NEW · GLM-5.3-FlashX launched
GLM-5.3-FlashX (launched 2026-09) is the high-speed edition of GLM-5.3-Flash: inference reaches 200 tokens/s — faster and smoother. Like Flash, it is the first natively multimodal model of the GLM-5 family — 320B total / 18B active parameters, with hybrid sparse + linear attention, supporting video / image / text / file input, with 1M context and 128K max output.
GLM-5.3-Flash released
GLM-5.3-Flash (released Aug 2026) is the first natively multimodal model of the GLM-5 family: vision built into the coding loop, accepting video / image / text / file input. It scores 57 on Artificial Analysis’ Intelligence Index — #4 globally, on par with Claude Opus 4.8 — at 1/40 the price.
GLM-5.3 released
GLM-5.3 (officially released 2026-08-14) is Zhipu's flagship MoE model. It reuses the exact GLM-5.2 base with no new pre-training data — all gains come from large-scale post-training. It targets long-horizon code engineering, terminal agents, and source-code vulnerability auditing. Motto: Built to Code. Ready for Cyber Defense.
GLM-5.3 is the flagship open-source GLM AI model: built on the GLM-5.2 base with fully upgraded post-training, a 50% gain in coding experience, 84.5% on CyberGym (currently leading) and a 6x jump on Terminal-Bench 3.0; its sibling GLM-5.3-Flash delivers frontier intelligence at 1/40 the price. Compare every GLM model below — or try the free AI playground first.
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Four capabilities define the current generation of GLM models — all reported by the model authors and reproducible from open rights.
A one-million-token context that stays usable and high-quality: GLM-5.3 shares the GLM-5.2 base and keeps training on long-horizon coding-agent trajectories — large-scale implementation, automated research and complex debugging.
Non-thinking, high, and highest three levels allow you to weigh between delay and capability for tasks — on the agent programming evaluation, about 63% (~35K output tokens) to 74% (~83K).
Every four sparse attention layers share a lightweight indexer, reducing the FLOPs per token by 2.9 times under a context length of one million, while maintaining long-distance quality without loss.
Open-source GLM models under the MIT license: weights are published on HuggingFace and ModelScope, without regional restrictions, and support local deployment through transformers, vLLM, SGLang, xLLM, and ktransformers.
Data below is reported by Zhipu’s official docs and the independent evaluator Artificial Analysis. glmmodel.cn only reports these results and does not run the evaluations.
Source-code vulnerability discovery
Agentic coding: higher accuracy with fewer tokens
Terminal agent tasks (GLM-5.2 scored 4.6)
GLM-5.3 surpasses GLM-5.2 on Terminal-Bench 3.0 (4.6→28.3), DeepSWE v1.1 (46.2→66.9) and Agents’ Last Exam (23.8→28.5), reaching open-source SOTA; it more than doubles GLM-5.2 on ExploitBench (54.4%). GLM-5.3-Flash scores 57 on the Artificial Analysis Intelligence Index (#4 globally), on par with Claude Opus 4.8.
Published GLM model benchmark results, covering 17 evaluations of 8 models.
| Benchmark | GLM-5.3 | GLM-5.2 | Kimi K3 | DeepSeek-V4-Pro | Qwen3.8-Max | Opus 4.8 | Fable 5 | GPT-5.6 Sol |
|---|---|---|---|---|---|---|---|---|
| Coding | ||||||||
| 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 |
| Cyber | ||||||||
| 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 |
| Agentic | ||||||||
| 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 |
Source: Z.ai official blog (2026-08-14).
GLM models come in more than one speed. Effort controls let you spend compute only when needed. GLM-5.3 always thinks (low / high / max, default max); per-tier efficiency figures are GLM-5.2 reported values, for reference.
~35K average tokens
Scenarios where latency is more important than depth, such as quick editing, template code, and routine refactoring.
~43K average tokens
The default choice for most agent programming: approaching the highest quality with about half the token consumption.
~83K average tokens
Difficult long-term problems — allocate additional computing resources only when the task truly requires it.
