As of August 25, 2026, GLM-4.5-AirX API pricing is $1.10 per million input tokens and $4.50 per million output tokens. A typical 100K-in / 20K-out request costs $0.20. Source: https://docs.z.ai/guides/overview/pricing
Source: Z AI pricing (https://docs.z.ai/guides/overview/pricing)
Input $/M
$1.10
Output $/M
$4.50
Blended / 1M
$5.60
100K / 20K request
$0.20
| Context Window | 131.1K |
|---|---|
| Max Output | N/A |
| Release Date | N/A |
| Knowledge Cutoff | N/A |
| License | Open Source |
| Throughput | N/A |
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As of August 25, 2026, GLM-4.5-AirX Z AI API pricing is $1.10 per million input tokens and $4.50 per million output tokens. Input tokens are the prompt you send; output tokens are the completion. Long answers therefore cost more than short ones even when the prompt stays the same. Source: https://docs.z.ai/guides/overview/pricing
On this page the blended GLM-4.5-AirX API cost is $5.60 for 1 million input tokens plus 1 million output tokens. That 1:1 mix is the comparison scale used across the LLM pricing table. A 3:1 mix (three million input tokens per one million output tokens), which some calculators use, would be about $7.80.
A typical GLM-4.5-AirX API request with 100K input tokens and 20K output tokens costs about $0.20 at current rates as of August 25, 2026. Multiply that by daily or monthly volume to estimate spend. Caching, batch APIs, and volume discounts from Z AI can bring the real invoice lower than the public list rate at https://docs.z.ai/guides/overview/pricing.
GLM-4.5-AirX supports a 131K token context window. The context window is the total prompt plus conversation history the model can see in one call. Larger windows cost more when you fill them, because every input token is billed per million tokens.
Yes. GLM-4.5-AirX is an open-weight model by Z AI. You can download the weights and self-host, or pay Z AI's hosted API and skip GPU ops. Hosted API pricing on this page is the public per-million-token rate, not the electricity cost of running the weights yourself.
Benchmark data for GLM-4.5-AirX is not yet available. Check back as evaluation results are published. Use those scores with the token prices above when you are choosing between a cheaper LLM and a frontier model. GPQA, SWE-bench, and HLE are the evals people usually compare first.
Use the Compare picker on this page to start an LLM comparison. Search by model or provider, pick a second model, and AnotherWrapper opens a dedicated GLM-4.5-AirX vs page with per-million-token prices, blended 1M-in / 1M-out cost, context window, throughput, and benchmarks. You can also open any model from the sidebar, or go back to the LLM pricing table to sort the full index.
The cheapest LLM alternative depends on whether you optimize for blended token cost, coding evals, or context window. Start with the popular matchups on this page, then sort the LLM pricing table by blended cost to see which hosted APIs undercut GLM-4.5-AirX. Open-weight models can be cheaper still if you self-host, but that is not the same as Z AI API pricing on this page.
GLM-4.5-AirX is called through Z AI's API with an API key. Bill against input and output tokens separately; cache or shorten prompts if spend is high. If you are shipping a full product, AnotherWrapper includes pre-built API routes and templates for Z AI and 5+ other providers with auth, payments, and deployment already configured.
This GLM-4.5-AirX page tracks the latest published Z AI API pricing from Z AI, quoted per million tokens. List rates can differ from invoices because of prompt caching, batch discounts, committed-use tiers, and private contracts. Official rate sheet: https://docs.z.ai/guides/overview/pricing. Benchmark scores come from official publications and independent evals, not from a private lab run by AnotherWrapper.
GLM-4.5-AirX is a open-source model from Z AI. Current API pricing is $1.10 per million input tokens and $4.50 per million output tokens, with a 131.1K token context window. A blended 1M-in / 1M-out workload costs $5.60.
Rank it on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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