GLM 5.2 Temperature and Sampling: How to Control Output Quality

Every token GLM 5.2 generates is drawn from a probability distribution, and temperature is the dial that reshapes that distribution before each draw. Set it near zero and the model locks onto its highest-probability answer at every step; push it above 1.0 and the sampling window widens until lower-probability tokens enter the mix. Getting this parameter right is often more impactful than any prompt rewrite.

Quick Comparison

Benchmark Performance

GLM 5.2's 89% on GPQA Diamond means the underlying reasoning is strong enough to benefit from low-temperature discipline. At temperature 0.0–0.1, the model isn't hedging toward the most probable token out of uncertainty — it's committing because the correct answer genuinely dominates its distribution. That distinction matters: a weak model made deterministic still produces wrong answers confidently; GLM 5.2 at low temperature produces correct, grounded outputs.

The 62.1% SWE-bench Pro score places it among the top performers on real-world software engineering tasks. Coding temperatures of 0.1–0.3 aren't artificially constraining the model — they unlock its strongest reasoning mode rather than suppressing creativity the task never called for in the first place.

Terminal-Bench at ~80% reflects agentic capability that is directly relevant to multi-step pipelines. Tool-calling chains require the model to stay on a planned execution path, and variance introduced at temperature 0.3+ can derail a mid-chain decision. For agent workflows, treat 0.1–0.2 as the default and raise temperature only for a final generation step, not for the planning steps.

Pricing Breakdown

Annualized cost example — 50,000 input tokens and 30,000 output tokens per request, 5,000 requests per day:

• Input cost: (50,000 × 5,000 ÷ 1,000,000) × $1.40 = $350/day
• Output cost: (30,000 × 5,000 ÷ 1,000,000) × $4.40 = $660/day
• Daily total: $1,010 — annualized: ~$368,650

Temperature does not add tokens by itself, but higher values tend to produce longer, more exploratory responses. Setting max_tokens alongside your temperature setting is the simplest guardrail against runaway output costs on creative workloads.

Context Window and Speed

GLM 5.2's 1,048,576-token (~1M) context window is one of the largest available in a production model as of mid-2026. At low temperatures, this enables deterministic long-context tasks — full codebase review, extended contract analysis, multi-document synthesis — with outputs that are both accurate and reproducible run-to-run. At higher temperatures the large context acts as a structural anchor: even at 1.0, a model reasoning over hundreds of thousands of prior tokens rarely drifts into incoherence the way a short-prompt generation can.

At 158 tokens per second, GLM 5.2 is fast enough for real-time use at any temperature setting. Sampling temperature shapes the output distribution; it does not affect inference speed.

Open Source and API Compatibility

Released under the MIT open-weight license by Zhipu AI (Z.ai), GLM 5.2 uses a Mixture-of-Experts architecture: 753B total parameters with 40B active per forward pass. MIT licensing means teams can self-host and fine-tune without royalty restrictions — an advantage over comparably capable closed models.

The API is fully OpenAI-compatible, so migrating an existing client requires only a base URL change:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_GLM5APP_KEY",
    base_url="https://glm5.app/v1",
)

# Low temperature — deterministic code generation
response = client.chat.completions.create(
    model="glm-4-flash",
    messages=[{"role": "user", "content": "Write a Python function to parse ISO 8601 dates."}],
    temperature=0.1,
    top_p=0.9,
    max_tokens=512,
)
print(response.choices[0].message.content)

# High temperature — creative brainstorming
response = client.chat.completions.create(
    model="glm-4-flash",
    messages=[{"role": "user", "content": "Generate 10 unconventional names for a note-taking app."}],
    temperature=1.0,
    top_p=0.95,
    frequency_penalty=0.3,
    max_tokens=300,
)
print(response.choices[0].message.content)

When to Choose GLM 5.2

• Graduate-level reasoning at production speed: 89% GPQA Diamond combined with 158 t/s makes it viable for demanding real-time tasks that other frontier models handle only in batch.
• Long-context pipelines: The 1M-token window accommodates full codebase ingestion or multi-document chains without chunking or retrieval workarounds.
• Self-hosting and fine-tuning: MIT licensing removes legal friction for teams running on private infrastructure or adapting the model to proprietary domains.
• Cost-sensitive high-volume deployments: At $1.40/M input and $4.40/M output, GLM 5.2 is competitively priced relative to comparable frontier models.
• Agentic and terminal workflows: ~80% Terminal-Bench reflects practical tool-use reasoning that holds across multi-step agent pipelines.
• OpenAI-compatible migration: Existing integrations can switch base URL and model name without restructuring client code or prompts.

