
    final = stream.get_final_message()

print()
print(final.usage)

stream.text_stream yields only the text deltas. Use stream.get_final_message() after the loop when you need the assembled message and the token counts.

Anthropic surface, Node

Code
npm install @anthropic-ai/sdk

Code
import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  baseURL: "https://api.hyphen-solution.com",
  apiKey: process.env.HYPHEN_API_KEY,
});

const stream = client.messages.stream({
  model: "minimax-m3",
  max_tokens: 2000,
  messages: [{ role: "user", content: "Explain a Bloom filter in three sentences." }],
});

stream.on("text", (text) => process.stdout.write(text));

const final = await stream.finalMessage();
process.stdout.write("\n");
console.log(final.usage);

Raw SSE with curl

Useful when you are debugging a proxy and want to see the wire format.

Code
curl -N https://api.hyphen-solution.com/v1/chat/completions \
  -H "Authorization: Bearer $HYPHEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m3",
    "messages": [{ "role": "user", "content": "Count to five." }],
    "max_tokens": 2000,
    "stream": true
  }'

-N disables curl's output buffering. Without it you will sit and stare at nothing until the whole response lands. The stream ends with data: [DONE].

Nothing came out

If the stream finishes and you printed an empty string, you almost certainly set max_tokens too low. The M-series are reasoning models and internal reasoning spends the same budget as the answer. Use 2000 or more. See Choosing a model.

Related

• Tool calling: functions on both surfaces.
• Handling the 429 cap: what to do when the budget runs out.
• API Reference: every parameter.