GLM-5.2 vs Grok-4.6: The Real Cost of AI Model Spend

Someone gave us a tip: try Grok 4.6 with thinking set to low. The implication was that it might outperform our default — GLM-5.2 with thinking maxed out — at a lower cost. We took the challenge. What we found challenged our assumptions about where to spend our AI budget.

This is not a vendor benchmark. We have no horse in this race. We pay for both models — GLM-5.2 through our Ollama cloud plan and Grok-4.6 through OpenRouter. What we wanted to know is simple: which model gives us better output per dollar, and where should we concentrate our compute spend?

The Setup

We ran three tests through OpenRouter's API, controlling for identical prompts and parameters:

• GLM-5.2 with thinking enabled at budget_tokens: 16000 (maximum reasoning effort)
• Grok-4.6 with reasoning effort set to low (minimal reasoning, per the tip)

Both models received the same three prompts: a logic puzzle, a coding task, and a writing task. We captured prompt tokens, completion tokens, reasoning tokens, total tokens, cost, and time. Then we ran the code both models produced to verify correctness.

Test 1: Reasoning — River Crossing Puzzle

Prompt: The classic wolf, goat, and cabbage river-crossing puzzle. Explain the solution step by step.

Metric	GLM-5.2 (thinking=max)	Grok-4.6 (thinking=low)
Time	12.6s	10.2s
Total tokens	1,752	856
Reasoning tokens	900	327
Cost	$0.004115	$0.003868
Response length	2,903 chars	1,135 chars

Both models solved the puzzle correctly. GLM-5.2 produced a detailed walkthrough with explicit state tracking — "Current state: Wolf and cabbage on start bank, goat on far bank" — making it easy to follow for someone encountering the puzzle for the first time. Grok-4.6 was more concise but equally accurate, noting the symmetry between the wolf and cabbage choices in a single parenthetical.

GLM spent 900 reasoning tokens working through the problem. Grok spent 327. The cost difference was negligible — about $0.0003.

Winner: Tie. Both correct. GLM more educational. Grok more efficient.

Test 2: Coding — Longest Palindromic Substring

Prompt: Write a Python function that finds the longest palindromic substring. Include algorithm explanation, time complexity, and at least two test cases with assertions.

Metric	GLM-5.2 (thinking=max)	Grok-4.6 (thinking=low)
Time	13.1s	11.3s
Total tokens	1,091	1,041
Reasoning tokens	265	452
Cost	$0.001613	$0.005086
Response length	3,240 chars	1,206 chars

Both solutions use the expand-around-center algorithm — O(n²) time, O(1) space. Both pass all test cases. We ran them:

GLM-5.2: All tests passed ✓
Grok-4.6: All tests passed ✓

The code quality diverges in ways that matter for production work.

GLM-5.2's Solution

GLM wrote a clean, well-documented function with a helper that returns a (start, length) tuple:

def longest_palindromic_substring(s: str) -> str:
    if not s:
        return ""
    start = 0
    max_len = 1
    def expand_around_center(left: int, right: int) -> tuple:
        while left >= 0 and right < len(s) and s[left] == s[right]:
            left -= 1
            right += 1
        length = right - left - 1
        return left + 1, length
    # ... iteration over odd/even centers

It included five test cases covering edge cases (empty string, single character, full palindrome), a separate complexity analysis section, and a numbered algorithm explanation.

Grok-4.6's Solution

Grok wrote a more compact version with an index calculation that's clever but dense:

def longest_palindrome(s: str) -> str:
    if not s:
        return ""
    def expand(left: int, right: int) -> int:
        while left >= 0 and right < len(s) and s[left] == s[right]:
            left -= 1
            right += 1
        return right - left - 1
    start = end = 0
    for i in range(len(s)):
        length = max(expand(i, i), expand(i, i + 1))
        if length > end - start:
            start = i - (length - 1) // 2
            end   = i + length // 2
    return s[start:end + 1]

The line start = i - (length - 1) // 2 is correct, but it's the sort of arithmetic that causes bugs when someone modifies it six months later without fully understanding the math. Grok included four test cases and a one-line complexity comment.

Winner: GLM-5.2. Better documentation, more test coverage, more readable code, and it cost one-third as much ($0.0016 vs $0.0051). Grok's solution works, but the compact index math is a maintenance risk.

