TL;DR

• GLM 5.3 is Zhipu AI's (Z.ai's) new flagship LLM, announced in August 2026. It keeps the GLM 5.2 base; all gains come from post-training.
• Confirmed specs: 1M-token context, 128K max output, text in / text out, thinking always enabled (low / high / max).
• Official API status: the Zhipu/BigModel API is "coming soon" — no firm date or official pricing yet; you can test glm-5.3 today via the OpenAI-compatible endpoint at https://glm5.app/api/v1.
• Z.ai-reported highlights (vendor-reported, harness-specific): Terminal-Bench 3.0 at 28.3 (vs 4.6 for GLM 5.2), DeepSWE v1.1 at 66.9 (vs 46.2), CyberGym at 84.5 (vs 77.2).

Glm5.3 API

You read the GLM 5.3 announcement, stared at the benchmark charts, and want it in your stack. Then you hit the wall: the official model API is "coming soon," there's no release date, and the model ID keeps being spelled three different ways across posts. Which endpoint, which parameters, does it support vision, what will it cost?

This guide answers those questions with what's actually confirmed — built from the official Zhipu/BigModel GLM-5.3 docs, the Z.ai technical report, the zai-org/GLM-5 GitHub repository, and the live glm5.app API and pricing pages, verified August 18, 2026.

What is the GLM 5.3 API?

GLM 5.3 is Zhipu AI's (Z.ai's) new flagship LLM, optimized for complex software engineering, long-horizon agent tasks, and security review. It uses the GLM 5.2 base — all improvements come from post-training.

The GLM 5.3 API is the programmatic interface to that model: text in, text out, over a Chat Completions-style endpoint. Z.ai has already rolled GLM 5.3 out through the GLM Coding Plan and its ZCode agent, but the official Zhipu platform API is not live yet — the official GLM-5.3 model docs say it "will go live shortly." The confirmed identifier across surfaces: glm-5.3.

Confirmed GLM 5.3 specs and parameters

The verified specification table below comes from the official model documentation; anything not listed here (like vision) is not documented for this model.

Two specs stand out.

Thinking is always on. GLM 5.3 does not let you disable reasoning. You pick an effort level — low, high, or max (max is the default). If your app sends thinking.type: "disabled", switch it to enabled with reasoning_effort low before changing the model ID to glm-5.3; otherwise the request fails:

{
  "model": "glm-5.3",
  "thinking": { "type": "enabled" },
  "reasoning_effort": "max"
}

It is text-only. No vision or multimodal input is documented, so plan image or video inputs against a different model in the GLM family.

GLM 5.3 benchmarks: a starting point, not a guarantee

Z.ai publishes the numbers below in its official technical report. A score only applies to the published harness, model version, and date — treat them as directional.

Two takeaways matter. First, gains are largest on long-horizon engineering benchmarks (Terminal-Bench, DeepSWE). Second, Z.ai reports higher accuracy with fewer output tokens — on its internal Z.ai Code Bench at max effort, GLM 5.3 scores 34.5% at roughly 75K output tokens per task versus GLM 5.2's 23.4% at roughly 96K. Vendor-reported, so budget your own evaluation.

How to call the GLM 5.3 API today

While the official Zhipu API waits for launch, glm5.app already serves glm-5.3 through an OpenAI-compatible Chat Completions endpoint. With the OpenAI SDK, keep your client and change three things: API key, base URL, model name.

Step 1 — Create an API key. Sign in to glm5.app, open API Keys, and generate a key. The full key is shown once — store it in a secret manager; never put it in browser code, public repos, or client-side code.

Step 2 — Point your client at the endpoint. Base URL https://glm5.app/api/v1, model glm-5.3.

Step 3 — Make your first request.

curl https://glm5.app/api/v1/chat/completions \
  -H "Authorization: Bearer $GLM5_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.3",
    "messages": [
      {
        "role": "system",
        "content": "You are a concise technical assistant."
      },
      {
        "role": "user",
        "content": "Plan this refactor and list the rollback steps."
      }
    ],
    "max_completion_tokens": 1024
  }'

Read the response from choices and token counts from usage; handle data: [DONE] if you enable stream: true.