When to Adjust Temperature

• Push temperature down when outputs feed a parser: JSON extraction, SQL generation, and any structured format should sit at 0.0–0.1 so the model does not introduce unexpected syntax variation.
• Stay below 0.3 for multi-step agent chains: Variance compounds across reasoning hops — a stochastic decision early in a chain can derail every step that follows.
• Raise temperature when diversity is the deliverable: Brainstorming and "give me 10 variations" tasks benefit from 0.9–1.2 because you want the long tail of the distribution, not the consensus answer.
• Tune frequency_penalty alongside temperature for long-form: At creative temperatures, 0.2–0.3 prevents the model from repeating phrases it committed to early in the response.
• Use presence_penalty to shift topic coverage: Combine it with mid-range temperature (0.5–0.7) for exploratory but coherent narrative that keeps introducing new angles.
• Test settings in pairs, not in isolation: A move from 0.5 to 0.8 means little without also checking top_p and max_tokens — run both configurations against the same eval before committing.

Frequently Asked Questions

Does temperature affect GLM 5.2's factual accuracy? Yes. At temperature 0.0–0.1, the model commits to its highest-probability token at each step, which correlates strongly with factual grounding. Above 0.7, the process draws from a wider distribution and can land on plausible-sounding but incorrect tokens. For knowledge-intensive tasks, stay at or below 0.3.

What is the difference between temperature and top-p? Temperature rescales the full probability distribution before sampling. Top-p (nucleus sampling) then cuts the tail by restricting the sample to the smallest token set whose cumulative probability meets the threshold. They interact: at temperature 0.1 the distribution is already heavily concentrated at the top, making top-p 0.9 and top-p 0.99 nearly identical in practice. Avoid pairing both at extreme values simultaneously.

When should I add frequency_penalty or presence_penalty? Use frequency_penalty (0.1–0.3) whenever you notice repeated phrases in long-form outputs — most common at mid-to-high temperatures. Use presence_penalty (0.1–0.3) to push the model toward mentioning new concepts rather than elaborating the ones it already covered.

Can I use temperature above 1.0? The API accepts values up to approximately 2.0. The 0.9–1.2 range is useful for maximum brainstorming divergence. Above 1.3, outputs trend toward incoherence and are rarely useful in production. If you are seeing incoherence at 1.0, the cause is usually a vague system prompt — fix the prompt before lowering temperature.

Is it better to change temperature or rewrite the prompt? They are independent levers. The prompt defines the target; temperature controls the spread around it. Fix the prompt first — clear task definition, format instructions, a few examples — then adjust temperature to calibrate precision versus creativity for that specific task.

How does the 1M context window interact with temperature at high settings? Long contexts act as a structural anchor. A model reasoning over 500K tokens of prior content rarely drifts at temperature 1.0 the way a short-prompt generation can, because the prior context constrains the distribution more than any temperature setting alone. You can tolerate slightly higher temperatures on long-context tasks than on minimal-context generation.

Bottom Line

GLM 5.2's sampling parameters follow the same OpenAI-compatible interface most teams already use, keeping migration cost low and tuning principles portable. Temperature 0.1–0.3 unlocks its strongest reasoning for coding and extraction; 0.7–1.0 opens it for creative and exploratory work — and the 89% GPQA Diamond score means quality holds at both extremes. Match the parameter to the task, set max_tokens as a guardrail, and GLM 5.2 delivers predictable performance across the full spectrum.

Try GLM 5.2 — no API key needed: glm5.app/chat

Sources

• Artificial Analysis Intelligence Index: https://artificialanalysis.ai/models/glm-5-2
• Zhipu AI / Z.ai official model page: https://z.ai
• GLM 5.2 on OpenRouter: https://openrouter.ai/zhipuai/glm-4-flash
• glm5.app pricing documentation: https://glm5.app/docs/pricing
• Zhiyu AI GitHub (open-weight release): https://github.com/THUDM/GLM