GLM-5.3-Flash API Pricing: AIHubMix vs OpenRouter, Including the 5.5% Fee

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GLM-5.3-Flash targets coding agents, long-context reasoning, and extended software-engineering workflows. Those workloads often send a large amount of repeated context, so a useful price comparison needs to include both prompt caching and platform-level fees.

This post compares the public rates available on September 1, 2026.

Base API rates

Route	Input / 1M	Output / 1M	Cache read / 1M	Platform fee
Z.ai API	$0.075	$0.250	$0.015	None listed
OpenRouter, Z.ai provider	$0.075	$0.250	$0.015	5.5%
AIHubMix	$0.056	$0.197	$0.014	None listed

AIHubMix, OpenRouter, and Z.ai are all running a limited-time 50% promotion through September 9, 2026 at 16:00 UTC.

Applying OpenRouter's platform fee

OpenRouter lists a 5.5% platform fee for pay-as-you-go accounts. Its model catalog still shows the provider's unmarked-up inference rate, so the effective cost of fully used credits can be modeled as:

effective_cost = model_cost * 1.055

For GLM-5.3-Flash, that produces effective rates of approximately:

input:     $0.075 * 1.055 = $0.0791 / 1M
output:    $0.250 * 1.055 = $0.2638 / 1M
cache read:$0.015 * 1.055 = $0.0158 / 1M

Adding prompt-cache ratios

Z.ai automatically identifies reusable prompt prefixes. A cache hit is part of the total input-token count, but is charged at the lower cache-read rate instead of the normal input rate.

For a workload with I total input tokens, cache ratio r, and O output tokens:

standard_input = I * (1 - r)
cached_input   = I * r

cost = standard_input * input_rate
     + cached_input * cache_read_rate
     + O * output_rate

The following examples use one million total input tokens and one million output tokens. Both platforms are assigned the same cache ratio to isolate the rate difference.

Cache ratio	AIHubMix	OpenRouter before fee	OpenRouter after fee	AIHubMix savings
0%	$0.253	$0.325	$0.343	26.2%
50%	$0.232	$0.295	$0.311	25.5%
80%	$0.219	$0.277	$0.292	24.9%

At an 80% cache ratio, for example, the one million input tokens consist of 200,000 standard input tokens and 800,000 cached tokens.

Reproduce the calculation

const platforms = {
  aihubmix: { input: 0.056, output: 0.197, cache: 0.014, fee: 0 },
  openrouter: { input: 0.075, output: 0.25, cache: 0.015, fee: 0.055 }
};

function cost({ input, output, cache, fee }, cacheRatio) {
  const modelCost =
    (1 - cacheRatio) * input +
    cacheRatio * cache +
    output;

  return modelCost * (1 + fee);
}

for (const ratio of [0, 0.5, 0.8]) {
  console.log({
    ratio,
    aihubmix: cost(platforms.aihubmix, ratio),
    openrouter: cost(platforms.openrouter, ratio)
  });
}

Prices in the code are dollars per million tokens, and the example fixes both total input and total output at one million tokens.

Takeaway

Caching reduces both bills. Because the cache-read rates are close, AIHubMix's relative price advantage narrows slightly as the cache ratio increases. After including OpenRouter's pay-as-you-go fee, AIHubMix remains approximately 24.9% to 26.2% less expensive across the scenarios above.

OpenRouter may still be the right choice when its unified provider ecosystem is the deciding feature. Z.ai remains the direct vendor route. For cost-sensitive GLM-5.3-Flash coding agents and batch jobs, AIHubMix currently has the lowest effective cost in this comparison.

Pricing can change, so verify the linked model pages before deploying a long-running workload.

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