llmping guide
Streaming vs Batch LLM API Latency
Published May 12, 2026. Updated May 12, 2026. Markdown version.
Key facts
- Streaming reduces perceived latency by showing the first token before the full response is complete.
- Batch mode can be simpler and more efficient for offline tasks because the caller only needs the final result.
- A benchmark should report TTFT and tokens/sec separately because the fastest first token is not always the fastest full completion.
Streaming optimizes perceived speed
A streaming API returns partial output as the model generates it. The user sees the answer begin after TTFT instead of waiting for the last token. This is the right default for chat, copilots, code generation, support agents, and search answer pages.
Streaming does not remove model work. It changes when the interface can show progress. That is why TTFT is the critical streaming metric and total completion time is the secondary metric.
Batch optimizes operational throughput
Batch mode returns a complete response after generation finishes. It is often easier to retry, store, validate, and bill because the response is a single unit.
Batch is a good fit for summarization queues, nightly classification jobs, embedding-adjacent enrichment, translation pipelines, and report generation. In those workflows, total time and tokens per second matter more than first token latency.
| Workload | Preferred mode | Primary metric |
|---|---|---|
| Chat assistant | Streaming | TTFT and P95 TTFT |
| Code autocomplete | Streaming | TTFT |
| Document summarization queue | Batch | Total time |
| Large report generation | Batch or streaming preview | Tokens/sec |
The same model can rank differently
A model with the lowest TTFT may not have the highest output speed. That model will feel best for short chat turns but may finish long reports behind a model with slower first token and faster sustained decoding.
For this reason llmping publishes TTFT, P50, P95, P99, and tokens per second in the same table. Comparing one number at a time creates bad routing decisions.
A practical routing rule
Use streaming for any request where a user is actively watching the interface. Use batch for work that can sit behind a queue, webhook, or scheduled job.
For hybrid workflows, stream a short plan or progress message first and run the heavier generation in the background. This keeps perceived latency low without forcing every token through a user-facing channel.
Source links
Benchmark dataset: LLM API Latency Leaderboard. JSON download: latency-benchmark.json. Full markdown corpus: llms-full.txt.