The table below shows GLM-5.3 against comparison models, as officially published by Z.ai. GLM-5.3 shares the GLM-5.2 base — every gain comes from post-training. glmmodel.cn only reports these results.
Increasing the context limit from 200K to one million tokens is an engineering issue rather than a configuration parameter. Three modifications are key.
Every four Transformer layers share a lightweight indexer. It is located at the first layer among the four, and its top-k index is reused by the other three layers — eliminating the dot product and top-k calculation in three-quarters of the layers. Result: Each token's FLOPs is reduced by 2.9 times under a context length of one million. IndexShare was introduced starting from a sequence length of 128K during the training mid-phase.
The multi-token prediction layer runs the indexer once in the first draft step and reuses it in subsequent steps, eliminating the mismatch between the training/inference KV cache of the previous generation. Combined with rejection sampling and end-to-end TV loss, the accepted length increases from 4.56 to 5.47 — about 20% — across 7 MTP steps.
| Baseline | 4.56 |
| + IndexShare + KVShare | 5.10 |
| + Reject Sampling | 5.29 |
| + End-to-End TV Loss | 5.47 (+20%) |
After more than a few tens of thousands of tokens, the bottleneck is no longer computation, but the KV cache capacity, long context kernel, and CPU overhead. Three fixes: fine-grained memory management and parallelization based on LayerSplit, kernel optimization for context length scaling coordinated with the cache transfer pipeline, and CPU-side cache management, request scheduling, and runtime path optimization. The throughput advantage expands with the growth of context.
Normalized Throughput Advantage
The training stack (slime) combines white-box and black-box rollouts, compact trajectories, and sub-agent workflows. Parallel OPD training merges more than a dozen expert models in about two days, and FP8 KV caching ensures that rollout memory does not exceed the budget.
Long-term RL can induce shortcuts, so suspicious actions are first passed through a rule-based recall filter, and then checked for accuracy by the LLM judge. Confirmed malicious actions are intercepted online and responded with virtual tool results, allowing rollouts to continue rather than be discarded. Based on Critic's PPO, compressible sub-traces are maintained for training on a single rollout.
From 30B Flash level to a million-context flagship — choose a GLM model to view the complete specification table and benchmark data.
Built on the GLM-5.2 base with fully upgraded post-training: open-source SOTA on complex coding and agent tasks, currently leading at vulnerability discovery.
First natively multimodal GLM: vision in the coding loop, #4 globally on the AA Intelligence Index, Opus-4.8-level intelligence at 1/40 the price.
Legal-domain vertical model on GLM-5.3-Flash: 19.2% on the Harvey LAB legal benchmark at roughly 1/70 the cost of closed-source peers. Beta.
Previous flagship: one-million-token context, open-source SOTA for programming and long-horizon tasks.
Previous generation flagship: significantly improved long-term capabilities and engineering-level outputs that can work autonomously for several hours.
Stronger programming capabilities, more reliable multi-step execution, and better complex agent behavior.
Basic model, optimized for long chain and dynamic intelligent agent scenarios.
Enhanced programming capabilities, stable multi-step inference, and improved frontend generation.
300 billion parameters; high efficiency and performance, strong among models of the same scale.
Small-sized GLM model with excellent unit computing power performance.
Real-time speech-to-text recognition, CER as low as 0.0717.
Multi-file functionality beyond a single context window: the model keeps the entire repository state in view, rather than re-reading it every few rounds.
Several hours of experimental cycles, agents need to plan, run, read results, and correct — this is exactly the workload designed to be evaluated by FrontierSWE and PostTrainBench.
Complete call graphs, analyzer outputs, and keeping the kernel and system work on the same track as previous attempts are required.
Long-term reproducibility tracking, unstable testing, and cross-service failures; truncating the context is the reason for losing bugs.
All the above scenarios can be run on your own hardware — MIT weights, transformers · vLLM · SGLang · xLLM · ktransformers.How to run the GLM model locally →
Read the GLM model benchmark tests, and then let the real model start working. The sandbox above is free and requires no information; if you want a more complete AI toolkit, the free package from the partners starts here.