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Blog · 2026-08-26 · Vynaris Team

GLM-5.3's 28-task lap cost $0.28 vs GPT-5.5's $1.43

GLM-5.3 cost $0.01011 per passed public task versus GPT-5.5's $0.05289, an 80.9% gap. One lap and overlapping intervals limit the claim.

GLM-5.3 passed all 28 public Featherbench tasks for $0.2831. GPT-5.5 passed 27 for $1.4282. That makes GLM-5.3 $0.04278 cheaper per passed task. The receipt is useful, but one lap is not a universal model ranking. Prices verified 2026-08-26.

TL;DR

Verdict table

Public result                                         GLM-5.3                GPT-5.5                 Decision
----------------------------------------------------  ---------------------  ----------------------  ---------------------------------------------
Provider price per 1M input / cached / output tokens  $1.40 / $0.26 / $4.40  $5.00 / $0.50 / $30.00  GLM has the lower token sticker
Benchmark date                                        2026-08-22             2026-07-14              Same harness, not a simultaneous run
Passed tasks                                          28/28                  27/28                   One observed pass separates them
Rubric score                                          9.3/10                 8.7/10                  Same published judge, single trial
Run cost                                              $0.28312724            $1.42816                GLM cost 80.2% less
Cost per passed task                                  $0.01011               $0.05289                GLM cost 80.9% less
Median time to first token                            16.3 seconds           13.2 seconds            GPT returned a first token 3.1 seconds sooner
Published 95% pass interval                           88% to 100%            82% to 99%              Intervals overlap from 88% to 99%

The verdict is narrow: test GLM-5.3 first when a 3.1-second slower first token is acceptable and your compliance policy permits it. Keep GPT-5.5 when that latency or governance difference is worth more than $0.04278 per accepted outcome.

The quality-adjusted bill changes the comparison

The public benchmark ran 28 fixed tasks across coding, data, real-world work, security and tool use. It used the same prompts and harness. Providers were pinned and fallbacks were disabled.

Attempt cost is simple. GLM-5.3 used $0.28312724 / 28 = $0.01011 per task. GPT-5.5 used $1.42816 / 28 = $0.05101.

The buyer pays for accepted outcomes, so the denominator should be passed tasks. GLM-5.3 passed 28, leaving its cost per task unchanged at $0.01011. GPT-5.5 passed 27, so $1.42816 / 27 = $0.05289. The gap is $0.05289 - $0.01011 = $0.04278 per pass, or $427.83 per 10,000 passes.

At these observed rates, GLM-5.3 could average 5.23 full-price attempts per accepted outcome before matching GPT-5.5's cost per pass. That is a break-even boundary, not permission to tolerate four failures. A failed security or medical answer can cost more than the API call.

Use the calculator with the average GLM-5.3 lap shape of 238 input and 2,223 output tokens per task. Then replace those averages with your own traces and accepted-answer rate.

Lower prices beat higher token use on this lap

GLM-5.3 generated 62,243 output tokens across the run. GPT-5.5 generated 46,578. GLM therefore emitted 33.6% more output tokens, yet its total bill was 80.2% lower.

The unit rates explain the inversion. Z.AI lists GLM-5.3 at $1.40 per million input tokens and $4.40 per million output tokens. Official OpenAI documentation lists GPT-5.5 at $5 and $30. GLM's input rate is 72% lower and its output rate is 85.3% lower.

We use the benchmark's recorded usage.cost for the lap bills. We do not rebuild them from today's sticker prices because the public summary does not expose cached-token counts for each model. Mixing a current rate card with an incomplete cache split would create false precision.

The confidence interval is the part to remember

One observed pass separates 28/28 from 27/28. The published Wilson confidence intervals overlap across an 11-point span, from 88% to 99%. The evidence does not establish that GLM-5.3 has a higher underlying pass rate.

The runs also happened 39 days apart. The public summary re-scores saved answers against current checks, which helps comparability. It does not make the API conditions, provider load or model snapshots simultaneous.

The 9.3 versus 8.7 rubric result is directional too. A single model judge scored saved answers. Binary checks provide a firmer floor than one judge's six-tenths gap.

This is why our minimal eval harness guide starts with your tasks and repeated trials. The public lap tells us which model deserves that test. It cannot replace it.

Honest tradeoff: GPT-5.5 buys speed and a different risk envelope

GPT-5.5 delivered its first token in 13.2 seconds versus 16.3 seconds for GLM-5.3. That 3.1-second gap matters in an interactive agent. It barely matters in an overnight queue.

Compliance can dominate both numbers. The benchmark itself tells readers to check compliance before choosing GLM-5.3. Data location, vendor approval and incident response can justify the $0.04278 premium per successful task.

Do not route models from this leaderboard alone. Run repeated trials on your acceptance checks. Track cost per accepted answer, time to first token, total latency and reviewer time. Our earlier GLM-5.2 cost comparison explains the sticker gap. This new lap adds a public outcome denominator, not a universal winner.

Sources

Further reading: GLM-5.3-Flash's promotion-expiry cost prices the same 8k-input/2k-output workload before and after the 2026-09-09 deadline.