GLM 4.6 Temperature: The 1.0 Default and When to Change It

By Harry | Published: November 11, 2026

For most work, leave the GLM 4.6 temperature at 1.0 and keep top_p at 0.95. Z.ai’s API reference lists that pair as the default for the GLM-4.6 series, and the official model card recommends 1.0 for general use. Drop the temperature only when you want steadier, more literal answers, and change temperature or top_p, not both at once.

That covers the quick answer. Below you’ll find the exact ranges, how the two settings interact, starting values by task, and what changes if you self-host the open weights or move to a newer GLM model.

Key Takeaways

• Z.ai’s API defaults GLM-4.6 to temperature 1.0 and top_p 0.95, and the model card recommends 1.0 for general evaluations.
• The documented temperature range is 0.0 to 1.0, so values like 1.2 copied from OpenAI code fall outside it.
• Z.ai advises adjusting temperature or top_p, not both. Pick one knob and leave the other at its default.
• For code tasks on self-hosted weights, the model card adds top_p 0.95 and top_k 40.
• Setting do_sample to false switches sampling off, which means temperature and top_p stop having any effect.

GLM 4.6 Temperature: The Official Numbers

Here’s what the official sources actually say, as of November 2026. Each value comes from either the Z.ai chat completion API reference or the GLM-4.6 model card on Hugging Face.

The ceiling is the detail people miss. OpenAI’s Chat Completions API accepts temperatures from 0 to 2, so a script written for GPT models might send 1.3 for “creative” output. On Z.ai’s API, 1.0 is the documented maximum. If you’re porting code, clamp the value before you send it.

What Temperature Actually Changes in GLM-4.6

Every time GLM-4.6 picks its next token, it has a probability for each candidate. Temperature reshapes that distribution. Z.ai’s guide to core parameters puts it simply: lower values make the distribution “sharp,” so output gets more deterministic and conservative, while higher values flatten it and produce more varied text.

At 1.0, the model samples from its distribution as trained, with no extra sharpening. That’s why Z.ai uses it as the default and recommends it for evaluations. It’s the setting the published scores reflect.

Top_p works differently. It’s nucleus sampling: the model keeps only the smallest set of tokens whose combined probability reaches the top_p value, then samples from that set. At 0.95, the long tail of unlikely tokens gets cut while most of the model’s range stays available.

How Temperature and Top_p Interact

Both settings narrow or widen the model’s choices, just by different methods. So stacking them makes it hard to tell which one caused a change. Z.ai’s GLM-4.6 migration guide says it directly: temperature defaults to 1.0, top_p defaults to 0.95, and adjusting both at the same time isn’t recommended.

In practice, that gives you two clean paths:

• Tune temperature: lower it (say, to 0.6) and leave top_p at 0.95.
• Tune top_p: keep temperature at 1.0 and lower top_p. Z.ai’s parameter guide suggests 0.8 to 0.95 as a range that balances diversity and quality.

Which should you pick? Temperature is the more familiar knob, and most tutorials talk about it. But if your outputs occasionally drift into odd word choices while otherwise sounding fine, trimming top_p targets that tail more directly. Change one, compare on the same prompts, then decide.

Starting Settings by Task

The official docs give two firm recommendations (1.0 for general use, plus top_p 0.95 and top_k 40 for code) and a general rule that rigorous, factual tasks suit lower temperatures. The rows below combine those into editorial starting points. They aren’t Z.ai figures, so treat them as values to test, not rules.

Say you’re building a support bot that pulls order details into JSON. Variety doesn’t help you there, so a lower temperature makes sense. For an AI coding assistant built on GLM-4.6, though, the model card’s 1.0 setting is the better starting point, since that’s what Z.ai itself recommends for code tasks.

Thinking Mode and Low Temperatures

GLM-4.6 has deep thinking turned on by default, and the API reference says the model decides on its own whether to think for a given request. Z.ai doesn’t publish guidance on how low temperatures affect the reasoning trace. Its own recommendation stays at 1.0.

So if you plan to run thinking mode at, say, 0.2, compare a batch of real prompts at both settings before you ship it. Watch answer quality and response length, because Z.ai’s migration guide flags latency and cost as things to check in deep thinking scenarios.

