GLM-5.2 Temperature and Top P: Prevent Sampling Drift

Evan Brooks

AI Infrastructure Editor

Editorial responsibility: GLM52.ai Editorial Team · Published and editorially reviewed August 16, 2026

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The same numbers can select different decoding modes

Z.AI documents hosted GLM-5.2 with do_sample=true, temperature=1.0, and top_p=0.95 by default. The pinned open checkpoint also stores temperature 1 and top P 0.95—but does not store do_sample. Transformers 5.12 treats an unset sampling flag as non-sampling, so an omitted local profile selects greedy decoding instead of reproducing the hosted sampled default.

Original editorial visualization of sampling-mode drift. The branching route represents multinomial sampling; the straight route represents greedy decoding. The amber switch is do_sample, not a quality score.

Pin do_sample, temperature, and top_p together whenever you compare a hosted GLM-5.2 request with direct Transformers inference. For the current Z.AI documented baseline, the explicit sampled profile is true, 1.0, and 0.95. For an intentional greedy profile, set do_sample=false and keep the two sampling controls neutral or omit them from the request.

This is not a cosmetic configuration preference. temperature and top_p only shape token selection when sampling is active. If the mode silently changes, two systems can display the same numeric settings while executing different decoding algorithms. That invalidates a quality comparison before the first answer is scored.

Our dated audit hashes eleven public artifacts and resolves fourteen local configuration fixtures. It makes zero authenticated API calls, zero tokenizer calls, and zero model calls. The evidence proves the documented and pinned configuration path—not output quality, provider implementation, or exact reproducibility.

Navigate the sampling controls

1. Read the short verdict
2. Compare the two omitted defaults
3. Understand each control
4. Choose a complete profile
5. Review fourteen fixtures
6. Copy the validator
7. Build a hosted request
8. Build a Transformers call
9. Handle vLLM and gateways
10. Design regression evidence
11. Keep reasoning separate
12. Select task profiles
13. Roll out safely
14. Review access
15. Audit sources and limits
16. Resolve common questions

The short verdict

The current contracts resolve as follows:

Route or artifact	do_sample when omitted	temperature	top_p	Effective mode	Evidence
Z.AI hosted Chat Completion	true	1.0	0.95	sampled	Current API schema
Pinned checkpoint generation_config.json	absent	1.0	0.95	not determined by the file alone	Revision b4734de…
Transformers 5.12 with that omitted flag	None, treated as not true	1.0	0.95	greedy	Pinned mode-selection source
Explicit local evaluation profile	true	1.0	0.95	sampled	Caller-owned configuration
Explicit greedy profile	false	1.0	1.0	greedy	Caller-owned configuration

The Z.AI Chat Completion reference sets the hosted defaults and says temperature and top P do not take effect when do_sample is false. The pinned checkpoint generation config stores 1.0 and 0.95 but has no do_sample key. In Transformers 5.12 mode selection, an absent flag loads as None, and any value that is not exactly true selects greedy search for the single-beam case.

The values therefore agree while the omitted mode does not. Treat the triplet as one versioned profile rather than three unrelated optional fields.

Hosted and Transformers defaults diverge

On the hosted route, omission is a real choice: it delegates to the provider’s published true / 1.0 / 0.95 defaults. On direct Transformers inference, the model file provides only the two numeric values. The library must still decide whether it will sample, and the pinned source resolves a missing flag to greedy mode.

The official Transformers generation guide states that multinomial sampling is enabled with do_sample=True and one beam. The GLM-5.2 model README reports several evaluation profiles using temperature 1 with top P either 0.95 or 1.0. Those numbers do not activate sampling by themselves.

This distinction affects three common workflows:

• A hosted-to-local quality check may compare sampled API answers with greedy local answers and attribute the difference to weights or quantization.
• A local upgrade may begin sampling after an engine or wrapper starts adding do_sample=true, even though the visible numeric controls did not change.
• A regression harness may log only temperature and top P, leaving no evidence of the algorithm that generated its baseline.

Resolve and log the mode before interpreting any output difference.

Three controls with two different jobs

do_sample selects the decoding family. When false, a single-beam generation chooses the most likely next token at each step. When true, the system can draw from a probability distribution after the sampling filters are applied.

temperature rescales logits before sampling. Lower values concentrate probability on stronger candidates; higher values flatten the distribution. Z.AI currently documents a GLM-5.2 range from 0 through 1, inclusive, with a default of 1.0.

top_p applies nucleus sampling: it keeps the smallest candidate set whose cumulative probability reaches the threshold. Z.AI documents 0.01 through 1, with a GLM-5.2 default of 0.95. The core-parameter guide recommends 0.8–0.95 when using top P for controlled diversity.

