GLM-5.3 vs GLM-5.3-Flash: Zhipu's Flagship Against Its Multimodal Flash

All figures below are from the China AI Hub database, last verified 2026-09-20. Where a field is not publicly disclosed, we say so rather than estimating.

At a glance

Dimension	GLM-5.3	GLM-5.3-Flash
Status (database)	active	active
Context window	1,048,576 tokens	1,048,576 tokens
Maximum output	131,072 tokens	131,072 tokens
Input price (per 1M)	$1.40	$0.15
Output price (per 1M)	$4.40	$0.50
Open weights	Yes (Apache-2.0)	Yes (Apache-2.0)
API available	Yes	Yes
Reasoning	Yes	Yes
Coding	Yes	Yes
Vision / Video	No / No	Yes / Yes
Tool calling	Not listed	Not listed
Computer use	Not listed	Yes
Agent capability	Not listed	Yes
Benchmark records in DB	4	3

Pricing

GLM-5.3 lists $1.40 input / $4.40 output per 1M tokens; GLM-5.3-Flash lists $0.15 input / $0.50 output — roughly 1/9 the input price and 1/9 the output price. The Flash tier undercuts the flagship on both axes, an unusual same-vendor structure where the cheaper model also lists more modalities.

Context and output

Identical listed context: 1,048,576 tokens input, 131,072 maximum output. On this axis the two are equivalent per the database. Both are open-weight 1M-context models — the only two in the collection with that combination.

Architecture and parameters

GLM-5.3 is a 744B-total / 40B-active MoE shipping open weights in FP8 (with a BF16 variant). GLM-5.3-Flash is a 320B-total / 18B-active model that Zhipu describes as the first open-source frontier model combining sparse and linear attention, with mHC hyper-connections and a claimed 30T-token multimodal pre-training corpus. The two are different architectures, not a base/sibling pair — GLM-5.3 shares its base with the deprecated GLM-5.2, while GLM-5.3-Flash is a distinct, more aggressively sparse design.

Coding

Both list coding. GLM-5.3 lists vendor-reported Terminal-Bench 3.0 (28.3), DeepSWE v1.1 (66.9), Agents’ Last Exam CLI (28.5) and CyberGym (84.5); GLM-5.3-Flash lists DeepSWE v1.1 (63.4), AutomationBench (48.8) and an Artificial Analysis Intelligence Index v4.1.1 (57). The benchmark sets differ, and GLM-5.3’s headline “50% coding improvement” claim is measured on Z.ai Code Bench, a private in-house benchmark — for both models the vendor-reported numbers rest partly on a private benchmark that cannot be externally reproduced. Notably, GLM-5.3’s CyberGym score (84.5) is a security-oriented agentic signal absent from the Flash model’s record.

Vision

A clear listed split: GLM-5.3-Flash lists vision and video input; GLM-5.3 is text-only input. For image and video workflows, only the Flash model lists the capability.

Agent capabilities

GLM-5.3-Flash lists computer use and agent capability; GLM-5.3 lists neither (and lists no tool calling or structured output). For GUI/agent automation, only the Flash model lists the relevant capability flags. Neither model lists tool calling under that exact label.

License and openness

Both are Apache-2.0 open weight (per GitHub repo metadata), with the same documented caveat that the README has no separate weights-license section — verify per-model Hugging Face cards before reuse. Both are self-hostable. This axis does not differentiate them.

Deployment

Both are served through Z.ai and BigModel, and both are self-hostable open weight. GLM-5.3 ships FP8 and BF16; GLM-5.3-Flash is a 320B/18B model versus GLM-5.3’s 744B/40B, so the Flash model has a smaller self-hosting footprint. GLM-5.3-Flash also has a FlashX speed-tier sibling SKU (higher throughput at a higher price); GLM-5.3 does not.

Benchmarks

The database holds 4 benchmark records for GLM-5.3 (Terminal-Bench 3.0, DeepSWE v1.1, Agents’ Last Exam CLI, CyberGym) and 3 for GLM-5.3-Flash (Artificial Analysis Intelligence Index, DeepSWE v1.1, AutomationBench). The overlap is only DeepSWE v1.1. All are vendor-reported.

Benchmark comparability is limited: the two models’ scores come from different test configurations — different benchmark sets and versions — and only DeepSWE v1.1 is shared. 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.

• Pricing: GLM-5.3-Flash has an advantage at roughly 1/9 the input and 1/9 the output price; the output-token gap compounds on reasoning workloads.
• Context: identical 1M-token windows — this axis does not differentiate.
• Coding: both list coding, so neither has a listed capability advantage; the benchmark sets differ and both rest partly on the private Z.ai Code Bench, limiting comparability.
• Vision / media: GLM-5.3-Flash is more relevant when image or video input is required — it lists vision and video where GLM-5.3 is text-only.
• Agent: GLM-5.3-Flash is more relevant for GUI/agent automation — it lists computer use and agent capability where GLM-5.3 lists neither.
• License: both Apache-2.0 open weight — this axis does not differentiate.
• Deployment: both self-hostable; GLM-5.3-Flash has a smaller footprint (320B/18B vs 744B/40B), which matters for self-hosting hardware.
• Benchmark: GLM-5.3 records 4 and GLM-5.3-Flash records 3 vendor-reported rows sharing only DeepSWE v1.1; not directly comparable.

Trade-off summary

• Price: GLM-5.3-Flash lists roughly 1/9 of GLM-5.3’s input and output prices.
• Media and agent: GLM-5.3-Flash lists vision, video, computer use and agent capability; GLM-5.3 is text-only.
• Context: identical 1M windows.
• License: both Apache-2.0 open weight.

Choose by modality and budget: multimodal, agent/GUI and cost-sensitive workloads favor GLM-5.3-Flash’s listed capabilities and price; text-only agentic/coding work where the flagship’s larger parameter budget matters favors GLM-5.3. Verify current 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-Flash lists ~1/9 the input and output prices, so cost favors the flash tier. It also lists vision, video, computer use and agent capability, which GLM-5.3 does not. Latency is not publicly documented for either model on this page.

For self-hosting. Both are Apache-2.0 open weight and self-hostable. GLM-5.3-Flash has a smaller footprint (320B/18B vs 744B/40B). Verify per-model Hugging Face cards for license clarity on both.

For coding agents. Both list coding. GLM-5.3-Flash additionally lists computer use and agent capability, which is more relevant for GUI/agent coding loops; GLM-5.3 lists higher scores on CyberGym and Agents’ Last Exam, but the benchmark sets differ and both rest partly on the private Code Bench.

For enterprise. Region, SLA, data-residency and compliance terms are not publicly documented — confirm with the vendor. Deployment is comparable (both self-hostable Apache-2.0); the modality and footprint differences decide.

What is uncertain

• Both models’ headline coding claims rest partly on Z.ai Code Bench, a private in-house benchmark that cannot be independently reproduced.
• Neither model lists tool calling under that exact label — an absence of documentation, not a verified lack of capability.
• The Apache-2.0 label comes from GitHub metadata; per-model Hugging Face cards should be verified before reuse.

Sources

• Z.ai — GLM-5.3 documentation
• Z.ai — GLM-5.3-Flash 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.