GLM-5.3
An open-source large language model released by Zhipu AI, sharing the same base as GLM-5.2 but achieving significant improvements in programming capabilities through post-training. It supports local deployment via GGUF quantization format.
Core Facts
MLExpert raised the base model's average medal rate on MLE-Bench Lite from 37.9% to 72.7%.
vLLM provides Day-0 support for GLM-5.3, enabling developers to deploy the model on the day of its release.
The actionable method for validating a coding model is to select a real repair task with tests, fix the repository version and tool permissions, have candidate models complete the same task, and compare first-pass rate, patch readability, per-run cost, and number of human takeovers.
Timeline (last 90 days)
GLM-5.3 and GLM-5.3 Flash are two specifications of the GLM series: the former has more complete capabilities and greater parameter count and inference overhead; the latter has undergone distillation or structural simplification, has lower inference cost, and is suitable for high-concurrency or resource-constrained deployments.
Zro released two uncensored model variants, Dolly 1 Security and Dolly 2 Security, based on GLM-5.3 Flash and GLM-5.3 respectively for abliteration processing.
The score of open-source models with released weights (32 points) trails the leader by 26 points and is lower than Claude Haiku 5.5 (max tier, 36 points).
Among open-source models with released weights, DeepSeek V4.1 Flash 1017 (max tier) and GLM-5.3 (max tier) lead in a tie at 32 points.
Cloudflare's GLM-5.3 Workers AI endpoint was reported to have returned other customers' data in some long-prompt responses.
A total of 155 long-prompt responses showed cross-user content leakage.
A discussion thread about the GLM-5.3 Workers AI model appeared on Hacker News, titled "Cross-request KV cache exposure", receiving 155 replies, 15 points and 5 comments.
According to the analysis in the post, as a relatively new model version, GLM-5.3's serving stack may not yet be fully polished for edge scenarios.
No doubts about the GLM-5.3 model weights or training data appeared in the discussion; the focus remained on the inference service's cache management.
Some commenters pointed out that cross-request KV cache issues like this are not unique to GLM-5.3, and that any inference framework using a shared cache pool has a theoretical possibility of out-of-bounds access.
40 more timeline events
All Facts (20)
MLExpert raised the base model's average medal rate on MLE-Bench Lite from 37.9% to 72.7%.
75%VerifiedvLLM provides Day-0 support for GLM-5.3, enabling developers to deploy the model on the day of its release.
75%VerifiedThe actionable method for validating a coding model is to select a real repair task with tests, fix the repository version and tool permissions, have candidate models complete the same task, and compare first-pass rate, patch readability, per-run cost, and number of human takeovers.
75%VerifiedZhipu released GLM-5.3, claiming its coding capability improved by 50% compared to GLM-5.2.
65%UnverifiedGLM-5.3 and GLM-5.2 use the same base, architecture, total parameters, and activated parameters, with the only variable being one month of long-horizon context extension and reinforcement learning.
70%UnverifiedGLM-5.3 scored 84.5 on the CyberGym security capability benchmark.
60%UnverifiedGLM-5.3 and GLM-5.3 Flash are two specifications of the GLM series: the former has more complete capabilities and greater parameter count and inference overhead; the latter has undergone distillation or structural simplification, has lower inference cost, and is suitable for high-concurrency or resource-constrained deployments.
50%UnverifiedThe score of open-source models with released weights (32 points) trails the leader by 26 points and is lower than Claude Haiku 5.5 (max tier, 36 points).
50%UnverifiedAmong open-source models with released weights, DeepSeek V4.1 Flash 1017 (max tier) and GLM-5.3 (max tier) lead in a tie at 32 points.
50%UnverifiedA total of 155 long-prompt responses showed cross-user content leakage.
50%UnverifiedNo doubts about the GLM-5.3 model weights or training data appeared in the discussion; the focus remained on the inference service's cache management.
50%UnverifiedAccording to the analysis in the post, as a relatively new model version, GLM-5.3's serving stack may not yet be fully polished for edge scenarios.
50%UnverifiedAccording to developer analysis in the Hacker News discussion, GLM-5.3 in Workers AI may have caused different requests to share cache content that should not have been shared, due to cache key design or gaps in request isolation.
50%UnverifiedThe post itself did not disclose specific reproduction steps or the scope of impact.
50%UnverifiedSome commenters pointed out that cross-request KV cache issues like this are not unique to GLM-5.3, and that any inference framework using a shared cache pool has a theoretical possibility of out-of-bounds access.
50%UnverifiedMiMo-V2.6-Pro (1T parameters) scores 46, while DeepSeek V4.1 Flash 1017 (552B) and GLM-5.3 (753B) both score 45.
50%UnverifiedIn Cursor's coding evaluation, GLM-5.3 received the highest score of 42.6% in the open-weight model category.
50%UnverifiedZhipu's GLM-5.3 model is now available on Cursor.
50%UnverifiedIt is suggested that developers add GLM-5.3 to their candidate models in Cursor and make the decision on whether to switch their main model only after doing A/B comparisons with real projects.
50%UnverifiedThe author judges that GLM-5.3 took only 3 minutes 15 seconds to complete the round of verifying the result.
50%