If an evaluation needs per-token alternatives or likelihood shifts, use the GLM-5.2 logprobs contract and record whether the runtime returned raw or processed scores. Sampling controls shape a distribution; they do not make its scores portable or calibrated.

The same guide recommends modifying only one of temperature and top P. A useful operational interpretation is to keep the unused control neutral:

• temperature experiment: set top_p=1;
• top-P experiment: set temperature=1;
• greedy experiment: set do_sample=false; numeric sampling controls are inactive, so do not pretend they explain the output.

This single-active-knob policy is a reproducibility rule, not a claim that the API rejects two non-neutral values. The API can accept both; the problem is that their combined effect is harder to attribute.

Choose one complete sampling profile

Profile	do_sample	temperature	top_p	Use it for	Main caveat
Hosted documented baseline	true	1.0	0.95	Reproducing the current Z.AI default explicitly	Still stochastic
Direct local equivalent	true	1.0	0.95	Matching the decoding controls used by the hosted baseline	Engine kernels can still differ
Greedy regression	false	1.0	1.0	Stable structural and tool-loop tests	Not proof of byte-identical output
Temperature-only trial	true	chosen 0–1	1.0	Isolating temperature sensitivity	Temperature 0 behavior can be engine-specific
Top-P-only trial	true	1.0	chosen 0.01–1	Isolating nucleus filtering	More than one run is needed
Published evaluation reproduction	true	copy the cited value	copy the cited value	Reproducing one named benchmark	Do not generalize it to every task

The official GLM-5.2 model guide currently contains SDK examples with temperature 1.0 and one Python streaming example with 0.6. That is another reason to name the intended profile instead of treating any copied snippet as the universal model default.

What the fourteen fixtures prove

The audit resolves hosted defaults, pinned checkpoint values, caller overrides, ranges, and policy warnings without sending a generation request.

Fixture	Route	Resolved mode	Result	Evidence value
Omitted hosted fields	Z.AI	sampled, 1.0 / 0.95	pass	Published hosted baseline
Omitted checkpoint fields	Transformers	greedy, 1.0 / 0.95 ignored	IMPLICIT_GREEDY_MODE	Reproduces the drift
Explicit local sampled profile	Transformers	sampled, 1.0 / 0.95	pass	Restores control parity
Explicit hosted greedy	Z.AI	greedy	pass	Mode switch is explicit
Explicit local greedy neutral	Transformers	greedy, 1.0 / 1.0	pass	No active sampling knob
Temperature-only	hosted policy	sampled, 0.6 / 1.0	pass	One active control
Top-P-only	hosted policy	sampled, 1.0 / 0.85	pass	One active control
Both non-neutral	hosted policy	sampled, 0.6 / 0.85	warning	Attribution is ambiguous
Greedy with overrides	hosted policy	greedy	warning	Both overrides are ignored
Temperature −0.01	hosted policy	sampled	reject	Below documented range
Temperature 1.01	hosted policy	sampled	reject	Above documented range
Top P 0	hosted policy	sampled	reject	Below documented range
Top P 1.01	hosted policy	sampled	reject	Above documented range
String "true"	hosted policy	invalid	reject	Boolean type is required

The positive controls show that the validator accepts explicit sampled, greedy, temperature-only, and top-P-only profiles. The negative controls isolate four different mistakes instead of treating every unusual output as “randomness.”

Download the byte-identical machine-readable receipt to inspect every source hash, resolved profile, warning, and invariant.

Validate the profile before network or GPU work

Use a fail-closed request gate. This compact version checks the documented ranges and the single-active-knob policy:

GLM-5.2 sampling profile validator

export function validateSamplingProfile(profile) {
  const errors = [];
  const warnings = [];
  const { do_sample, temperature, top_p } = profile;

  if (typeof do_sample !== "boolean") errors.push("INVALID_DO_SAMPLE");
  if (!Number.isFinite(temperature) || temperature < 0 || temperature > 1) {
    errors.push("TEMPERATURE_OUT_OF_RANGE");
  }
  if (!Number.isFinite(top_p) || top_p < 0.01 || top_p > 1) {
    errors.push("TOP_P_OUT_OF_RANGE");
  }

  if (do_sample === false) {
    if (temperature !== 1) warnings.push("TEMPERATURE_IGNORED");
    if (top_p !== 1) warnings.push("TOP_P_IGNORED");
  } else if (temperature !== 1 && top_p !== 1) {
    warnings.push("BOTH_NON_NEUTRAL_SAMPLERS");
  }

  return {
    accepted: errors.length === 0,
    mode: do_sample === true ? "sample" : "greedy",
    errors,
    warnings,
  };
}

Require the application to choose a named profile first. Do not silently add do_sample=true merely because non-neutral numeric values appear; that changes the decoding algorithm. Do not silently clamp out-of-range values either. Return a configuration error before network I/O or GPU allocation.

