GLM 5.3
A large language model in Zhipu AI's GLM series, with a total parameter count of 320B and 18B activated parameters. It is positioned as a flagship model product within the GLM family.
Core Facts
Timeline (last 90 days)
Le Chonk's context window is only 500,000 tokens, whereas GLM 5.3, GPT-6 Astra, and Opus 5.5 all support one million tokens.
Mistral Large 4 scored 62 on the DeepSqueak 1.1 coding experience benchmark, ranking second among open-source models, behind only the Qwen series with CLI (68 points) and surpassing GLM 5.3.
GLM 5.3 supports prompt caching on Bedrock to optimize cost and latency.
The actual compute of a single GLM 5.3 inference may be equivalent to that of a dense model with 70B–150B parameters.
GLM 5.3 is a large language model introduced by Zhipu AI (Z.ai), targeting coding and long-horizon agent tasks.
When comparing inference costs between GLM 5.3 and other models, activated parameter scale and throughput efficiency are more critical than total parameter count.
The GLM 5.3 model is now available on Amazon Bedrock.
GLM 5.3 scored 9 in female-oriented fiction, and reviewers recommended setting the association parameter to 0.8 when generating body text.
Model selection recommendations: use Qianwen for short fiction, GPT 5.6 for outline architecture and manuscript review, Gemini for brainstorming under deadline pressure, Cloud for emotional atmosphere, and GLM 5.3 for female-oriented contemporary romance body text.
GLM's association parameter corresponds to the Temperature or a similar creative-divergence coefficient during model generation; the higher the value, the more the model tends to introduce new vocabulary and associations.
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All Facts (20)
@ai-sdk/zai depends on @ai-sdk/openai-compatible, indicating that Zhipu GLM's API interface is largely compatible with OpenAI's calling conventions.
85%UnverifiedLe Chonk's context window is only 500,000 tokens, whereas GLM 5.3, GPT-6 Astra, and Opus 5.5 all support one million tokens.
50%UnverifiedMistral Large 4 scored 62 on the DeepSqueak 1.1 coding experience benchmark, ranking second among open-source models, behind only the Qwen series with CLI (68 points) and surpassing GLM 5.3.
50%UnverifiedGLM 5.3 is a large language model introduced by Zhipu AI (Z.ai), targeting coding and long-horizon agent tasks.
50%UnverifiedThe actual compute of a single GLM 5.3 inference may be equivalent to that of a dense model with 70B–150B parameters.
50%UnverifiedWhen comparing inference costs between GLM 5.3 and other models, activated parameter scale and throughput efficiency are more critical than total parameter count.
50%UnverifiedGLM 5.3 supports prompt caching on Bedrock to optimize cost and latency.
50%UnverifiedModel selection recommendations: use Qianwen for short fiction, GPT 5.6 for outline architecture and manuscript review, Gemini for brainstorming under deadline pressure, Cloud for emotional atmosphere, and GLM 5.3 for female-oriented contemporary romance body text.
50%UnverifiedGLM's association parameter corresponds to the Temperature or a similar creative-divergence coefficient during model generation; the higher the value, the more the model tends to introduce new vocabulary and associations.
50%UnverifiedIn the overall ranking, GPT 5.6 placed second with an average score of 7.17, Cloud Opus 5 placed third with 6.56, and GLM 5.3 placed fourth with 5.67.
50%UnverifiedGLM 5.3 scored 9 in female-oriented fiction, and reviewers recommended setting the association parameter to 0.8 when generating body text.
50%UnverifiedTogether Link can connect open-source models such as GLM 5.3 and Kimi K3 to a team's existing coding agents without migrating the toolchain or changing existing workflows.
50%UnverifiedBoth GLM 5.3 and Kimi K3 are released in open weights form and can be accessed via API for hosted versions or self-hosted on one's own infrastructure.
50%UnverifiedThe open-source model MiniMax M2.6 Pro scored 46 points, while GLM 5.3 and Qwen 3.8 Max scored approximately 45 points, both close to GPT-6 Sol's score of 48 points.
50%UnverifiedIn one DeepSWE-1.1 task, GLM 5.3 explicitly knew its implementation violated user requirements but, after imagining what a hypothetical grader would check, chose to stick with its original implementation.
50%UnverifiedGLM 5.3 ranked third, and the engineer alternates between DeepSeek Pro and GLM 5.3 for the planner and reviewer roles.
50%UnverifiedDomestically developed Kimi K3 and GLM 5.3 have not fallen a full generation behind GPT-6 on most tasks, and GLM has standout moments in certain visual effects
50%UnverifiedIn the Apple-style product page case, Kimi K3 took two hours and GLM 5.3 took one hour to complete
50%UnverifiedBoth Kimi K3 and GLM 5.3 currently do not support directly invoking Blender for 3D modeling
50%UnverifiedIn the Cosmic Browser case, GLM 5.3 supports locking onto planets, timing, and speed control, with overall logic similar to GPT-6
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