Qwen3.8-Max vs GLM-5.3: Openness and Media

All figures below are from the China AI Hub database, last verified 2026-09-22. Where a field is not publicly disclosed, we say so rather than estimating.

At a glance

Dimension	Qwen3.8-Max	GLM-5.3
Context window	1,048,576 tokens	1,048,576 tokens
Maximum output	131,072 tokens	131,072 tokens
Input price (per 1M)	$2.00	$1.40
Output price (per 1M)	$6.00	$4.40
Open weights	No (proprietary)	Yes (Apache-2.0)
API available	Yes	Yes
Reasoning	Yes	Yes
Coding	Yes	Yes
Vision / Video	Yes / Yes	Not listed
Tool calling	Yes	Not listed
Structured output	Yes	Not listed
Benchmark records in DB	5	4

Pricing

Qwen3.8-Max lists $2.00 input / $6.00 output per 1M tokens; GLM-5.3 lists $1.40 input / $4.40 output — about 30% lower on both axes. Both prices come from official provider pages. Reasoning workloads multiply output tokens, so the output-price difference compounds on long chains of thought.

Context

Identical listed context: 1,048,576 tokens input, 131,072 tokens maximum output. On this axis the two are equivalent per the database.

Capabilities

Both list reasoning and coding. Qwen3.8-Max additionally lists vision, video, tool calling and structured output. GLM-5.3 lists none of those four. For document-image, video and structured-output workflows, the database records a clear difference in listed capability.

Openness and deployment

This is the largest split. GLM-5.3 is open weight under Apache-2.0 and can be self-hosted; Qwen3.8-Max is proprietary and API-only. Organizations with data-residency or self-hosting requirements have an option in one and not the other.

Benchmarks and verification

The database holds 5 benchmark records for Qwen3.8-Max and 4 for GLM-5.3, each labeled with source type and version. Counts differ because benchmarks differ; we do not rank the models. Check the benchmark pages for per-benchmark scores and source-type labels.

Benchmark comparability is limited: the two models’ scores come from different test configurations — different benchmark versions, evaluation harnesses and tool setups — and several benchmarks cover only one of the two. Do not rank on headline scores alone.

Why each difference matters

China AI Hub analysis indicates the following per-dimension implications, drawn from the listed facts above.

• Cost: GLM-5.3 has an advantage at ~30% lower input and output price; the output-price gap compounds on reasoning workloads that multiply output tokens.
• Long-context: identical listed windows (1M input / 131,072 output) — this axis does not differentiate.
• Reasoning: both list reasoning, so neither has a listed advantage on this axis.
• Coding: both list coding; Qwen3.8-Max is more relevant for structured, tool-driven coding pipelines because it additionally lists tool calling and structured output.
• Vision / media: Qwen3.8-Max is more relevant when document-image or video input is required — it lists vision and video where GLM-5.3 lists neither.
• Deployment: GLM-5.3 has an advantage for self-hosting and data-residency (Apache-2.0 open-weight, self-hostable); Qwen3.8-Max has a limitation here (proprietary, API-only).
• API: both expose an API; deployment constraints and workload decide.

Trade-off summary

• Media and tools: Qwen3.8-Max lists vision, video, tool calling and structured output; GLM-5.3 lists none of these.
• Openness: GLM-5.3 is Apache-2.0 open weight and self-hostable; Qwen3.8-Max is API-only.
• Price: GLM-5.3 lists roughly 30% lower input and output prices.
• Context: identical listed windows.

Choose by deployment constraints and workload: self-hosting and budget favor GLM-5.3’s openness and price; vision/video and tool-heavy pipelines favor Qwen3.8-Max’s listed capabilities. Verify prices on the official pages before committing.

Decision context

China AI Hub analysis indicates the following decision-context implications, drawn from the listed facts above.

For API developers. GLM-5.3 lists ~30% lower input and output prices, and the output gap compounds on reasoning workloads. Qwen3.8-Max lists tool calling, structured output, vision and video, which is more relevant when the pipeline needs structured/tool-driven or media input; GLM-5.3 lists none of these. Context is identical (1M / 131,072). Latency is not publicly documented on this page.

For self-hosting. GLM-5.3 is Apache-2.0 open weight and self-hostable; Qwen3.8-Max is proprietary and API-only. Hardware requirements and quantization are Not publicly documented on this page.

For coding agents. Both list coding; Qwen3.8-Max additionally lists tool calling and structured output, which is more relevant for structured, tool-driven coding pipelines. Context is identical. SWE-benchmark and terminal scores are not broken out (aggregate counts 5 vs 4).

For enterprise. Region, SLA, data-residency and compliance terms are not publicly documented — confirm with the vendor. Deployment is the key split: GLM-5.3 is self-hostable (Apache-2.0) with a data-residency option, while Qwen3.8-Max is API-only.

What is uncertain

• Benchmark comparability is limited — different versions, harnesses and partial coverage.
• GLM-5.3 lists no tool calling or structured output — an absence of documentation, not a verified lack of capability.

Sources

• Alibaba Cloud Model Studio — Qwen3.8-Max
• Alibaba Cloud Model Studio — Model pricing
• Z.ai — GLM-5.3 documentation
• Z.ai — Pricing overview
• Z.ai — GLM-5 (GitHub)

Labels used above: Official fact (prices, context windows, capabilities and license terms from primary provider sources), Vendor-reported claim (benchmark scores), and China AI Hub analysis (the “why each difference matters” reasoning, introduced as analysis). No third-party benchmark evidence is currently recorded for these models.