Streaming lets AI models deliver responses token-by-token in real time, instead of waiting for the entire response to complete. These 295 models support streaming output — essential for chatbots, real-time UIs, and progressive rendering.
These 216 models support both streaming and function calling — the combination required for agentic workflows where tool calls stream in real time.
| # | Model | Score |
|---|---|---|
| 1 | GPT-5.2 ProOpenAI | 90 |
| 2 | GPT-5 ProOpenAI | 90 |
| 3 | o3 ProOpenAI | 82 |
| 4 | Claude Opus 4.1Anthropic | 81 |
| 5 | Claude Opus 4Anthropic | 76 |
| 6 | o3 Deep ResearchOpenAI | 74 |
| 7 | Claude Opus 4.6Anthropic | 71 |
| 8 | Claude Opus 4.5Anthropic | 70 |
| 9 | Claude Sonnet 4.5Anthropic | 69 |
| 10 | Qwen3 VL 30B A3B ThinkingAlibaba | 69 |
| 11 | Qwen3 VL 235B A22B ThinkingAlibaba | 69 |
| 12 | GPT-5.2OpenAI | 68 |
| 13 | Gemini 3.1 Pro Preview Custom ToolsGoogle | 68 |
| 14 | Gemini 3.1 Pro PreviewGoogle | 68 |
| 15 | Gemini 3 Pro PreviewGoogle | 68 |
| 16 | Claude Sonnet 4.6Anthropic | 68 |
| 17 | GPT-5.1OpenAI | 67 |
| 18 | GPT-5.3-CodexOpenAI | 67 |
| 19 | GPT-5.2-CodexOpenAI | 67 |
| 20 | GPT-5OpenAI | 67 |
| 21 | Gemini 3 Flash PreviewGoogle | 66 |
| 22 | o4 Mini Deep ResearchOpenAI | 66 |
| 23 | GPT-5.1-Codex-MaxOpenAI | 66 |
| 24 | Gemini 3.1 Flash Lite PreviewGoogle | 66 |
| 25 | Gemini 2.5 ProGoogle | 66 |
Streaming is a response delivery method where the model sends output tokens incrementally as they are generated, rather than waiting for the entire completion to finish. This is implemented via Server-Sent Events (SSE) or WebSocket connections at the API level.
Without streaming, you send a prompt and wait for the full response — which can take 10-60 seconds for long outputs. With streaming, the first tokens appear in milliseconds and continue flowing in real time. The total generation time is the same, but perceived latency drops dramatically because users see output immediately.
Every modern AI chatbot uses streaming to create the characteristic “typing” effect. Without it, users would stare at a blank screen for seconds before seeing any response. Streaming makes conversations feel natural and responsive, even when the model is generating thousands of tokens.
Applications like code editors, writing assistants, and data analysis tools use streaming to progressively render output. Users can start reading, reviewing, or even editing AI-generated content before the model finishes. This is critical for UX in production applications where long wait times cause user drop-off.
For AI agents, streaming + function calling enables real-time observation of the model's decision-making process. You can see tool calls as they are emitted, execute them in parallel, and provide results back to the model — all within a single streaming session. This powers responsive agent UIs where users see each step as it happens.
Most AI APIs implement streaming via the OpenAI-compatible SSE protocol. Set stream: true in your API request and the response arrives as a series of data: events, each containing a delta with new tokens. Client libraries handle parsing and reassembly automatically.