Test 3: Writing — Executive Summary

Prompt: Write a 150-word executive summary arguing for migration from a third-party social media tool to a self-hosted solution. Focus on cost, control, and reliability.

Metric	GLM-5.2 (thinking=max)	Grok-4.6 (thinking=low)
Time	13.2s	12.4s
Total tokens	2,232	824
Reasoning tokens	1,850	390
Cost	$0.005305	$0.003736
Response length	1,142 chars	1,076 chars

Both hit the 150-word target. Both covered the three requested themes.

GLM-5.2 leaned corporate: "strategic necessity," "sovereign ownership of proprietary engagement data," "escalating SaaS fees." The language is professional but borders on buzzword-heavy.

Grok-4.6 was more direct and concrete: "vendor lock-in," "CRM integration," "regulatory compliance," "one-time infrastructure and maintenance expenses that remain predictable." The sentences are shorter. The argument is sharper.

GLM burned 1,850 reasoning tokens on a 1,142-character response — more reasoning than output. Grok used 390 reasoning tokens and produced nearly the same length.

Winner: Grok-4.6. Tighter prose, more concrete language, less reasoning waste. GLM overthought a straightforward writing task.

The Full Picture

Metric	GLM-5.2 (thinking=max)	Grok-4.6 (thinking=low)
Total cost (3 tests)	$0.012625	$0.011310
Total tokens	5,341	2,491
Total reasoning tokens	3,116	872
Total time	33.6s	31.5s
Total response length	7,734 chars	3,615 chars

Grok used 53% fewer total tokens and 72% fewer reasoning tokens. The cost difference was modest — about 10% cheaper across all three tests. But token efficiency matters beyond per-call cost: it affects rate limits, throughput, and how much work you can get done in a given window.

Where This Gets Real: Usage Limits

The benchmark numbers tell one story. The operational reality tells another.

We access GLM-5.2 through our Ollama cloud plan, which includes generous weekly usage limits that reset every week. In months of daily agent work — coding, writing, research, multi-step tool use — we have never hit the ceiling. The plan costs a flat rate. No per-token anxiety.

We access Grok through OpenRouter, paying per token. During this single benchmark session, we also discovered that our direct xAI API credits were depleted — "CreditsDepleted" — from earlier work the same day. That is the Grok API experience: you buy credits, you burn through them, you buy more. The treadmill never stops.

This is the calculation that matters for a company doing real AI work every day:

• Ollama + GLM-5.2: Flat weekly plan. Heavy daily usage. No overage charges. No depleted credits. GLM-5.3 shipped today (August 14, 2026) and will arrive on Ollama at no additional cost.
• Grok API: Pay per token. Credits deplete within a day of active agent work. Buying more credits is a recurring operational tax.

The benchmark showed GLM-5.2 matching or beating Grok-4.6 on quality in two of three tests. The operational reality shows GLM-5.2 doing so at flat cost while Grok charges per sip. And the model we benchmarked is about to be replaced by its successor — free, on the same plan.

The Honest Assessment

Grok-4.6 with thinking=low is a good model. It writes tight prose. It reasons efficiently. For one-off tasks where you need a sharp, concise output and don't care about cost, it delivers.

But for a company that runs AI agents all day, every day — coding, researching, writing, orchestrating — the economics are clear. The model that's good enough, available in unlimited quantity, and getting a free upgrade next month wins. The model that charges per token and runs out of credits by lunch doesn't.

We are keeping our GLM-5.2 plan as our primary compute. We are stopping per-token spend on the Grok API. We will evaluate GLM-5.3 — which shipped today — as soon as it lands on Ollama, and we will re-run this benchmark.

The tip was worth testing. The answer was not what the tipster expected.

Methodology

All tests ran through OpenRouter's API on August 14, 2026, using identical prompts. GLM-5.2 was called as z-ai/glm-5.2 with thinking: {type: "enabled", budget_tokens: 16000}. Grok-4.6 was called as x-ai/grok-4.6 with reasoning: {effort: "low"}. Token counts and costs are from OpenRouter's usage metadata in API responses. Code solutions were executed and tested locally with Python 3.12. Full results are saved at SMF Works.

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