GLM-5.3

GLM-5.3

Quick answer: GLM-5.3 is Zhipu AI's August 2026 update to GLM-5.2 — same underlying base model, entirely scaled post-training. It scores 88.2% on Terminal-Bench 2.1, 28.3% on the new Terminal-Bench 3.0, 84.5% on CyberGym, 73.0% on Toolathlon Verified, and 1,769 Elo on GDPval-AA v2. Zhipu describes it as the most capable open-weights model for coding once weights ship (~2 weeks post-launch), and it shows a sharp, unexpected jump in cybersecurity/vulnerability-discovery capability versus GLM-5.2.

At a Glance

Where GLM-5.3 leads

• 88.2% Terminal-Bench 2.1 and open-source SOTA on the newer Terminal-Bench 3.0 (28.3%)
• 84.5% CyberGym — state of the art on this vulnerability-discovery benchmark, ahead of Claude Opus/Fable-class closed models cited in Zhipu's own comparison
• Large generational jump over GLM-5.2: Terminal-Bench 3.0 4.6%→28.3%, DeepSWE v1.1 46.2%→66.9%, ALE 23.8→28.5 (Zhipu's own reported deltas)
• Emergent exploitation-chain reasoning: found 2,436 vulnerabilities (1,097 medium/high severity) across 269 real-world open-source projects during internal red-team testing, tracked publicly via Zhipu's Security Disclosure Ledger
• Same underlying ~743–753B-parameter base as GLM-5.2 — all gains are from post-training scaling (more RL environments, more compute), not a new pretrain

Where it lags

• Weights are not yet public as of this writing — currently API-only via Z.ai; open release promised ~2 weeks after the Aug 14, 2026 launch, pending safety review
• Still behind the closed frontier on the hardest exploitation-chain cyber benchmarks (e.g., ExploitBench, ExploitGym) versus the top closed models Zhipu cites in its own comparison
• Text-only; no vision
• Thinking can no longer be disabled — GLM-5.3 requires thinking.type: "enabled" (with low/high/max effort), a breaking API change from GLM-5.2

Best for: Coding agents and long-horizon agentic coding tasks (via Z.ai's Coding Plan/ZCode or the Claude Code/OpenCode integrations); security teams evaluating vulnerability-discovery capability; teams already on GLM-5.2 planning to upgrade once open weights ship.

What GLM-5.3 Is

GLM-5.3 is Zhipu AI's (Z.ai) August 2026 release, positioned explicitly as a post-training-only upgrade: "Scaling post-training is all we did for GLM-5.3." It reuses the same base model as GLM-5.2 and layers on more reinforcement-learning environments, more diverse long-horizon tasks, and more compute spent training on them — built on the same IndexShare (long-context), SAO (long-horizon RL), and slime (async large-scale training) stack introduced with GLM-5.2.

The headline result is a large jump in coding-agent capability: Zhipu reports a 50% improvement over GLM-5.2 on its in-house Z.ai Code Bench, plus open-source state-of-the-art results on the public Terminal-Bench 3.0 and Agents' Last Exam benchmarks. Notably, GLM-5.3 achieves this while using fewer output tokens than GLM-5.2 at every effort level (e.g., 34.5% at ~75K tokens/task at Max effort, versus GLM-5.2's 23.4% at 96K).

A second, unplanned result is a sharp rise in cybersecurity capability. Zhipu added vulnerability-discovery data/environments to post-training expecting incremental gains, but found the model developed the ability to reason across multiple stages of a full exploitation chain — not just spot isolated flaws. On CyberGym it reaches 84.5% (from 77.2% for GLM-5.2), the best published result on that benchmark in Zhipu's own comparison. The gains are largest on exploitation-chain benchmarks further from the frontier's current strength (ExploitBench more than doubled, from 24.4% to 54.4%), though GLM-5.3 remains behind the closed frontier there. Zhipu also ran the model against real-world codebases with several security teams in China, surfacing 2,436 vulnerabilities (1,097 medium-to-high severity) across 269 open-source projects — some undiscovered for decades — now tracked via the public Z.ai Security Disclosure Ledger.