Setting Temperature in a GLM-4.6 API Call

Z.ai’s general endpoint is https://api.z.ai/api/paas/v4/chat/completions, with your key sent as a Bearer token. A request that lowers temperature for a factual task looks like this:

{
  "model": "glm-4.6",
  "messages": [
    {"role": "user", "content": "Extract the invoice total and due date as JSON."}
  ],
  "temperature": 0.4,
  "max_tokens": 1024
}

Notice that top_p isn’t in the request. Leaving it out keeps the 0.95 default, which follows Z.ai’s one-knob advice. If you’d rather tune top_p, drop the temperature line and add "top_p": 0.85 instead.

Self-Hosting and Third-Party Providers

GLM-4.6 ships as open weights under the MIT license, so you can also run it yourself or reach it through other hosts. The 1.0 and 0.95 defaults above are documented for Z.ai’s own API. Elsewhere, the serving stack decides. The model repo’s generation_config.json sets temperature to 1.0, and servers that read that file will start there.

Routers and multi-model platforms may apply their own defaults, so the safest habit is to send temperature and top_p explicitly in every request. If you’re self-hosting for code, add top_k 40 as the model card suggests. Our comparison of large open-weight models covers other options in the same size class.

Is GLM-4.6 Still Z.ai’s Latest Model?

No. According to Z.ai’s release notes, GLM-4.6 launched on September 30, 2025, with a context window expanded to 200K tokens. Newer models followed: GLM-4.7 (December 22, 2025), GLM-5 (February 12, 2026), GLM-5.1 (April 7, 2026), GLM-5.2 (June 16, 2026), and GLM-5.3 (August 18, 2026).

The good news is that your temperature settings carry over. The API reference lists the same defaults, temperature 1.0 and top_p 0.95, for every text series from GLM-4.6 through GLM-5.3. One behavior does differ: GLM-4.7 always thinks when thinking is enabled, while GLM-4.6 decides per request. The release notes don’t mark GLM-4.6 as deprecated, and it’s still listed as a model option in the API reference.

If you’re weighing an upgrade, our GLM-5.1 vs Claude Opus 4.6 breakdown shows how a later GLM release compares with a frontier rival.

Which GLM 4.6 Temperature Should You Use?

Start with the GLM 4.6 temperature at 1.0 and top_p at 0.95. That’s the official default, the evaluation setting, and the code recommendation all in one. Move temperature down toward 0.3 to 0.6 only for extraction, classification, or strict factual answers, and leave top_p alone when you do.

Never go above 1.0, since that’s outside the documented range. And if you need repeatable output for testing, set do_sample to false instead of hunting for a magic temperature. Then test any change on your own prompts, because the official docs give direction, not task-by-task numbers.

FAQ

Can GLM-4.6 temperature go above 1.0?

No, Z.ai documents the temperature range for GLM-4.6 as 0.0 to 1.0. That’s narrower than OpenAI’s 0 to 2 scale, so a value like 1.5 from older code won’t map over. If you want more variety than 1.0 gives you, raising top_p toward 1.0 is the remaining lever.

Does temperature 0 make GLM-4.6 deterministic?

Not necessarily, and Z.ai doesn’t promise that it does. The documented range includes 0.0, but the cleaner option is setting do_sample to false, which the API reference says turns sampling off so temperature and top_p no longer apply. Even then, verify repeatability on your own requests before relying on it.

What top_k should I use with GLM-4.6?

The model card recommends top_k 40, paired with top_p 0.95, for code-related tasks. That advice matters most when you self-host the weights with a serving stack that exposes top_k. For general tasks, the card only recommends a temperature of 1.0 and doesn’t give a top_k value.

Are GLM-4.6 defaults the same on OpenRouter or other providers?

Not guaranteed. The 1.0 temperature and 0.95 top_p defaults are documented for Z.ai’s own API, and each third-party host controls its own serving setup. To get consistent behavior across providers, pass both values explicitly in every request instead of trusting whatever default the platform applies.

How long can GLM-4.6 responses be?

Z.ai lists a 200K-token context window and a 128K-token maximum output for GLM-4.6. The temperature setting doesn’t change those limits. The migration guide does suggest setting max_tokens deliberately, since long outputs combined with deep thinking can raise both latency and cost.

Harry covers artificial intelligence for AI Journal Now.

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