AnyModelStreaming vs non-streaming LLM responses explained
When you call a language model, you get the answer in one of two ways: all at once when it's finished (non-streaming), or token by token as it's generated (streaming). The choice affects perceived speed, how you parse the output, and how your code handles errors. Here's a practical breakdown.
What non-streaming means
In non-streaming mode, the API holds the connection open until the model finishes generating, then returns a single JSON object with the full text. Your code makes one request and reads one response — simple and predictable.
curl https://anymodel.org/v1/chat/completions \
-H "Authorization: Bearer $ANYMODEL_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-4o","messages":[{"role":"user","content":"Hi"}]}'
The downside is wait time. If a reply takes 12 seconds to generate, the user stares at a spinner for 12 seconds before seeing anything.
What streaming means
In streaming mode, the server sends partial output as it's produced, usually as Server-Sent Events (SSE). Each chunk carries a small delta, and a final [DONE] marker closes the stream. Set "stream": true in the request body and the same endpoint behaves differently.
The first visible token can arrive in a few hundred milliseconds. The total generation time is roughly the same, but time to first token (TTFT) drops dramatically — which is what users actually feel.
Side-by-side comparison
| Factor | Non-streaming | Streaming |
|---|---|---|
| Perceived speed | Slower (wait for full reply) | Fast (text appears immediately) |
| Implementation | Simple — one JSON response | Harder — parse incremental chunks |
| Error handling | Clean: success or failure | Mid-stream failures need recovery |
| Token usage data | Returned in the response | Often only in the final chunk |
| Best for | Batch jobs, function calls, short replies | Chat UIs, long-form generation, coding tools |
When to use each
Reach for streaming when a human is watching. Chat interfaces, AI writing assistants, and coding agents all benefit from text that scrolls in live. It keeps users engaged and lets them cancel early if the answer drifts off course. CLI coding tools stream by default for exactly this reason.
Reach for non-streaming when a machine is consuming the output. Batch summarization, data extraction, classification, and scoring pipelines don't care about TTFT — they need the complete, validated result. Non-streaming also makes structured outputs and tool/function calls easier to handle, since you parse one finished object instead of reassembling fragments. The cost is identical either way: you pay per token regardless of how the bytes are delivered.
The gotchas of streaming
Streaming is not free of friction:
- Parsing. You must accumulate deltas and handle the closing event. Most SDKs give you an iterator, but raw HTTP clients need care.
- Mid-stream errors. A network drop after 80% of the text means partial output. Decide whether to retry or salvage.
- Buffering proxies. Some load balancers buffer responses, silently defeating streaming. Test end to end.
- Usage accounting. Token counts usually land in the last chunk, so don't read them too early.
Switching modes without switching providers
The convenient part is that streaming is a per-request flag, not a different API. With AnyModel you point your client at one OpenAI-compatible endpoint — https://anymodel.org/v1 — and a single API key reaches GPT, Claude, Gemini, DeepSeek, GLM, Kimi, Qwen, and Grok. To switch models you change the model id; to switch delivery mode you flip stream. Nothing else changes, so you can compare models on the same prompt and decide which streams fastest for your use case.
For supported CLIs (codex, claude, opencode, hermes) the one-line installer wires everything up:
bash <(curl -fsSL "https://anymodel.org/i?tool=codex") <YOUR_API_KEY>
For Cursor, Windsurf, Zed, Cline, Aider, Continue, or Gemini CLI, use a manual OpenAI-compatible setup with the same base URL and key. If privacy matters for streamed or batched prompts, Ghost Mode offers opt-in zero-retention keys — we keep only a token counter, though the model provider still receives your prompt.
Bottom line
Streaming wins on perceived speed and is the right default for anything a person reads in real time. Non-streaming wins on simplicity and is ideal for automated pipelines and structured results. Since both run over the same endpoint, you don't have to commit — pick per request. Want more practical guides? Browse the blog.
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