---
id: coding-agent-streaming-apr26
slug: coding-agent-streaming-adoption-apr-2026
title: "Streaming adoption by coding agent, April 2026"
topic: agentic
period: Apr 2026
updated: 2026-05-16
license: CC BY 4.0
canonical: https://requesty.ai/data/coding-agent-streaming-adoption-apr-2026
---

# Streaming adoption by coding agent, April 2026

> Do coding agents stream their API responses? In April 2026, most agents stream nearly 100% of calls. Aider is the major outlier at 22% streaming, preferring batch completions. Claude Code streams 93% of calls. Aider also has the highest reasoning token intensity at 82%, suggesting it relies on reasoning models in non-streaming mode.

*Topic: Agentic workloads. Period: Apr 2026. Last updated 2026-05-16.*

## Why it matters

Streaming affects both user experience and infrastructure cost. Streaming responses allow coding agents to show partial output in real time, improving perceived latency. Aider takes a different approach: it sends batch requests to reasoning models, waits for the full response, then applies code changes. This architectural choice explains its lower streaming rate and higher reasoning intensity.

## Questions this answers

- Which coding agents use streaming responses?
- Why does Aider not use streaming for most calls?
- How does streaming adoption correlate with reasoning model usage?
- What percentage of Claude Code calls use streaming?

## Key findings

1. Cline, Forge, Zed, and OpenCode: 100% streaming. No batch completions at all.
2. Claude Code: 93% streaming. The 7% non-streaming calls may be health checks or metadata requests.
3. Aider: 22% streaming, 82% reasoning intensity. The only agent that primarily uses batch mode with reasoning models.
4. Zed: 100% streaming with 40% reasoning intensity. Highest reasoning use among fully-streaming agents.
5. Forge: 100% streaming but only 0.6% reasoning intensity. Minimal use of reasoning models.

## Data

| Agent | Streaming (percent) | Reasoning intensity (percent) | Cache hit rate (percent) |
| --- | --- | --- | --- |
| Cline | 100.00% | 12.07% | 61.36% |
| Forge | 100.00% | 0.62% | 63.93% |
| Zed | 100.00% | 39.83% | 80.05% |
| OpenCode | 100.00% | 21.00% | 88.98% |
| Kilo Code | 99.92% | 13.87% | 45.49% |
| Roo Code | 99.87% | 8.07% | 73.63% |
| Claude Code | 93.47% | 2.17% | 91.91% |
| Aider | 22.23% | 81.55% | 84.02% |

## Caveats

- Streaming percentage reflects request count, not token volume. A single non-streaming request may generate more tokens than multiple streaming ones.
- Reasoning intensity is computed as reasoning_tokens / output_tokens. Claude models report 0 reasoning_tokens in our pipeline even when extended thinking is active.
- GitHub Copilot and Codex CLI are excluded due to minimal traffic volume.

## Cite as

**APA.** Requesty (2026). Streaming adoption by coding agent, April 2026. Requesty Data. https://requesty.ai/data/coding-agent-streaming-adoption-apr-2026

```bibtex
@misc{requesty_coding_agent_streaming_adoption_apr_2026,
  author       = {{Requesty}},
  title        = {Streaming adoption by coding agent, April 2026},
  year         = {2026},
  howpublished = {\url{https://requesty.ai/data/coding-agent-streaming-adoption-apr-2026}},
  note         = {Requesty Data}
}
```

---

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