GLM 5.3 Flash Uncensored is an FP8 uncensored fine-tune of the efficient 320B mixture-of-experts reasoning model with restored vision support, built for unrestricted chat, creative writing, coding, agentic work, tool use, and long-context tasks.
Added Jul 29, 2026
Context Window
262.1K
Max Output
32.8K
Input Price (Auto)
$0.35/1M
Output Price (Auto)
$1.40/1M
Cache Read (Auto)
$0.17/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
No benchmark data is available yet for this model.
Providers
Choose explicit providers for this model. Auto routing remains available as the default option.
Loading provider options…
Related text models
Compare GLM 5.3 Flash Uncensored with similar models from the same provider or model family.
GLM 5.3 Flash
z-ai/glm-5.3-flashox-alpha out of stealth! GLM-5.3 Flash is Z.ai's first natively multimodal GLM-5 model, with 320B total parameters and just 18B active parameters for efficient coding, agentic work, and precise 1M-token context. Its hybrid sparse-and-linear attention architecture helps it outperform GLM-5.2 at one-tenth the price while approaching Claude Opus 4.8 on coding and agentic benchmarks.
GLM 4.7 Flash
z-ai/glm-4.7-flashGLM-4.7-Flash is a lightweight 30B model optimized for coding and agentic tasks. Balances high performance with efficiency.
GLM 4.7 Flash Original
z-ai/glm-4.7-flash-originalGLM-4.7-Flash is a lightweight 30B model optimized for coding and agentic tasks. Balances high performance with efficiency, perfect for local deployment.
GLM 4.7 Flash Original Thinking
z-ai/glm-4.7-flash-original:thinkingGLM-4.7-Flash with extended thinking capabilities for complex reasoning. Lightweight 30B model optimized for coding and agentic tasks.
GLM 4.7 Flash Thinking
z-ai/glm-4.7-flash:thinkingGLM-4.7-Flash with extended thinking capabilities for complex reasoning. Lightweight 30B model optimized for coding and agentic tasks.
GLM 4.6V Flash
z-ai/glm-4.6v-flash-originalGLM-4.6V-Flash (9B), a lightweight model optimized for local deployment and low-latency applications. Scales context window to 128k tokens and achieves SoTA performance in visual understanding among similar-scale models.