GLM-5.3 Launches: The Leak Was Real — Z.ai's Post-Trained Coding & Cyber-Defense Flagship

The leak was real, the launch is here, and GLM-5.3 now has an independent score to argue about. Artificial Analysis' Intelligence Index — measured by the lab, not by Z.ai — puts GLM-5.3 at 60, tied with Kimi K3 for the top open-weights score on the board and 7 points clear of GLM-5.2's 53. The API went live this week at the same price as its predecessor, the open weights are confirmed for Friday, August 28, and the coding and cyber-defense claims Z.ai has been making since the August 14 announcement are starting to become testable. This page first tracked GLM-5.3 from its August 3 leak traces; this is the launch report, updated in place with what the launch, the API, and the first independent benchmark actually confirmed.

From leak to launch

The four traces that surfaced on August 3 — a "ZCode for GLM-5.3" harness page, an official docs page reachable for roughly an hour, a Bing index entry reading "GLM-5.3 Official Harness," and a commit adding a "glm-5.3" entry with JSON Schema support to Zhipu's official Java SDK — all pointed at a real, named release. Z.ai co-founder Tang Jie's "sooooooon" reply and the "epic-level plus" framing are now confirmed by an actual product rather than a rumor. The "roughly a week" timing signal this page tested — posted on X by @teortaxesTex after DeepSeek V4 Pro shipped on August 13 — held to the day: Z.ai formally announced GLM-5.3 on August 14 under the slogan "Built to Code. Ready for Cyber Defense." The follow-on came this week: on August 19 Z.ai said the GLM-5.3 API was live and open for calls, priced the same as GLM-5.2, with the model already wired into ZCode, AutoClaw, and the GLM Coding Plan.

What post-training actually bought

The architectural claim is the part worth pinning down, because the leak's loudest specific — parameters rising past one trillion — is wrong. GLM-5.3 is not a larger model. Z.ai says it reuses the exact same 743B Mixture-of-Experts base as GLM-5.2 (roughly 40 billion active parameters per token), keeps the same 1M-token context window and roughly 128K max output, and gets all of its gains from scaled-up post-training: more long-horizon task environments, more environment types, and longer training runs, built on the IndexShare long-context, SAO asynchronous-RL, and open-source slime framework that already produced GLM-5.2. That "no retraining, all post-training" framing is Z.ai's own, and it has not been independently audited. The size is the other headline: at 743B total parameters with only about 40B active per token, GLM-5.3 is light enough to self-host on a modest cluster and cheap enough to serve at volume — the "smaller, cheaper, open" positioning that launch-day commentary attached to it, and a sharp contrast with the closed frontier flagships it is being measured against.

Coding and agents: the numbers Z.ai is claiming

On coding, Z.ai reports — all vendor-reported and unreproduced — Terminal-Bench 3.0 rising from 4.6 to 28.3, which Z.ai calls the top open-weights score on that harness; DeepSWE v1.1 rising from 46.2 to 66.9; SWE-Marathon roughly doubling from 19.4 to 42.5; and Agents' Last Exam (CLI) rising from 23.8 to 28.5. On Z.ai's internal code bench, GLM-5.3 scored 31.4% at roughly 50K output tokens per task at high effort, against Claude Opus 4.8's 29.5% at about 120K tokens — Z.ai's point being that GLM-5.3 reaches a comparable result while spending far fewer output tokens. Claude Fable 5 still leads that internal eval at 39.5% at maximum effort, and Z.ai concedes GLM-5.3 still trails GPT-5.6 Sol and Claude Fable 5 on several harder coding evaluations. Treat all of these as vendor numbers until an independent harness reproduces them.

Cyber defense: the capability nobody saw coming

The cybersecurity numbers are the real news, and they are also entirely vendor-reported. On CyberGym, a white-box vulnerability discovery and validation benchmark, Z.ai reports GLM-5.3 at 84.5%, up from GLM-5.2's 77.2% and ahead of Anthropic's Mythos 5 (83.8%) and GPT-5.6 Sol (83.6%). On ExploitBench, which demands both root-cause analysis and a working exploit, GLM-5.3 more than doubled from 24.4% to 54.4%, though Mythos 5 (78.0%) stays ahead. On ExploitGym, Z.ai reports 105 tasks completed in a 2-hour budget and 130 in 6 hours, versus 29 and 39 for GLM-5.2 — again behind Mythos 5 (181 and 247). Z.ai frames the cyber capability as an emergent property of scaled post-training — "capability kept compounding as training scaled," in the company's words — rather than a deliberate target.