Weights are not yet released. Zhipu states they'll ship "in two weeks after launch, once safety evaluation and hardening are complete" — consistent with this tracker's original watchlist note (~Aug 28, 2026 target). Until then, GLM-5.3 is available only via the Z.ai API and GLM Coding Plan/ZCode.

Specifications

Pricing

Available now via the Z.ai API and the GLM Coding Plan (points-based quota, 50% off-peak discount outside 14:00–18:00 UTC+8 on weekdays). Refer to Z.ai's pricing page for current per-token rates. Self-hosting will become possible once open weights ship (~Aug 28, 2026).

Public Benchmark Scores

Additional benchmarks reported by Zhipu, not yet on Benchgen's leaderboard (either no existing benchmark page/model to compare against, or the reported number uses a variant/version/harness that isn't directly comparable to an existing page — see notes):

• ProgramBench "Almost Solved": 19.0% — this is a distinct partial-credit metric from ProgramBench's canonical "% accuracy" score already tracked on Benchgen (e.g., Kimi K3 scores 77.8% on the canonical metric vs. 17.5% Zhipu-reported "Almost Solved" for the same model) — not directly comparable, so no leaderboard entry was added.
• FrontierSWE: 78.1% "Dominance score" (evaluated by Proximal) — Zhipu's own comparison shows this specific evaluation round excluding Kimi K3/Qwen3.8 Max entirely (shown as "—" even though both have existing FrontierSWE entries here from other eval rounds), suggesting a distinct evaluation protocol; not added as a leaderboard entry pending reconciliation.
• SWE-Marathon (v1.1): 42.5% — a version-tagged variant with scores that don't cleanly match existing SWE-Marathon entries (e.g., Kimi K3 48.1% here vs. 42% on Benchgen's existing page); not added pending version reconciliation.
• PostTrainBench: 39.8% — scores reported here don't closely match existing PostTrainBench entries for the same models; not added pending reconciliation.
• AutomationBench v1.0.6: 48.2% — this version includes a bug fix (null-type handling, PR #13) that measurably raises scores across models versus Benchgen's existing AutomationBench entries (e.g., Kimi K3 46.7% here vs. 30.8% on the existing page) — not directly comparable, so no leaderboard entry was added.
• Agents' Last Exam (ALE-CLI): 28.5 — this specific CLI-harness evaluation protocol produces materially different scores than Benchgen's existing Agents' Last Exam entries for the same models (e.g., Qwen3.8 Max scores 27.0 here vs. 52.4% on the existing page) — treated as a distinct harness variant, not added as a leaderboard entry.
• HLE w/ Tools: 62.5% — a tool-augmented variant of Humanity's Last Exam that scores substantially higher than the standard (no-tools) protocol already tracked on Benchgen (e.g., Qwen3.8 Max scores 56.2% here vs. 43.6% on the existing page) — treated as a distinct variant, not added as a leaderboard entry.
• Z.ai Code Bench: an in-house, non-public benchmark (50% improvement over GLM-5.2 claimed) — no independent documentation, prose-only per established convention for proprietary in-house evals.

GLM-5.3 vs Alternatives

GLM-5.3 is a substantial upgrade over its own predecessor across every listed benchmark (e.g., CyberGym 77.2%→84.5%, DeepSWE 46.2%→66.9%), and is competitive with (though not always ahead of) Kimi K3 and Qwen3.8 Max on coding/agentic tasks. Zhipu's own broader comparison (not independently reproduced here) also places GLM-5.3 behind top closed-frontier models (Claude-class, GPT-5.6 Sol) on the hardest cybersecurity exploitation benchmarks, while leading on CyberGym specifically.

Related Models & Benchmarks

• Compare: GLM-5.2 · Kimi K3 · Qwen3.8 Max
• Benchmarks: TerminalBench 2.1 · Terminal-Bench 3.0 · DeepSWE · CyberGym · Toolathlon-Verified · GDPVal-AA v2

Frequently Asked Questions

Specs from Zhipu AI's official GLM-5.3 launch blog (z.ai/blog/glm-5.3, Aug 14, 2026) and Benchgen research. Last updated 2026-08-24.