Model comparison

GLM 5.2 vs GLM 5.3

Two Z.ai tiers compared on the figures that decide which one a workload actually needs.

Recorded profile verification October 2, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below

Z.ai

GLM 5.2

Balanced · Open weights

Z.ai

GLM 5.3

Frontier · Open weights

AI model capability comparison
Specification	GLM 5.2	GLM 5.3
Provider
Provider	Z.ai	Z.ai
Tier
Tier	Balanced	Frontier
Context window
Context window	1M	1M
Max output
Max output	131K	131K
Input / 1M tokens
Input / 1M tokens	$1.40	$1.40
Output / 1M tokens
Output / 1M tokens	$4.40	$4.40
Weights
Weights	Open	Open
Parameters
Parameters	Not verifiedUnverified	753B total (MoE; active count not verified)
Reasoning levels
Reasoning levels	none, minimal, low, medium, high, xhigh, max	low, high, max
Modalities
Modalities	text	text
License
License	MIT	GLM-5.3 License (custom)
API model id
API model id	glm-5.2	glm-5.3
Released
Released	June 16, 2026	August 14, 2026
Artificial Analysis Intelligence Index [max] · source date GLM 5.3: 2026-10-01
Artificial Analysis Intelligence Index [max] · source date GLM 5.3: 2026-10-01	Not verifiedUnverified	45
Terminal-Bench 2.1 · source date GLM 5.3: 2026-08-14
Terminal-Bench 2.1 · source date GLM 5.3: 2026-08-14	Not verifiedUnverified	88.2
DeepSWE 1.1 · source date GLM 5.3: 2026-08-14
DeepSWE 1.1 · source date GLM 5.3: 2026-08-14	Not verifiedUnverified	66.9
Humanity's Last Exam · source date GLM 5.3: 2026-08-14
Humanity's Last Exam · source date GLM 5.3: 2026-08-14	Not verifiedUnverified	62.5

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

Source date2026-10-01
Artificial Analysis (GLM-5.3 (max))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
Source date2026-08-14
Z.ai GLM-5.3 blog + HF model card (vendor, with tools, full set)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
Source date2026-08-14
Z.ai GLM-5.3 blog + HF model card (vendor, mini-swe-agent family)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
Source date2026-08-14
Z.ai GLM-5.3 blog + HF model card eval results (vendor, Claude Code harness)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

Pricing tiers

GLM 5.2: Z.ai token API $1.40/$4.40 per MTok, cached input $0.26; cache storage is limited-time free with no verified end date. Text-only API advertises 1M context and a 131,072 output ceiling. The MIT checkpoint config sets 1,048,576 native context.

GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling.

BalancedOpen weightsRecord checked October 2, 2026

GLM 5.2

The previous GLM text-only generation, available as an MIT-licensed checkpoint and documented token-API model; GLM-5.3 uses the same base with newer post-training.

Best for

Open-weight deployments
Reasoning
Cost-sensitive work

Watch out

Coding Plan 5.2 requests route to 5.3; direct token-API identity is separate. GLM-5.2 supports thinking on/off: none/minimal stop thinking, low/medium map to high, and xhigh maps to max (default). Accepted effort strings do not represent seven independent compute tiers. Exact total/active parameter counts remain unverified; do not infer them solely from the shared base.

FrontierOpen weightsRecord checked October 2, 2026

GLM 5.3

Z.ai's text-only GLM flagship, with a 753B-total MoE checkpoint and always-enabled reasoning. Published benchmark figures identify particular harnesses and settings.

Best for

Coding Plan subscribers
Long-horizon coding
Chinese + English

Watch out

Custom licence: model-as-a-service operators whose licensee/affiliate aggregate revenue exceeds $10B in any consecutive 12 months require Z.AI security review before commercial use; the definition excludes embedded end-user features/harnesses and mere relays. Keep attribution/permission notices. Reasoning defaults to max; disabling it fails. Accounts that ever subscribed to Coding Plan, including expired subscriptions, currently have Model API access only through the Chat Completions-compatible protocol. Active parameter count and exact initial weight-publication day remain unverified.

When the cheaper one wins

Both charge $4.40 per million output tokens at standard rates, so output price is not the split. Prefer the row whose “best for” line matches the job, then confirm the live vendor rate.

Run the model picker

Evidence confidence: Medium

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

Pricing verified on both sides — Input and output rates are verified for both models.
4/5 core specs verified on both sides — Not published for at least one side: parameter count.
No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
Verified within the last 90 days — Older of the two catalog checks was 24 days ago.
Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

GLM 5.2: GLM-5.2 official announcement (accessed 2026-10-01)
GLM 5.2: GLM-5.2 API scope (accessed 2026-10-01)
GLM 5.2: GLM-5.2 exact accepted effort mappings (accessed 2026-10-01)
GLM 5.2: GLM output ceiling and direct model identifier (accessed 2026-10-01)
GLM 5.2: Z.ai first-party pricing (accessed 2026-10-01)
GLM 5.2: GLM-5.2 official checkpoint (accessed 2026-10-01)
GLM 5.2: GLM-5.2 exact MIT licence (accessed 2026-10-01)
GLM 5.2: GLM-5.2 native checkpoint context (accessed 2026-10-01)
GLM 5.3: Z.ai GLM-5.3 announcement (accessed 2026-10-01)
GLM 5.3: GLM-5.3 official API limits and account/protocol restrictions (accessed 2026-10-01)
GLM 5.3: Z.ai exact output ceiling (accessed 2026-10-01)
GLM 5.3: Z.ai current first-party prices (accessed 2026-10-01)
GLM 5.3: GLM-5.3 checkpoint and publisher benchmarks (accessed 2026-10-01)
GLM 5.3: GLM-5.3 exact custom licence (accessed 2026-10-01)

Related comparisons

Diving deeper on one model? GLM 5.2 · GLM 5.3

Common questions

GLM 5.2 vs GLM 5.3

Answered from the verified figures on this page rather than general guidance.

Is GLM 5.2 or GLM 5.3 cheaper for input?

Both list a base rate of $1.40 per million input tokens. Actual cost can differ with context tiers, caching, tools and other charges. GLM 5.2: Z.ai token API $1.40/$4.40 per MTok, cached input $0.26; cache storage is limited-time free with no verified end date. Text-only API advertises 1M context and a 131,072 output ceiling. The MIT checkpoint config sets 1,048,576 native context. GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling.

Is GLM 5.2 or GLM 5.3 cheaper for output?

Both list a base rate of $4.40 per million output tokens. Actual cost can differ with context tiers, caching, tools and other charges. GLM 5.2: Z.ai token API $1.40/$4.40 per MTok, cached input $0.26; cache storage is limited-time free with no verified end date. Text-only API advertises 1M context and a 131,072 output ceiling. The MIT checkpoint config sets 1,048,576 native context. GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling.

Which has the larger context window, GLM 5.2 or GLM 5.3?

Both accept about 1M tokens of context, so document length will not decide between them.

Do GLM 5.2 and GLM 5.3 support the same reasoning levels?

GLM 5.2 exposes none, minimal, low, medium, high, xhigh, max, while GLM 5.3 exposes low, high, max.

Should I use GLM 5.2 or GLM 5.3?

We classify GLM 5.2 in the balanced tier and GLM 5.3 in the frontier tier. Test whether either meets your accuracy, tool-use and latency requirements before choosing on price.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

Positioning: GLM 5.2 sits in the balanced tier, GLM 5.3 in the frontier tier. Compare accuracy, reliability, latency, and cost on representative tasks before choosing.

Run the model picker Estimate stack cost All model comparisons

Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.