Z.ai adds a real-world claim to go with the benchmarks: in testing with security teams, GLM-5.3 identified 2,436 vulnerabilities across 269 open-source projects, 1,097 of them rated critical or high severity, with the oldest finding dating to 1981 and an average "lifespan" of 26.6 years. That is the most striking number in the announcement and also the least independently checkable. The company has paired the capability with a Security Disclosure Ledger for coordinated disclosure, a "trusted access" program that limits sensitive cyber functions to verified users, and an "Open Source Shield" initiative to continuously audit key open-source projects.

The baseline GLM-5.3 had to beat

GLM-5.2 is the reference point the whole story hangs on. It shipped in June 2026 as a 743B Mixture-of-Experts model with roughly 40B active parameters per token, a 1M-token context window, a 128K max output, an MIT license, and open weights on Hugging Face. Independently, Artificial Analysis' Intelligence Index puts GLM-5.2 at 53 — the highest open-weights score on the index until this week. GLM-5.3 now clears it by 7 points: Artificial Analysis measures GLM-5.3 at 60 on the same index (v4.1.1), tying Kimi K3 for the top open-weights position and landing it in the frontier band alongside closed flagships like Claude Fable 5 and GPT-5.6 Sol. That is the first independent number attached to GLM-5.3, and it is consistent with the direction — if not every detail — of Z.ai's own claims. On long-horizon coding, the OrcaRouter harness measures 77.9 on Terminal-Bench 2.1, while Z.ai's best-reported GLM-5.2 figure is 82.7, which would be the first open-weight score above 80 but is vendor-reported and unreproduced. The list price is $1.40 per million input and $4.40 per million output tokens.

The scoreboard above is the leak-era projection this page published before launch — the ">1T params (rumored)" row, the unconfirmed context and license, the projected AA index. The launch corrected the biggest cell: GLM-5.3 reuses the same 743B base as GLM-5.2, so there is no parameter jump. The context window is confirmed at 1M, and the license stays unconfirmed because the open weights have not shipped yet. The projected index cell — this page's own guess of ~57–60 — was the rare projection that came in on the nose: the real number is 60, and the open question now is what happens when that score is reproduced against the actual weights.

The capture above is the independent baseline GLM-5.3's claims are measured against. GLM-5.2 tops the open-weights leaderboard at an Artificial Analysis Intelligence Index of 53. The first test of whether GLM-5.3's post-training deltas move that number has now arrived: Artificial Analysis measures GLM-5.3 at 60 on the same index — tied with Kimi K3 for the open-weights lead, 7 points ahead of GLM-5.2, and reported by the lab as independently measured.

Pricing and availability

GLM-5.3 is priced identically to GLM-5.2: $1.40 per million input and $4.40 per million output tokens (¥8 / ¥28 in the domestic listing), with cached-input reads at $0.26 / ¥2 per million. Z.ai announced the API was open on August 19, and it is reachable through Z.ai's own API, ZCode, AutoClaw, the GLM Coding Plan, and several partner gateways. One behavior change matters for API callers: requests now require "thinking" enabled across three effort levels — low, high, and max — with no off switch, a breaking change for existing integrations.

Same price does not mean same bill. GLM-5.3 runs roughly 20% more tokens per task than GLM-5.2 did on the same workloads, which a cost-per-task reading puts at about $0.68 against GLM-5.2's $0.44 — still under Kimi K3 (about $0.84) and GPT-5.6 Sol (about $1.23). That per-task math is a derived estimate from observed token usage, not a vendor figure, but it is the number that decides whether the flat $1.40 / $4.40 rate card actually saves you money.

