GLM-5.3 Flash
Zhipu AI's 320B/18B-active multimodal MoE model featuring hybrid linear attention, 1.31M context, and top-tier agentic coding efficiency.
Empirical Evaluation Results
Architectural Profile & Capabilities
GLM-5.3-Flash is a native multimodal Mixture-of-Experts (MoE) foundation model developed by Zhipu AI (Z.ai). Originally stress-tested in stealth as 'Ox Alpha', the model has emerged as a disruptive frontier entry in the late-2026 landscape, competing directly against lightweight frontier models like Google Gemini 3.8 Flash, Qwen 3.8 Flash-Next, and Anthropic Claude Sonnet 5. Built with 320 billion total parameters and routing 18 billion active parameters per token, GLM-5.3-Flash resolves the primary economic and infrastructure bottlenecks of ultra-long context reasoning through an architectural redesign that combines Gated Linear Attention (GLA) and sparse Multi-Head Latent Attention (MLA). This hybrid design slashes attention compute requirements by 3.01x and compresses runtime KV-cache memory footprints by 4.44x, enabling cost-effective serving across a 1,310,720 (~1.31M) token context window at disruptively low token pricing ($0.07/1M input, $0.2333/1M output). In empirical evaluations, GLM-5.3-Flash posts near-frontier software engineering results—achieving 63.4% on DeepSWE v1.1, 56.3% on SWE-bench Verified, 84.3% on Terminal-Bench 2.1, and 80.5% on LiveCodeBench—matching or exceeding heavyweight models like Claude Opus 4.8 and GPT-5.1 Codex at roughly 5% of their inference operating expenditure.
GLM-5.3-Flash (codenamed "Ox Alpha" during evaluation) is an open-weights native multimodal Mixture-of-Experts (MoE) model comprising 320B total parameters with 18B active parameters routed per token across 45 transformer layers. It incorporates a breakthrough hybrid sparse and linear attention architecture (GLA/KDA - Gated Linear Attention / Kernelized Decay Attention interleaved with sparse Multi-Head Latent Attention/MLA), achieving a 3.01x reduction in attention compute and a 4.44x compression in Key-Value (KV) cache memory footprint compared to conventional dense attention models. The architecture supports full native multimodal token processing (interleaved text and high-resolution vision) and dynamic test-time reasoning tokens for complex agentic rollouts across an ultra-long 1,310,720 (~1.31M) token context window.
Recommended Workloads & Primary Use Cases
- •Despite its 'Flash' moniker, generation throughput is moderate (~80-115 tps on standard deployments; up to 320 tps only on high-tier FP8 parallel nodes); 'Flash' primarily designates compute/cost efficiency and KV cache compression rather than raw inference speed.
- •Linear attention layers can experience minor associative precision decay on needle-in-a-haystack tasks situated beyond 800k tokens unless explicit system prompt grounding or retrieval-augmented routing is enforced.
- •Requires specialized inference runtime kernels (such as vLLM with Glm5NextTextLinearAttention support or NVIDIA Dynamo custom kernels) to realize the 4.4x KV cache compression and sub-100ms TTFT benefits.
- •Prompt formatting strictly requires adherence to Z.ai's chat template structure (including specific reasoning delimiters) to prevent premature termination of test-time thinking sequences during complex multi-step tool calls.
Calls to GLM-5.3 Flash are routed through strict Zero Data Retention inference channels. Prompts and outputs are never stored, indexed, or monitored by Z.ai.
More Models from Z.ai
Compare other engines available in this laboratory.
Z.ai's most capable model (released Aug 14, 2026).
Long-horizon agentic engineering, repository-scale refactors, multi-hour autonomous coding, cross-file/long-chain tasks, frontend coding (best-in-class among open models), tool use, and competition math.
GLM-5.1 is a high-performance foundation model engineered by Z.ai, accessible with Zero Data Retention on ARMES.