GLM-5.3: Coding and Cyber Upgrade on the 753B Base, No Retraining

TL;DR

Last updated August 28, 2026. Weights status and benchmarks get updated as they land.

GLM-5.3 is Z.ai's (Zhipu AI's) upgrade of GLM-5.2, released August 14, 2026. Same 753B mixture-of-experts base, no new pretraining; scaled post-training moves Terminal-Bench 3.0 from 4.6 to 28.3, DeepSWE v1.1 from 46.2 to 66.9, and CyberGym from 77.2% to 84.5%. Independently, Artificial Analysis scores it 60 on the Intelligence Index, #2 of 111 open-weights models, tying Kimi K3 at about one-fifth the price. API pricing is unchanged at $1.40/M input and $4.40/M output with a 1M-token context. A public ModelOpt NVFP4 checkpoint is now available, and Morph serves it as morph-glm53-744b.

What Is GLM-5.3?

GLM-5.3 is the model Z.ai shipped on August 14, 2026 under the tagline "Built to Code. Ready for Cyber Defense." It is not a new base model. Z.ai took the 753B-total mixture-of-experts network behind GLM-5.2 and ran a much larger post-training program against agentic coding, long-horizon tool use, and vulnerability research, then shipped the result as a new model id, glm-5.3, at the same price. Context stays 1M tokens with 128K max output. Reasoning is always on and cannot be disabled; effort levels are low, high, and max, with max the default.

The release-week storyline had two threads. First, the post-training claim itself: a 6x jump on Terminal-Bench 3.0 without touching pretraining is a statement about where capability gains currently come from. Second, the cyber positioning: Z.ai says it pointed the model at 269 open-source projects and found 2,436 vulnerabilities, and it stood up a public coordinated-disclosure ledger at cvd.z.ai. The launch hit the Hacker News front page at 1,170 points ("GLM-5.3: Frontier coding with emergent cyber capabilities").

Morph serves the public incoai/GLM-5.3-NVFP4 checkpoint as morph-glm53-744b at $1.25/M input, $0.26/M cached input, and $4.40/M output with the full 1M context. See Morph Open Source Models. Prefix caching is automatic with no write surcharge, and a per-conversation session key keeps every turn on the worker that holds its prefix; the mechanics and a 30-turn cost example are on the prompt caching page. For how GLM-5.3 sits against GLM-5, GLM-5.1, GLM-5.2, and GLM-5.3-Flash, see the GLM 5 family guide.

GLM-5.3 vs GLM-5.2: What Actually Changed

The honest framing: same body, new training. Z.ai did not retrain the 753B base; it scaled post-training. That makes the deltas below unusually clean as a measurement of what post-training alone buys on a fixed base.

Note the Terminal-Bench version: 3.0 is a harder revision than the 2.1 where GLM-5.2 scored 81.0, which is why GLM-5.2 shows 4.6 here. The 4.6 → 28.3 delta is the meaningful number, not a cross-version comparison. If you are choosing between the two models today: the API costs the same, so on Z.ai there is no reason to prefer 5.2. The reason 5.2 still matters is that its weights are out (MIT) and cheaper hosts serve it; Morph runs it at $1.25/$4.40.

Benchmarks: Vendor Numbers and the Independent Check

Nearly all launch benchmarks are Z.ai's own. The one substantial independent datapoint so far is Artificial Analysis: Intelligence Index 60, #2 of 111 open-weights models, measured output speed 71.6 tokens per second. That ties Kimi K3 (60) and clears GLM-5.2 (53). No SWE-bench Verified figure has been published for GLM-5.3 by anyone; if you see one quoted, it is invented.

On Z.ai's internal Code Bench the claim is efficiency, not supremacy: 31.4% at roughly 50K output tokens per task, which Z.ai charts ahead of Claude Opus 4.8 (29.5% at 120K tokens) and behind Claude Fable 5 (39.5% at max effort). Beating Opus 4.8 at less than half the tokens is the pitch; staying a tier below Fable 5 is the fine print. Treat the whole chart as vendor-internal until someone replicates it.

The Cyber-Defense Story

The differentiated part of the release is not the coding delta, it is the vulnerability-research program built around the model. Z.ai reports CyberGym at 84.5%, which it says edges Claude Mythos 5 (83.8%) and GPT-5.6 Sol (83.6%), and ExploitBench at 54.4%, more than doubling GLM-5.2's 24.4% while still trailing the closed frontier (Mythos 5 posts 78.0%). In Z.ai's ExploitGym harness, GLM-5.3 solved 105 tasks in 2 hours and 130 in 6, versus 29 and 39 for GLM-5.2.