Pin the Z.AI request

An explicit hosted baseline avoids future ambiguity:

Explicit Z.AI sampled baseline

{
  "model": "glm-5.2",
  "messages": [{ "role": "user", "content": "Review this patch." }],
  "do_sample": true,
  "temperature": 1.0,
  "top_p": 0.95,
  "max_tokens": 4096
}

For a greedy contract test:

Explicit hosted greedy profile

{
  "model": "glm-5.2",
  "messages": [{ "role": "user", "content": "Return the required fields." }],
  "do_sample": false,
  "max_tokens": 1024
}

The API documentation says temperature and top P have no effect in the second request. Omitting them communicates that intent more honestly. Keep the output cap and total context as separate gates; sampling controls do not expand either limit.

Pin direct Transformers generation

Direct local inference must activate sampling explicitly if it is meant to match the hosted sampled profile:

Explicit direct-Transformers profile

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

output = model.generate(
    inputs,
    do_sample=True,
    temperature=1.0,
    top_p=0.95,
    max_new_tokens=4096,
)

For greedy decoding, set do_sample=False and either leave the sampling-only arguments unset or neutralize them. Pin the model revision and Transformers version in the same run record. A configuration copied from a newer engine is not evidence for the pinned 5.12 behavior.

The full checkpoint is far beyond a casual workstation. Before downloading or allocating GPUs, use the local hardware guide to check weight, KV-cache, storage, and interconnect requirements. The sampling validator itself needs no model weights.

Do not inherit a server’s unknown default

An OpenAI-compatible vLLM, SGLang, gateway, or agent client may translate or omit fields differently from both Z.AI and direct Transformers. The current official vLLM GLM-5.2 recipe pins engine and serving controls, but its example request does not pin temperature, top P, or do_sample.

Treat each adapter as a separate contract:

1. capture the application payload;
2. capture the translated server payload or resolved generation config;
3. record the engine and image version;
4. verify the selected mode in a bounded canary;
5. compare outputs only after those facts match.

Do not infer an engine’s behavior from “OpenAI compatible.” Compatibility can cover field names without guaranteeing identical defaults. The API provider comparison helps separate access routes; it does not replace a route-specific receipt. Keep repetition controls in a separate profile: the GLM-5.2 repetition-penalty guide shows why Z.AI, Transformers, vLLM, and SGLang do not share one portable penalty payload.

Deterministic does not mean byte-identical

do_sample=false removes multinomial token selection in the documented path. It does not prove identical bytes across GPU types, kernels, quantizations, batch sizes, engine releases, or distributed execution. Floating-point ties and implementation changes can still alter a sequence.

The current Z.AI Chat Completion schema does not document a seed request field. That absence does not prove every proxy rejects a seed extension, but it means a portable GLM-5.2 test should not depend on one.

Build regression evidence around outcomes:

• validate JSON or tool arguments against a schema;
• assert required facts, files, or tests rather than exact prose;
• run stochastic profiles more than once and report pass rate;
• store finish_reason, token usage, route, model revision, engine version, and the complete sampling profile;
• keep prompts, fixtures, and graders versioned.

For machine-consumed results, pair a conservative profile with the structured-output validation pattern. For tools, validate arguments and execute them through the complete function-calling loop.

Sampling and reasoning effort are independent controls

Sampling controls token selection. reasoning_effort controls how much reasoning work the model is asked to perform. One does not neutralize or replace the other.

A comparison can hold do_sample, temperature, and top P constant while changing reasoning effort, or hold reasoning effort constant while testing one sampling knob. Changing both groups at once makes attribution weak. Use the reasoning-effort guide to pin the accepted effort value and output budget independently.

Streaming is also orthogonal. It changes delivery, not the chosen sampling profile. Preserve the same fields when switching to SSE, then use the streaming parser to reconstruct reasoning, content, usage, tool calls, and terminal state.

Match the profile to the reader task

Reader task	Starting profile	Acceptance evidence
JSON extraction	explicit greedy	schema pass rate, missing-field rate
Tool routing	explicit greedy first	valid tool name and arguments across fixtures
Coding benchmark reproduction	exact cited profile	harness, revision, pass rate, time and token budget
Brainstorming	top-P-only or temperature-only	diversity plus human usefulness rubric
Factual Q&A	lower-temperature trial with top P neutral	source-grounded correctness, not fluency
Hosted-to-local parity	explicit 1.0 / 0.95 sampled profile	repeated shared-fixture comparison

These are starting points, not universal quality claims. A task can perform better with a different profile. Change one dimension, preserve the baseline, and measure the task outcome that matters.

