GLM-5 API Quick Start on WaveSpeed (Code Examples)

Hello, I’m Dora. I bumped into GLM-5 ​while shuffling through model options for a small content-generation feature I was prototyping in January 2026. I’d heard the name in passing, solid performance, sensible architecture, but what I wanted was simple: could I slot it into an existing workflow without a week of plumbing? This piece is exactly that: a calm, hands-on tour of the​ ​GLM-5 API from the moment you get credentials to the point you think about attaching it to an image or video pipeline. I’ll show commands, point out where I hesitated, and note the trade-offs I ran into so you can decide if it fits your way of working.

Prerequisites — WaveSpeed account + API key

Before you write a single curl line, there’s one quiet step: an account and an API key. I set mine up on WaveSpeed: the flow is straightforward but pay attention to two small details.

First, get a key that’s scoped for the GLM-5 ​endpoints. There’s sometimes a separate token or role for higher-throughput models, and using the wrong key will give you a terse “model not found” error that looks like something else, which annoyed me for ten minutes until I checked that. Second, note the region/endpoint listed on the dashboard. Some accounts map models to regional endpoints, and that matters for latency if you’re doing video or interactive features.

Practical checklist I used:

• Create WaveSpeed account and verify email.
• Create an API key labeled for dev/testing.
• Confirm the GLM-5 model appears in the dashboard and note the listed endpoint region.
• Put the key in a local .env file rather than pasting it into testing scripts (least friction for later).

That’s it. No special hardware or SDK purchases. Just an API key and the patience to check the endpoint mapping.

First request in 3 steps (curl + Python + JS)

I like starting with a curl request, it’s honest and exposes headers, status codes, and raw JSON without abstractions. After that, I move to Python for experimentation and JS when I want to prototype a tiny UI.

Model ID and endpoint

The GLM-5 API ​expects a model ID and an endpoint URL. In my tests the model ID looked like glm-5-v1 (double-check your dashboard: names can vary by release). The endpoint is the host you POST to: for me it was region-prefixed. Getting either wrong gives an immediate 404 or a model-not-found JSON error.

A minimal curl example I ran (adapt to your key and endpoint):

curl -X POST "https://your-region.api.wavespeed/v1/models/glm-5-v1/generate" \
-H "Authorization: Bearer $WAVESPEED_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt":"Write a short intro about mindful workflows.","max_tokens":120}'

It returned a small JSON with text and token metadata. Clean, immediate feedback.

Streaming vs non-streaming

GLM-5 supports both streaming and non-streaming responses. I started non-streaming to keep things simple, then switched to streaming for a tiny editor prototype. Streaming saves perceived latency: text appears as it’s generated, which helps with interactivity. But streaming adds complexity, connection handling, partial results, and a bit of state management on your side.

When I used streaming in a local demo (Node.js, EventSource-style), I noticed two behaviors:

• The first token arrived quickly, which feels responsive.
• Occasionally a partial chunk would arrive with a small formatting quirk (cut mid-sentence). It was trivial to handle but worth knowing.

If you care about immediate user feedback, chat UIs, live assistants, start with streaming. For batch generation or simple scripts, non-streaming is simpler and less error-prone.

Key parameters: thinking mode, temperature, max tokens

Three parameters shaped my experience more than any other: thinking mode, temperature, and max tokens. I tuned them across several short experiments.

Thinking mode

GLM-5 exposes a “thinking mode” parameter that nudges how the model reasons about a prompt. Think of it as a loose instruction set: inexpensive modes prioritize speed and brevity: heavier modes prioritize depth and multi-step reasoning. I used the faster mode for short marketing blurbs and a deeper mode when I asked the model to outline a multi-part tutorial.

My takeaway: don’t treat thinking mode as magic. It changes the model’s approach, but you still need to structure prompts when you need multi-step outputs.

Temperature

Temperature controls randomness. I ran the same prompt with 0.0, 0.3, and 0.8. At 0.0 the outputs were consistent and safe, useful for templates and code generation. At 0.8 the model offered more creative turns, sometimes producing helpful phrasings, sometimes drifting into fluff.