Step 4 — Keep context costs predictable. Chat Completions is stateless: every request is billed for all the messages you send. Keep a bounded history, summarize older turns, and monitor usage.prompt_tokens / usage.completion_tokens.

Want to evaluate the model before you build? Try GLM 5.3 free on glm5.app, then move to the API once it fits your workflow.

GLM 5.3 API pricing

Official Zhipu API pricing for GLM 5.3 has not been announced. On glm5.app, GLM 5.3 is priced in credits on a shared balance: 78 credits per 1M input tokens and 245 credits per 1M output tokens, where 100 credits equal $1.80 of API usage (about $1.40 input / $4.40 output per 1M). Chat, image, and video tools draw from the same pool. Confirm current rates on the pricing page before scaling.

GLM 5.3 vs GLM 5.2: what actually changed

Because both models share the same base, the comparison is short:

• Same architecture and context. GLM 5.3 keeps GLM 5.2's 1M-token context and open-source heritage — GLM 5.2 weights are on Hugging Face; the GLM 5 series sits at 744B parameters (40B active).
• Much larger long-horizon post-training. Z.ai says it added "tens of times" more long-horizon training across real compute clusters, storage, docs, and codebases.
• About 50% better coding feel in Z.ai's own reporting, plus better token efficiency.
• Emergent security capability. The largest relative gains are in exploit-chain benchmarks: ExploitBench at 54.4 vs 24.4 for GLM 5.2, per the technical report.

If you're already on GLM 5.2, migration is a model ID change plus the thinking-parameter update. From an older model, budget a real evaluation window — differences show up on long-horizon tasks.

Glm5.3 API FAQ

When is the GLM 5.3 API release date? The model page and benchmarks arrived in August 2026, and the GLM Coding Plan already serves it. The official model API is expected "shortly," but no firm date is published. glm5.app exposes glm-5.3 via an OpenAI-compatible endpoint today.

What model ID do I use for the GLM 5.3 API? glm-5.3. Variants like glm5.3 and GLM-5.3 refer to the same model; the documented identifier is glm-5.3.

Does GLM 5.3 support vision or images? No vision input is documented. GLM 5.3 is text-in/text-out; pair it with a vision model in the GLM family for multimodal workloads.

Is GLM 5.3 free? Free ways to try it exist — glm5.app offers free chat credits, and the GLM Coding Plan covers it inside coding tools. The API itself is paid via credits (78 per 1M input, 245 per 1M output on glm5.app, subject to change).

Are the GLM 5.3 weights open source? Not yet. Z.ai open-sourced GLM 5.2 on Hugging Face, and open weights for GLM 5.3 are widely expected, but none were published as of August 18, 2026.

Bottom line

The GLM 5.3 API is a story in two parts: the official Zhipu API is still rolling out, but the model is already reachable through an OpenAI-compatible endpoint with one confirmed model ID (glm-5.3). The confirmed specs — 1M-token context, 128K max output, always-on thinking — are enough to plan an integration.

Your smallest next step: chat with GLM 5.3 for free, then bring the working prompt patterns to your API integration.

Sources

• BigModel GLM-5.3 model documentation — Official specs, parameters, benchmarks, and API status.
• Z.ai GLM-5.3 technical report — Z.ai's published benchmark and security-evaluation results.
• Z.ai GLM-5.2 launch blog — GLM-5.2 base model and 1M-token context background.
• zai-org/GLM-5 GitHub repository — GLM 5 series weights, architecture, and licensing context.
• glm5.app API quick start — Live endpoint, model IDs, and billing details for glm-5.3.

Note: benchmark figures are vendor-reported and apply only to the published harness and date; official Zhipu API pricing and a firm API launch date were still pending as of August 18, 2026.

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