Applied, Z.ai says the model surfaced 2,436 vulnerabilities across 269 open-source projects, 1,097 rated critical or high, the oldest introduced in 1981. Disclosure runs through a public ledger at cvd.z.ai, which listed 53 public CVEs at launch with 2,383 still embargoed. This program is also the stated reason the weights shipped late: Z.ai held them back for safety hardening rather than releasing day-one alongside the API.

GLM-5.3 API: Pricing and Access

Morph serves the public NVFP4 checkpoint through its OpenAI-compatible API as morph-glm53-744b. The Morph rate is $1.25/M input, $0.26/M cached input, and $4.40/M output with a 1M-token context. Z.ai's first-party model id is glm-5.3, and OpenRouter lists the model as z-ai/glm-5.3.

Call the canonical Morph model id through the standard OpenAI-compatible endpoint:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.morphllm.com/v1",
    api_key="YOUR_MORPH_API_KEY",
)

resp = client.chat.completions.create(
    model="morph-glm53-744b",
    messages=[
        {"role": "user", "content": "Refactor this module and explain the change."},
    ],
)
print(resp.choices[0].message.content)

Where Are the Weights?

Public since August 25, 2026. Z.ai published zai-org/GLM-5.3 on Hugging Face eleven days after the API launch, not gated, under a custom "GLM-5.3 License" rather than MIT: the terms are MIT-like, plus a clause requiring model-as-a-service operators with more than $10 billion in aggregate revenue over any 12-month period to pass a Z.ai security review before commercial use. Read the LICENSE file before building a hosted product on it.

The production checkpoint is public at incoai/GLM-5.3-NVFP4 on Hugging Face under the GLM license. It is a ModelOpt NVFP4 quantization of the Z.ai GLM-5.3 base, with NVFP4 experts and FP8 KV cache, packaged for tensor-parallel-8 serving. Morph pins the immutable checkpoint revision used by morph-glm53-744b.

The NVFP4 checkpoint is about 465 GB across 87 weight shards. That still demands a serious serving box: Morph runs the full model across eight GPUs and preserves the optimized parallel and cache paths rather than falling back to a reduced configuration.

GLM-5.3 vs Kimi K3

The clean comparison, because it is independent: Artificial Analysis scores both GLM-5.3 and Kimi K3 at 60 on the Intelligence Index. The separation is elsewhere. GLM-5.3 costs $1.40/$4.40 per M tokens against K3's $3/$15, roughly one-fifth the output rate. K3 counters with native vision and with weights that are already public (released July 27, 2026 under the Kimi K3 License), while GLM-5.3's went public on August 25 under the custom GLM-5.3 License, so both are servable by any host today. Both models run always-on reasoning and both are verbose, so per-task cost depends on effort caps more than sticker price.

For the K2/K3 lineage in depth, see Kimi K3 and the Kimi K3 API guide. For the GLM lineage, GLM-5.2 and the GLM-5.2 API guide.

GLM-5.3: Pros and Cons

When to Use GLM-5.3 (and When Not)

Frequently Asked Questions

What is GLM-5.3?

Z.ai's August 14, 2026 upgrade of GLM-5.2: the same 753B MoE base improved entirely through scaled post-training, aimed at agentic coding and cyber defense. 1M context, 128K max output, always-on reasoning, $1.40/$4.40 per M tokens.

Is GLM-5.3 a new model or a fine-tune?

Neither a new base nor a light fine-tune: same 753B network, but a much larger post-training program. The deltas (Terminal-Bench 3.0 4.6 to 28.3) are what post-training alone bought on a fixed base.

How much does the GLM-5.3 API cost?

$1.40/M input, $0.26/M cached input, $4.40/M output on Z.ai, identical to GLM-5.2, with the 1M context included. Also bundled in the GLM Coding Plan from $18/month.

When do GLM-5.3 weights come out?

A public ModelOpt NVFP4 checkpoint is available now at incoai/GLM-5.3-NVFP4. Morph serves that pinned checkpoint as morph-glm53-744b.

GLM-5.3 or Kimi K3?

They tie at 60 on the independent AA Intelligence Index. GLM-5.3 is about one-fifth the price; K3 adds native vision. Morph serves both public checkpoints on tuned kernels: GLM-5.3 and Kimi K3.