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.

Catalog record checked August 14, 2026; individual provider fields may change.

Z.ai

GLM 5.2

Frontier · Open weights

Z.ai

GLM 5.3

Frontier

AI model capability comparison
Specification	GLM 5.2	GLM 5.3
Provider	Z.ai	Z.ai
Tier	Frontier	Frontier
Context window	1M	1M
Max output	128K	128K
Input / 1M tokens	$1.40	Not verified
Output / 1M tokens	$4.40	Not verified
Weights	Open	Closed
Parameters	Not disclosed	Not disclosed
Reasoning levels	high, max	low, high, max
Modalities	text	text
Released	June 16, 2026	August 14, 2026
Artificial Analysis Intelligence Index [max] (2026-08-08)	53	Not verified

Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Pricing tiers: GLM 5.3: Direct `glm-5.3` API is documented as coming soon and is not on the Z.ai per-token table (do not assume GLM 5.2's $1.40/$4.40). Today it is on every GLM Coding Plan: points-based quota (input / cached input / output). Off-peak is 50% of standard points. Peak is Monday–Friday 14:00–18:00 UTC+8. Coding Plan requests for GLM-5.2/5.1 are routed to 5.3.

FrontierOpen weights

GLM 5.2

Z.ai's flagship GLM-5.2 coding model with a documented 1M-token context and 128K max output.

Best for

Open-weight deployments
Long-horizon coding agents
Long context on a budget

Watch out

Ecosystem tooling is thinner than the closed frontier labs — budget integration time. Parameter count is not published on the current Z.ai model card, so it stays unverified here. GLM Coding Plan now defaults to GLM 5.3; requests for 5.2/5.1 on that plan are routed to 5.3. This row remains the open-weight / token-list API identity at $1.40/$4.40.

Frontier

GLM 5.3

Z.ai's 2026-08-14 Coding Plan flagship — same base as GLM 5.2, with the documented gains from post-training only. Direct token API and open weights are not published yet.

Best for

GLM Coding Plan agent work
Long-horizon coding in Claude Code / OpenCode / Cline
Teams already on Z.ai subscriptions

Watch out

Thinking cannot be disabled (`thinking.type: disabled` fails); `reasoning_effort` is low / high / max (default max). Open weights are promised about two weeks after launch pending safety review — not a downloadable checkpoint today. Vendor DeepSWE v1.1 66.9 used mini-swe-agent at 400K context, not the public Datacurve v1.1 board (snapshot generated 2026-08-13, before this launch).

Benchmark

DeepSWE 1.1 in context

Only GLM 5.2 has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.

Local leader

Claude Opus 5 [max]

74%

Rows shown

Highest published reasoning effort per model (not best Pass@1)

Snapshot date

2026-08-13

Mirrored from deepswe.datacurve.ai

Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.

DeepSWE 1.1 leaderboard with pass rate, cost, tokens, and steps per task
#	Model	Pass@1	Cost / task	Tokens / task	Steps / task
1	Claude Opus 5 [max]	74%	$11.84	118k	99
2	GPT-5.6 Sol [max]	73%	$8.39	60k	61
3	Claude Fable 5 [max]	70%	$21.63	119k	88
4	GPT-5.6 Terra [max]	70%	$4.95	72k	76
5	Kimi K3 [max]	69%	$4.65	82k	98
6	GPT-5.6 Luna [max]	67%	$3.03	73k	102
7	GPT-5.5 [xhigh]	67%	$7.23	46k	82
8	Grok 4.6 [xhigh]	67%	$5.50	71k	87
9	Gemini 3.7 Flash [high]	65%	$2.18	107k	125
10	DeepSeek V4-Pro [max]	63%	$0.24	106k	155
11	Claude Opus 4.8 [max]	59%	$13.22	135k	120
12	Qwen3.8-Max [xhigh]	58%	$3.73	95k	111
13	Muse Spark 1.2 [xhigh]	55%	$3.70	99k	101
14	Claude Sonnet 5 [max]	54%	$26.40	214k	268
15	Grok 4.5 [high]	54%	$2.42	36k	61
16	DeepSeek V4-Flash [max]	53%	$0.10	108k	153
17	Muse Spark 1.1 [xhigh]	53%	$2.36	74k	96
18	GPT-5.4 [xhigh]	52%	$5.65	71k	70
19	Gemini 3.6 Flash [high]	47%	$4.42	96k	117
20	GLM 5.2 [max]	44%	$3.92	78k	129
21	Gemini 3.5 Flash [high]	36%	$3.45	76k	105
22	Kimi K2.7 Code	31%	$2.82	59k	149
23	Claude Sonnet 4.6 [high]	30%	$5.52	76k	134
24	Gemini 3.1 Pro [high]	12%	$2.14	28k	76

DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.

Common questions

GLM 5.2 vs GLM 5.3

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

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 high, max, while GLM 5.3 exposes low, high, max.

Should I use GLM 5.2 or GLM 5.3?

Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GLM 5.2 suits open-weight deployments; GLM 5.3 suits glm coding plan agent work.

Can I self-host GLM 5.2 or GLM 5.3?

GLM 5.2 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GLM 5.3 is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

Tier and workload decide this more reliably than a leaderboard position does.

If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.

If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.

Find the right AI tool Estimate stack cost All model comparisons

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