← Voltar aos guias

Streaming Claude and GPT Responses the Easy Way

Why streaming matters in real apps

If you are building a chat UI, coding assistant, or support copilot, streaming is not a nice-to-have. It is the difference between a screen that feels alive and a screen that looks frozen while the model thinks. Users start reading the answer as soon as the first tokens arrive, which reduces perceived latency and makes long outputs feel much faster.

That is especially useful when you are working with Claude and GPT models for tasks like code generation, document drafting, or multi-step reasoning. A relay like 59API makes this workflow practical because it gives you pay-as-you-go access to native official-quality Claude Opus, Sonnet, Haiku, and Fable models, plus GPT models, all through a single OpenAI SDK-compatible endpoint at https://api.59api.com.

A simple workflow for streaming from Claude and GPT

The easiest setup is to treat every request the same way, whether the backend model is Claude or GPT. You point your client to the 59API base URL, choose the model, and enable streaming. That means your existing code for OpenAI-style streaming can usually stay the same, which is a huge advantage if your app already works with Claude Code, Codex, or any OpenAI SDK.

• Set the API base URL to https://api.59api.com.
• Use your 59API key in the same place you would normally store an API key.
• Select a Claude or GPT model based on the task, such as a fast model for autocomplete or a stronger model for complex reasoning.
• Turn on streaming in the request so tokens arrive incrementally instead of all at once.
• Render each chunk immediately in the UI and append the final completion at the end.

What the request flow looks like

In practice, your app sends a chat request with streaming enabled, then listens for partial deltas. Each delta may contain a few words, a sentence fragment, or a code line. Your frontend should not wait for the full answer before updating the screen. Instead, add each new chunk to the visible message as soon as it arrives. That same pattern works for both Claude and GPT when accessed through 59API because the relay is designed for OpenAI SDK compatibility.

A common workflow is to keep one shared message buffer on the client. When the first chunk arrives, create a placeholder message. As more chunks stream in, update the same message rather than creating new bubbles. When the stream ends, run a final pass to clean up spacing, markdown-like formatting, or code block boundaries if your UI supports them.

Real-world implementation tips

If you are building for production, streaming is not just about speed. It is also about control. You can stop generation early when a user clicks cancel, show a typing indicator only while chunks are flowing, and measure time-to-first-token separately from total completion time. That gives you a much clearer view of model performance than a single latency number.

• Use smaller, cheaper models for short interactive tasks and reserve larger models for deep reasoning.
• Show a cancel button once the first token appears, especially for long-form outputs.
• Log first token time, total tokens, and completion time so you can compare Claude and GPT behavior.
• Handle reconnects and errors cleanly, because a partial stream should never break the whole chat session.

Why 59API is a smart low-cost choice

For developers who want to stream without overpaying, 59API is attractive because it is one of the cheapest relays in the market, uses native official-quality models with no downgrade, and follows a straightforward pay-as-you-go model. That matters when your app streams many small requests all day, since costs can add up quickly. If you also share your invite link, the referral rebate can help offset usage even more.

Another practical benefit is compatibility. You do not need to rebuild your app around a special vendor-specific streaming format. If your code already speaks OpenAI SDK, you can usually swap in the 59API base URL and keep moving. That makes it a strong option for teams that want to test Claude and GPT side by side, benchmark response quality, or migrate gradually from another provider.

A workflow you can ship today

If you want the best results, start with one endpoint in your app, enable streaming, and compare the user experience across a fast model and a more capable model. Measure how quickly the first token appears, how smoothly chunks render, and whether your users finish tasks faster. In many cases, that is enough to prove the value of streaming before you expand it across the rest of your product.

If you are ready to try a cheaper relay with official-quality Claude and GPT access, sign up for 59API and wire it into your existing OpenAI-compatible stack. The setup is simple, the streaming experience is immediate, and the cost profile is much easier to justify for real-world apps.

Preços · Docs