What the launch changes for you

For API callers already on GLM-5.2, the practical step is a model-name change, not a project: GLM-5.2 is OpenAI-compatible and the integration carries over, with the thinking-effort caveat above. For self-hosters, the timeline is now a date rather than a guess: Zhipu promised the weights "two weeks after release" on August 14, which lands on Friday, August 28, and the open question is whether the license stays permissive. For anyone comparing models in the DeepSeek V4 Pro, Qwen3.8-Max, Kimi K3, GPT-5.6 Sol, and Claude Fable 5 tier, GLM-5.3 is now a live, independently scored variable in that ranking instead of a rumor.

The launch-day argument around GLM-5.3 is that the coding frontier has converged: for most everyday tasks, the story goes, few users can reliably tell GPT-5.6 Sol, Claude Fable 5, Kimi K3, GLM-5.2, and Qwen3.8-Max apart. If that convergence is real, the deciding factors stop being raw capability and become price, openness, and switching cost — which is exactly the corner GLM-5.3 is staking out at $1.40 / $4.40 per million on a self-hostable 743B base with weights confirmed for August 28. Whether coding models are genuinely interchangeable is an opinion, not a benchmark; the prices, the parameter count, and the weight date are not.

On the routing side, GLM-5.3 went live on OrcaRouter on August 18, the same day Z.ai's API opened — at the first-party list price, $1.40 / $4.40 per million, passed through with zero markup. The screenshot below shows GLM-5.2's page, which is exactly the shape GLM-5.3 now has: same price, same 1M-token context, same 128K max output. Routing a slice of real traffic to GLM-5.3 with automatic failover to GLM-5.2 or another proven model is a configuration change, not a rewrite — same key, no second contract. If the new model regresses on your workload, the router falls back before a page turns, and you get a quality signal on your own traffic instead of a vendor's slide. For a model whose flagship claims are still mostly vendor-reported, that is the low-risk way to find out for yourself.

What to watch next

• The weights, on Friday, August 28, and the license line on the model card — permissive MIT like GLM-5.2, or something narrower. Zhipu's cyber-safety hardening is the stated reason for the two-week delay, and the "trusted access" program suggests some functions will be gated regardless.

• Whether the cyber claims hold up outside Z.ai's own harness. The 2,436-vulnerability real-world claim and the CyberGym lead are the numbers independent labs will probe first; the AA Intelligence Index measures general capability, not security.

• Where the index lands once the weights are out. The 60 is scored against the served API; the self-hosted version, with a license attached, is the one teams will actually redeploy.

• DeepSeek V4 Flash's announced price increase, which sets the pricing envelope GLM-5.3 is being judged against, and GPT-5.6 Sol's one-point lead at 61.

FAQ

Is GLM-5.3 a bigger model than GLM-5.2?

No. Z.ai says GLM-5.3 uses the same 743-billion-parameter Mixture-of-Experts base as GLM-5.2, with the same 1M-token context window and roughly 40 billion active parameters per token. All reported gains come from scaled post-training, not a parameter increase — which directly corrects the leak-era rumor of a base above one trillion. That architecture claim is Z.ai's own and has not been independently audited.

When will GLM-5.3's open weights be available?

Friday, August 28. Zhipu promised the weights "two weeks after release" when it announced GLM-5.3 on August 14, and said "next Friday" when the API went live on August 19 — both readings land on the same date. The license has not been confirmed, and Z.ai has said sensitive cyber functions will be restricted to a verified-user "trusted access" program.

How should I treat the benchmark numbers?

Split the list. The coding jumps — Terminal-Bench 3.0 at 28.3, DeepSWE v1.1 at 66.9, SWE-Marathon at 42.5 — and the cyber results — CyberGym 84.5%, ExploitBench 54.4% — all come from Z.ai's own announcement and remain vendor-reported until an independent harness reproduces them. The Artificial Analysis Intelligence Index of 60 is the first independent measurement, and it is the number to weigh against everything Z.ai claims.

The leak was real, and the launch confirmed the name, the framing, and the timing — while correcting the one specific the rumor mill got loudest about. GLM-5.3 is the same base, post-trained hard, and now it carries an independent score to hold its vendor claims against: 60 on the Artificial Analysis Intelligence Index, tied with Kimi K3, seven ahead of GLM-5.2. The coding and cyber numbers are still Z.ai's own, the weights land on August 28, and the license line and a genuinely independent probe of the security claims are what's left to settle. Until then, the low-risk way to form your own view is a slice of real traffic and a failover to something proven.