Log the resolved profile, not only the payload

At rollout time, store both requested and resolved state:

Minimum sampling receipt

{
  "route": "direct-transformers",
  "model_revision": "b4734de4facf877f85769a911abafc5283eab3d9",
  "engine": "transformers-5.12.0",
  "requested": { "do_sample": true, "temperature": 1.0, "top_p": 0.95 },
  "resolved_mode": "sample",
  "reasoning_effort": "max",
  "fixture_set": "sampling-canary-v1"
}

Canary the new profile before sending production traffic. Roll back when schema pass rate, task success, latency, token use, or retry rate crosses its bound. Do not attribute a regression to sampling until model revision, prompt, reasoning controls, context, tools, and output cap are also held constant.

Choose access after the profile is explicit

Once the payload contract and acceptance test are ready, choose the delivery route. Hosted access removes checkpoint serving work; self-hosting gives more control but makes engine defaults, GPU capacity, and observability your responsibility.

After the sampling profile passes

Review official GLM-5.2 access

Z.AI publishes the hosted GLM-5.2 parameter contract used in this audit. Confirm the exact product, route, current price, quota, retention terms, and supported request fields before purchase or production use; documentation defaults are not an account-specific admission receipt.

Check GLM access on Z.ai →

Affiliate disclosure: This is an affiliate link and a referral link. GLM52.ai may receive compensation after a qualifying purchase at no additional cost from us. That relationship does not change the source hashes, hosted-default extraction, checkpoint finding, fourteen fixture outcomes, or zero-call boundary.

Sources, method, and limits

Checked on August 16, 2026 (Hong Kong time). The audit fetched the Z.AI core parameters, Chat Completion schema, and GLM-5.2 model guide; the current Hugging Face model API; pinned checkpoint generation config and README; Transformers 5.12 generation source and strategy guide; the pinned vLLM recipe; the required Z.AI release entry; and the Zhipu research index. Redirects, compression, source byte counts, and SHA-256 hashes are recorded in the public receipt.

The foreground round began with an AI HOT lead about AI-generated Amazon books. It was rejected as a weak GLM link and was not used as evidence. The topic came from proactive official-doc and checkpoint research. One exact Google result set for glm 5.2 temperature top_p do_sample was recorded under request serpapi-6b6d88585c14422ca0fd923fb0097fef; it found official and adjacent guidance but no page centered on the fail-closed hosted-to-Transformers drift test.

The fixtures are deterministic JavaScript configuration checks. They do not prove output quality, account entitlement, server-side defaults beyond the published schema, bit-for-bit determinism, latency, or route acceptance. No GLM model, grader, authenticated endpoint, credential, or private prompt was used. Re-run the audit when the API schema, checkpoint revision, Transformers version, or serving engine changes.

GLM-5.2 temperature and top P FAQ

What are the documented GLM-5.2 temperature and top_p defaults?

For the current Z.AI Chat Completion text-model schema, temperature defaults to 1.0 and top P defaults to 0.95. The schema also defaults do_sample to true. Pin all three when a baseline must survive documentation or client changes.

Does temperature 0 make GLM-5.2 deterministic?

Z.AI documents 0 as an accepted temperature boundary, but exact behavior can depend on the engine. For an explicit greedy test, use do_sample=false. Neither choice guarantees identical bytes across different hardware and software stacks.

Should I change temperature and top_p together?

The official core-parameter guide recommends changing only one. Keep the other neutral at 1.0 so the experiment has one active stochastic control.

Why is my local output less varied than the Z.AI API?

Check do_sample first. The pinned checkpoint stores temperature 1 and top P 0.95 but omits the sampling flag; Transformers 5.12 treats the missing flag as greedy mode. Set do_sample=true explicitly before comparing with the hosted sampled baseline.

Does GLM-5.2 support a seed parameter?

The checked Z.AI Chat Completion schema does not document one. A gateway may define an extension, but do not assume it is portable or that a shared seed produces identical output across engines.

Does reasoning_effort replace temperature?

No. Reasoning effort controls requested reasoning depth; sampling controls how the next token is selected. Pin and evaluate the two groups separately.

About the editorial role

Evan Brooks

AI Infrastructure Editor

Evan Brooks serves as GLM52.ai’s disclosed editorial pen name for infrastructure, deployment, and training guides. The role examines checkpoint size, memory math, provider limits, implementation evidence, and operational risk. The GLM52.ai Editorial Team produces and fact-checks every article under this byline.

The GLM52.ai Editorial Team accepts responsibility for this article. Read our editorial policy or request a correction.

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