GLM-5.3 API on Run BiOS

GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It uses the same base model as GLM-5.2: every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks:

• Stronger coding. Z.ai describes GLM-5.3 as the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on its in-house Z.ai Code Bench and open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam.
• Emergent cyber capability. As post-training scaled, cyber capability developed faster than Z.ai expected: GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.

Architecture

GLM-5.3 shares GLM-5.2's base: a mixture-of-experts model with DeepSeek Sparse Attention (DSA). Published model configuration:

• Scale: 744B total parameters with 40B active per token (744B-A40B). BF16 weights.
• Context: a maximum position embedding of 1,048,576 tokens (1M context). Text input and output.
• Configuration: 78 hidden layers (the first 3 dense, the remainder sparse MoE), hidden size 6144, 256 routed experts with 8 routed experts active per token plus 1 shared expert, MoE intermediate size 2048, dense-layer intermediate size 12288, 64 attention heads, MLA-style attention with Q LoRA rank 2048 and KV LoRA rank 512, vocabulary size 154,880.
• Speculative decoding: one multi-token-prediction (MTP) layer.

Thinking and Reasoning Effort

Per the official model card:

• GLM-5.3 supports controlling the thinking budget through the reasoning_effort parameter, which accepts three levels: low, high and max. It defaults to max if not passed (or if set to any other value). To use low or high, pass them explicitly.
• For benchmark and leaderboard reproduction, Z.ai recommends keeping the default max.
• In the model's chat template, clear_thinking defaults to false. Z.ai recommends explicitly passing clear_thinking=true for chat scenarios.

Benchmarks

Full benchmark table as published in the official GLM-5.3 model card.

Benchmark	GLM-5.3	GLM-5.2	Kimi K3	DeepSeek-V4 Pro-0813	Qwen3.8-Max	Opus 4.8	Fable 5 (w/ fallback)	GPT-5.6 Sol
Terminal Bench 2.1	88.2	81.0	88.3	87.9	86.6	85.0	88.0	88.8
Terminal Bench 3.0	28.3	4.6	17.4	-	-	21.1	33.7	34.6
DeepSWE (v1.1)	66.9	46.2	67.5	62.7	56.6	58.0	69.7	72.7
NL2Repo	58.0	48.9	58.0	61.1	55.9	69.7	-	-
ProgramBench (Almost Solved)	19.0	9.5	17.5	-	10.5	15.5	33.0	23.0
FrontierSWE	78.1	67.5	-	-	-	66.5	88.2	-
SWE-Marathon (v1.1)	42.5	19.4	48.1	-	-	48.8	33.1	42.5
PostTrainBench	39.8	31.7	32.0	-	-	32.9	41.8	36.2
CyberGym	84.5	77.2	80.0	83.3	78.5	78.1	83.8	83.6
ExploitGym (2h / 6h)	105 / 130	29 / 39	36 / 70	-	14 / 26	80 / 120	181 / 247	216 / 293
ExploitBench	54.4	24.4	32.2	-	28.8	40.0	78.0	76.5
Toolathlon Verified	73.0	59.9	76.5	74.1	72.5	76.2	74.7	74.9
AutomationBench (v1.0.6)	48.2	26.2	46.7	43.2	39.8	41.0	46.2	45.8
Agents' Last Exam (ALE-CLI)	28.5	23.8	27.6	25.7	27.0	25.7	23.8	28.6
HLE w/ Tools	62.5	54.7	59.8	60.0	56.2	57.9	63.9	64.5
GDPval-AA v2	1769	1508	1682	1590	1739	1588	1743	1730

Evaluation Notes (as published by Z.ai)

• HLE w/ tools: sampling temperature=1.0, top_p=0.95, maximum generation length 163,840 tokens, maximum context length 300,000 tokens with a context management strategy. GPT-5.6-luna (medium) as the judge model.
• NL2Repo: temperature=1.0, top_p=1.0, max_new_tokens=64k under 1M context, with rule-based and LLM-based judgement to prevent malicious behaviors.
• DeepSWE: the mini-swe-agent harness with temperature=0.95, top_p=1.0, timeout=6h and 400K context.
• Terminal-Bench 2.1: evaluated in Claude Code 2.1.207 with temperature=1.0, top_p=1, max_new_tokens=65536 and a 6-hour timeout.
• Terminal-Bench 3.0: the Claude Code 2.1.207 harness (reasoning effort max, 400K context, 128K maximum output), avg@3 over three rollouts per task, each in an isolated container built from the task's official image, capped at 600 agent turns with a 10-hour timeout. Artifacts scored by the task's official separate verifier.
• Agents' Last Exam (CLI): the official evaluation protocol with the Claude Code harness (reasoning effort max, 1M context, 64K maximum output). Each of the 105 tasks in an isolated container with its Task Card resources. Scored by the official ALE evaluators.
• Toolathlon Verified: the official evaluation service, pass@1 averaged over 3 independent runs.
• AutomationBench: v1.0.6 with the null-type handling fix.
• GDPval-AA v2: evaluated by Artificial Analysis.
• CyberGym / ExploitGym / ExploitBench: Claude Code 2.1.207 at max reasoning effort with no web tools (temperature=1.0, top_p=1.0, max_new_tokens=128000), with a domain whitelist to prevent the agent from cheating. CyberGym is single-run Pass@1 over 1,507 tasks. ExploitGym is single-run Pass@1 on 869 tasks under 2-hour and 6-hour budgets rescaled by per-model tokens-per-second. ExploitBench averages coverage over 41 tasks across 3 revisions.
• FrontierSWE: 1M context length, max effort level, 128K maximum output tokens. Dominance score as of 2026/08/14.
• PostTrainBench / SWE-Marathon: Claude Code 2.1.207 at max effort with a 1M-token context window and 128K maximum output tokens (see the official card for the anti-cheat adjustments documented for individual tasks).

License

GLM-5.3 is released under the GLM-5.3 License: a permissive grant (use, modify, distribute, sell, run, deploy, fine-tune and create derivative works) with one condition beyond attribution: a licensee operating a Model-as-a-Service business whose aggregate revenue exceeds US$10 billion over any consecutive twelve months must pass Z.AI's security review before commercial use.

On Run BiOS

On the Run BiOS serverless catalog this model is served as glm-5.3 with its full 1,048,576-token context window, function calling, structured outputs, adjustable reasoning effort (exposed as a low / high / max ladder, matching the model's own three levels, the model's own default is max) and prompt caching for repeated prefixes.