Practical rule I used: start at 0.2–0.4 for production text, 0.0 for deterministic tasks (like SQL), and 0.6–0.8 for ideation.

Max tokens

Max tokens limits output length. I found that GLM-5 gives a predictable token count in responses. When I set max_tokens too low, the model would cut off mid-thought, frustrating when composing bullet-point outlines. When unsure, I over-provision and then trim client-side. A small heuristic I used: estimate words × 1.3 = tokens, then add 10% buffer.

Error handling — rate limits, model not found, timeouts

Errors are where you’ll learn the shape of a platform.

Rate limits

WaveSpeed returns clear rate-limit headers and an HTTP 429. In my prototype, I hit 429s while running concurrent tests from two machines. I handled it by implementing an exponential backoff with jitter and queuing requests client-side. That removed most 429s. If your app queues user requests, surface a gentle “processing” state rather than showing an error.

Model not found

This one’s a common false alarm. It can mean a mis-typed model ID, a key without permission for that model, or the model being unavailable in your region. My checklist when I saw this:

• Confirm model ID matches the dashboard exactly.
• Check that the API key has the right scope/role.
• Try another regional endpoint if available.

Timeouts

For long generations or heavier thinking modes I saw occasional timeouts. My approach was conservative: increase server-side timeouts for specific routes that call the GLM-5 API and provide a progress UI if streaming is possible. If you can break a task into smaller steps (generate outline → expand sections), you reduce timeout risk and get more manageable failures.

Logging and observability

I log request IDs from successful and failed responses. That made it much easier to debug with support later.

Cost estimation — tokens per request

Cost matters. I ran a small experiment across four days in January 2026 to estimate per-request token usage for a content feature that generated 400–800 words per request.

What I measured

• Prompt tokens: typically 40–120 depending on context size.
• Completion tokens: for a 600-word output I saw ~750 tokens (different models have slightly different tokenization). Total per request averaged 820–900 tokens.

A quick way I calculated costs:

1. Track prompt + completion tokens from the response metadata.
2. Average those across 30 requests for a given use case.
3. Multiply by the model’s token price (check your WaveSpeed dashboard for current rates).

Things that surprised me

• System prompts and long conversation histories add up fast. If you stash chat history, prune it aggressively.
• Repeated retries during development skewed my numbers: I recommend using a separate dev key and watching token headers closely.

If you want a rough figure: for short copy generation (100–200 words), expect 150–300 tokens per request. For long-form (500–800 words), expect 600–900 tokens. Your mileage will vary, so measure with your actual prompts.

Next steps — integrate into your image/video pipeline

I didn’t stop at text. The obvious question for me was how GLM-5 fits a media pipeline: captions, scene descriptions, video scripts, or metadata enrichment.

A few practical patterns I tried

• Captioning assistant: Send short scene descriptions and ask GLM-5 for concise captions. Keep prompts rigid and temperature low for consistent phrasing.
• Script expansion: Use GLM-5 to expand a bulleted outline into a short script. I split the outline into per-scene requests to avoid long completions and to parallelize generation.
• Metadata tagging: For automated tagging of clips, I used a deterministic mode and a small JSON schema prompt so the model returned predictable key/value pairs.

Integration tips

• If you include extracted frames or thumbnails, send them to your image model first, extract a short caption (3–6 words), then use that caption as context for GLM-5. It reduces prompt size and keeps tokens lower.
• Batch requests where you can: send multiple short tasks in parallel rather than one long prompt. It’s often cheaper and faster.
• Add a human-in-the-loop for final edits. For creators and marketers juggling platforms, the save comes from reduced slog, not perfect outputs.

Who this fits, and who it doesn’t

GLM-5 is solid if you want a flexible text model you can control: deterministic tasks, content expansion, and metadata generation. It’s less appealing if you need ultra-cheap ad-hoc outputs at massive scale without token monitoring.

If you’re curious, test it in a sandboxed feature with real prompts and measure tokens and latency. For me, the model found a quiet place in a small content feature: not flashy, but it cut steps and left the rest of my workflow intact.

A small lingering thought: I keep wanting an official endpoint health page with per-region latency numbers. If you build real-time UI, that visibility makes a difference. For now, a few quick regional pings and token logging will do the job.