GLM-5.3 vs GLM-5.2: A Hard Benchmark with Real API Calls

The first benchmark showed that GLM-5.3 was more likely to deliver visible answers within a fixed output budget. This follow-up raised the difficulty across six objectively verifiable tasks: extreme mathematics, causal calibration, constraint reasoning, nested JSON, multi-stage physics, and executable optimization code.

The first-delivery score was 4/6 for GLM-5.3 and 2/6 for GLM-5.2. That headline does not tell the whole story: GLM-5.2 produced the only dependency optimizer that passed the hidden test suite, while both models failed the extreme math task.

Test setup

Field	Value
Base URL	https://cn.crazyrouter.com
Endpoint	POST /v1/chat/completions
Models	glm-5.3, glm-5.2
Temperature	0.2
Tools and web access	Disabled
Scoring	First delivery only; larger-budget retries were diagnostic

Before the run, GET https://cn.crazyrouter.com/v1/models listed both exact model IDs. All 12 first-run responses returned the requested model identity.

Results

Task	Budget	GLM-5.3	GLM-5.2
Unequal-probability coupon collector	9,000	Length, empty body	Length, empty body
Simpson's paradox and causal limits	6,000	Pass	Length, empty body
Minimal unsatisfiable core	5,000	Pass, strict JSON	Length, empty body
Nested regional incident JSON	4,000	Pass	Pass
Pulley, slack rope, friction, spring	14,000	Pass	Length, empty body
Dependency-constrained optimizer	16,000	Length, empty body	Hidden tests pass

GLM-5.3 completed four first-run tasks in 334.1 seconds total. GLM-5.2 completed two in 665.0 seconds. Latency was retained as operational evidence but did not change correctness scores.

The extreme math task defeated both models

The task used four coupon probabilities: 1/2, 1/4, 1/8, 1/8. It required two exact inclusion-exclusion results:

E[T] = 1339/105 ≈ 12.752380952
P(T<=8) = 46179/131072 ≈ 0.352317810

Both models consumed the full 9,000-token reasoning budget and returned no visible answer. A 20,000-token retry still produced an empty body for GLM-5.3; GLM-5.2 exceeded the runner's 420-second read timeout.

This is a useful production warning: raising max_tokens does not guarantee a deliverable answer. A gateway should inspect finish reason, visible content, and timeout state together.

Where GLM-5.3 was stronger

On the Simpson task, GLM-5.3 correctly recovered the reversal:

Small stones: A 81/87 > B 234/270
Large stones: A 192/263 > B 55/80
Crude total: A 273/350 < B 289/350
50/50 standardized rates: A=0.8305, B=0.7771

It also separated standardization from causal identification, noting the need for randomization or adequate adjustment, positivity, no unmeasured confounding, and uncertainty estimates.

The minimal-UNSAT task required exactly one JSON object. GLM-5.3 returned [1,2,3] and correctly proved that the constraints force C and D into the same position. The response parsed without cleanup.

The physics task required switching system boundaries after the hanging mass hit the ground and the rope went slack. GLM-5.3 matched all four reference values:

a ≈ 0.847 m/s²
v1 ≈ 1.59 m/s
v2 ≈ 1.18 m/s
x ≈ 0.0835 m

Where GLM-5.2 won

The code task asked for optimize_release_plan(items, capacity_by_day, dependencies) with transitive dependencies, per-day capacities, value maximization, risk and lexical tie-breaks, invalid references, and cycle detection.

GLM-5.2 returned a complete Python file at the 16k budget and passed the external hidden test suite without edits. GLM-5.3 returned no body at 16k. At 24k it finally produced code, but selected a plan worth 24 instead of the correct optimum worth 29, so the retry still failed.

This is why code generation must be executed. Version numbers, code length, and a polished explanation are not substitutes for tests.

OpenAI-compatible request

import os
import requests

r = requests.post(
    "https://cn.crazyrouter.com/v1/chat/completions",
    headers={"Authorization": f"Bearer {os.environ['CRAZYROUTER_API_KEY']}"},
    json={
        "model": "glm-5.3",
        "messages": [{"role": "user", "content": "Your acceptance task"}],
        "temperature": 0.2,
        "max_tokens": 6000,
    },
    timeout=600,
)

choice = r.json()["choices"][0]
assert choice["finish_reason"] == "stop"
assert choice["message"]["content"].strip()

Production guidance

1. Prefer GLM-5.3 for causal analysis, constraint reasoning, and multi-stage derivations based on this run.
2. Keep GLM-5.2 in the candidate pool for executable algorithm generation.
3. Treat HTTP 200 + empty content as a business failure.
4. Parse and validate JSON instead of accepting visually plausible output.
5. Run generated code in an isolated test harness.
6. Track first-delivery success separately from retry-assisted success.

FAQ

Is GLM-5.3 universally better?

No. It had better first-run coverage, but GLM-5.2 won the executable code task.

Did more output budget fix the failures?

Not reliably. GLM-5.2 physics still returned an empty body at 20k. GLM-5.3 math remained empty at 20k, and its 24k code retry failed hidden tests.

Why count HTTP 200 responses as failures?

Because multiple responses consumed the entire reasoning budget and delivered no usable content.

How was code graded?

The original generated file was imported and executed against the same hidden harness. No manual fixes were allowed.

Was latency part of the score?

No. It was kept as operational evidence only.

What is the practical model-selection rule?

Route by task type and enforce real acceptance tests. A single aggregate model ranking loses the most important result from this benchmark.

Verdict

GLM-5.3 won first-delivery coverage, 4/6 to 2/6. GLM-5.2 delivered the best executable algorithm. The defensible production conclusion is task-aware routing with strict validation, not automatic replacement of the older model.

Run the same models through Crazyrouter's OpenAI-compatible API.