> ## Documentation Index
> Fetch the complete documentation index at: https://docs.deepinfra.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Request Compression

> Compress large request bodies with gzip to cut upload bandwidth and latency.

DeepInfra accepts **gzip-compressed request bodies**. For large payloads — long chat
histories, big embeddings batches, sizable inputs — compressing the request shrinks the
bytes you upload (JSON and text typically compress 4–8×), which lowers your egress and can
speed up requests over slower or metered connections.

To use it, gzip the request body and set the `Content-Encoding: gzip` header. It works on
**every** endpoint (chat completions, embeddings, and the rest), including requests whose
response streams back — the request body is a single upload regardless of how the response
is returned.

<CodeGroup>
  ```bash cURL theme={null}
  # Pipe the JSON body through gzip and send it with Content-Encoding: gzip
  echo '{
    "model": "deepseek-ai/DeepSeek-V3",
    "messages": [{"role": "user", "content": "Hello!"}]
  }' | gzip | curl "https://api.deepinfra.com/v1/openai/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Content-Encoding: gzip" \
    -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
    --data-binary @-
  ```

  ```python Python theme={null}
  import gzip
  import json
  import requests

  payload = json.dumps({
      "model": "deepseek-ai/DeepSeek-V3",
      "messages": [{"role": "user", "content": "Hello!"}],
  }).encode("utf-8")

  response = requests.post(
      "https://api.deepinfra.com/v1/openai/chat/completions",
      data=gzip.compress(payload),
      headers={
          "Content-Type": "application/json",
          "Content-Encoding": "gzip",
          "Authorization": "Bearer $DEEPINFRA_TOKEN",
      },
  )
  print(response.json())
  ```

  ```javascript JavaScript theme={null}
  import { gzipSync } from "node:zlib";

  const payload = JSON.stringify({
    model: "deepseek-ai/DeepSeek-V3",
    messages: [{ role: "user", content: "Hello!" }],
  });

  const response = await fetch("https://api.deepinfra.com/v1/openai/chat/completions", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Content-Encoding": "gzip",
      Authorization: "Bearer $DEEPINFRA_TOKEN",
    },
    body: gzipSync(payload),
  });
  console.log(await response.json());
  ```
</CodeGroup>

## When it helps

Request compression pays off in proportion to how large your request body is:

* **Worth it** for large bodies — long conversations, large embeddings batches, big inputs —
  where trimming the upload meaningfully cuts bandwidth and transfer time.
* **Not worth it** for small requests (a short prompt). gzip adds a small header and a little
  CPU on both ends, so for tiny bodies there's nothing to gain.

<Note>
  **gzip only.** `Content-Encoding: gzip` is the only supported request compression. Other
  codings (`deflate`, `br`, `zstd`) are not decompressed — a body compressed with those will
  fail to parse. Use `gzip`.
</Note>