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Head to head: GLM 5.2 vs OpenAI: GPT-5.6 Sol - RuntimeWire

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The scoreboard says it plainly: OpenAI: GPT-5.6 Sol wins 113.0 to 93.5, with 99% confidence . It took 9 task wins to GLM 5.2’s 1 , with 2 ties. That’s not a vibes-based edge or a judge’s whim; it’s a decisive result spread across coding, extraction, SQL, classification, and reasoning.

What separates Sol here is not flashy creativity but discipline. It repeatedly did the boring, important things right: returning exactly one JSON object when asked, avoiding Markdown fences in code-only tasks, keeping justifications within strict word limits, and producing cleaner normalized outputs. That showed up in lease extraction, strict JSON extraction, support-ticket classification, nuanced classification, the LRU cache task, and the Python log redactor. GLM was often close on substance, but too often lost on compliance details that matter in production.

The biggest quality gap came on tasks where correctness and query logic actually bite. In the SQL window query, GLM simply answered the wrong problem by computing a max-minus-min difference instead of the second-highest order. In reasoning-shift-coverage, its analysis was mostly sound but the final answer was truncated, which is still a miss. Sol, by contrast, was consistently complete and structurally correct. Even where both models were strong, Sol tended to be the one that was tighter, cleaner, and less error-prone.

GLM’s lone outright win — the European Spanish localization toast — is real and deserved. Its phrasing was more natural, more concise, and better suited to the mobile UI constraint. And the two ties show GLM is not outclassed everywhere: on the weekly refunds SQL and ops-email proofreading, it was competitive enough that judge preference split. But those are islands in a match otherwise controlled by Sol.

Final call: GPT-5.6 Sol is the clear winner. GLM 5.2 looks capable, but Sol was markedly better at turning correct intent into correct, usable outputs — and in this test, that difference was decisive.

How they were tested

We ran 12 fresh text tasks, generated on the fly for this matchup so neither model could prepare in advance, and had gpt-5.4 score each one. To cancel position bias, every task was judged twice — once in each presentation order — and every number reported here, including the headline totals, is the average of both passes. GLM 5.2 scored 93.5 to OpenAI: GPT-5.6 Sol's 113.0.

1. extract-lease-details

Extract the requested fields from the messy text below. Return exactly one JSON object with keys: tenant_name, property_address, lease_start, lease_end, monthly_rent_usd, security_deposit_usd, parking_spaces, pets_allowed, notice_days, utilities_included. Messy text: "Draft notes from call with Juniper Ridge Properties re: new lease. Tenant will be Malik Rowan (spell checked). Unit is 4C at 1187 Waverly Avenue, Spokane, WA 99204. Term discussed: starts 2025-09-15 and runs through 2026-09-14. Base rent is $1,845/mo; deposit equals $1,200. They said one reserved parking stall is included behind the building. Pets: cats okay, no dogs — mark as pets allowed yes. Tenant must give 45 days notice before moving out. Utilities covered by landlord are water, trash, and sewer; tenant handles electric + internet. Ignore old quote for 1189 Waverly and the earlier rent figure of $1,795 from April."

Winner: OpenAI: GPT-5.6 Sol — Model B is slightly better because it cleanly returns exactly one JSON object and formats the property address more naturally as a standard address string. Model A is also highly accurate, but its address phrasing ("Unit 4C at ...") is a bit less clean for extraction purposes. (Second judge pass, order swapped — scores are the average of both: Model B is fully correct and follows the instruction to return exactly one JSON object. Model A extracts the same details correctly, but it wraps the JSON in a Markdown code block, which violates the formatting requirement.)

2. python-log-redactor

In Python, write a function redact_log(line: str) -> str for application logs. Requirements: - Replace every email address with [EMAIL]. - Replace every IPv4 address with [IP]. - Replace every occurrence of token= followed by one or more non-space characters with token=[REDACTED]. - Preserve all other text exactly. - Use only the Python standard library. - Return code only, no explanation. Example: Input: "login failed for nora.ives@elmbyte.io from 203.44.18.9 token=abC99_x at 09:14" Output: "login failed for [EMAIL] from [IP] token=[REDACTED] at 09:14"

Winner: OpenAI: GPT-5.6 Sol — Model B is better because it returns valid Python code...