GLM-5.3Next-Gen Open Frontier Intelligence

Enterto send

753B

Total MoE (48B Active)

千亿参数底座兼顾极速单 Token

1,000,000

Tokens Context Window

大海捞针 99.8% 无损召回率

58.4%

SWE-bench Verified

登顶全球公认开源代码推理第一

Apache 2.0

Commercial Open Source

支持全权私有化本地集群闭环部署

Production Workflows

Tackle Hard Real-World Engineering

Engineered for developers and architects dealing with multi-file codebases and massive documentation.

SWE-bench Top-Ranked Reasoning

Multi-File Dependency & Race Condition Hunter

Unlike single-file snippets, GLM-5.3 parses entire project dependency trees. It tracks mutex contention and asynchronous leaks across dozens of files, producing clean git patches.

Tracks AST symbol definitions across entire repo
Exports industry-standard unified diff patches
Generates accompanying unit regression tests

Launch Code Debugger

src/engine/worker_pool.rs (git diff)

GLM-5.3 Patch

@@ -42,7 +42,9 @@ impl WorkerPool {

- let guard = self.mutex.lock().unwrap();

+ // GLM-5.3 Fix: Replaced blocking mutex with try_lock + Tokio atomic wait

+ let mut guard = self.channel.try_acquire_owned()?;

+ tokio::task::spawn(async move { guard.process_queue().await });

self.active_tasks.fetch_add(1, Ordering::SeqCst);

// Result: 0 race conditions, zero deadlocks verified in cargo test

Benchmark Proof

Head-to-Head Frontier Benchmarks

Directly validated against top proprietary frontier models across industry-standard engineering benchmarks.

GLM-5.3 (753B MoE)

Claude Sonnet 5.5

OpenAI Codex (GPT-5)

Gemini 4 Argon

SWE-bench Verified

Real-world GitHub Bug Solving

🏆 #1

Resolves real GitHub issues and passes full regression suites. GLM-5.3 leads all open models globally.

GLM-5.358.4%

Claude Sonnet 5.557.2%

OpenAI Codex56.5%

Gemini 4 Argon53.8%

Terminal-Bench 3.0

Shell & DevOps Automation

🏆 #1

Measures autonomous shell execution, container debugging, and cluster orchestration in headless environments.

GLM-5.364.8%

Claude Sonnet 5.562.1%

OpenAI Codex63.8%

Gemini 4 Argon59.4%

CyberGym Security

Vulnerability Analysis & Reverse Eng

🏆 #1

Evaluates static binary disassembly, memory corruption analysis, and exploit patch generation.

GLM-5.378.2%

Claude Sonnet 5.577%

OpenAI Codex74.5%

Gemini 4 Argon72.3%

HumanEval+ (0-Shot)

Algorithmic Edge-Case Synthesis

Synthesizes error-free Python algorithmic solutions passing strict adversarial edge tests.

GLM-5.391.5%

Claude Sonnet 5.592.8%

OpenAI Codex91%

Gemini 4 Argon89.8%

MATH-500 (Complex Proof)

Olympiad-Grade Math Proofs

🏆 #1

Complex formal proofs across abstract algebra, combinatorics, and number theory.

GLM-5.392.4%

Claude Sonnet 5.590.2%

OpenAI Codex91.5%

Gemini 4 Argon90%

1M Needle-In-A-Haystack

1M Long-Context Retrieval

🏆 #1

Multi-layer key-value retrieval across full 1,000,000 token context without mid-document degradation.

GLM-5.399.8%

Claude Sonnet 5.598.6%

OpenAI Codex97.4%

Gemini 4 Argon99.6%

Dual Model Matrix

Choose the Perfect GLM-5.3 Model

From 753B MoE frontier reasoning to low-latency Flash deployment, built for production scale.

GLM-5.3 (753B MoE)

Flagship MoE · Frontier Reasoning & Code

Flagship Recommended

Total Parameters753 Billion

Active Parameters48 Billion / Token

Context Window1,000,000 Tokens

SWE-bench Verified58.4% (开源第一)

Inference Throughput~45 tokens/s (FP8)

Key Capabilities

1M context window: ingest entire full-stack repositories at once
Top-tier 58.4% SWE-bench Verified coding reasoning
Native autonomous sandboxed tool-calling (Bash, Docker, Python)
Open weights under Apache 2.0 with private on-prem deployment

Start with GLM-5.3

GLM-5.3-Flash (320B)

Ultra-Fast High Throughput · Low Latency

High Speed

Total Parameters320 Billion

Active Parameters32 Billion / Token

Context Window128,000 Tokens

Time-to-First-Token< 180ms

Inference Throughput140+ tokens/s

Key Capabilities

3x throughput with sub-180ms time-to-first-token response
128K context window for real-time document search and log parsing
Optimized distillation cutting deployment compute cost by 65%
Runs on standard 4x RTX 4090 (INT4) or dual 80GB accelerators

Start with Flash

Architectural Deep Dive

Built for True Production Scale

Every architectural breakthrough is purpose-built to solve real-world memory bottlenecks and complex logic.

Only 48B active per token

753B Sparse MoE Architecture

16 routed experts · 48B active per token

Routes each token dynamically to Top-2 specialized experts, maintaining 753B parameter world knowledge while slashing computation overhead and latency by 70%.

99.8% Needle-in-Haystack Recall

1M Context & MLA KV Compression

Low-rank latent projection · 82% KV Cache saving

Combines Multi-Head Latent Attention (MLA) with adaptive RoPE extrapolation. Ingests 1M tokens with 99.8% Needle-in-a-Haystack recall, eradicating lost-in-the-middle degradation.

Self-healing Autonomous Loop

Native Tool-Calling & Sandbox Execution

Direct Bash, Docker & API self-healing loop

Executes Bash commands, Docker containers, and REST APIs natively without glue code, inspecting error traces and auto-refining patches until test suites pass green.

Zero-Regression Codebase Refactoring

AST-Aware Full-Repo Graph Reasoning

Symbol-level cross-file dependency graph

Embeds codebase ASTs and dependency graphs directly into reasoning attention, tracking type inference, imports, and interface contracts across hundreds of files without regression.

Hardware & Cluster Sizing

Private Deployment VRAM Calculator

Estimate exact GPU memory requirements for model weights and KV Cache at scale.

Deployment Parameters

Model Architecture

Quantization Precision

Max Context Window128K Tokens (131072 words)

Concurrent Request Streams4 并发流

Resource Estimation

825 GB VRAM

Total GPU Memory required with runtime headroom

Model Weights760 GB

KV Cache (MLA)41 GB

GPU Configuration16x NVIDIA H100 80GB (2 Nodes over RoCE v2)

vLLM Launch Command

vllm serve THUDM/glm-5.3-753b \
  --tensor-parallel-size 8 \
  --pipeline-parallel-size 2 \
  --quantization fp8 \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.95 \
  --trust-remote-code

Flexible Pricing

Simple, Transparent Pricing

Choose between permanent credit packs that never expire, or high-volume monthly plans.

Starter Plan

For individual developers & engineers

Individual

$19/ month

Token Allowance

15,000,000 Tokens / month

Full access to GLM-5.3 (753B) & Flash (320B)
Full 1M token context window unlocked
Standard priority queue (up to 4 concurrency)
Codebase packaging & unified diff export

Pro Engineering

Most Popular: Intensive refactoring & projects

🔥 Most Popular

$49/ month

Token Allowance

50,000,000 Tokens / month

Dedicated high-speed priority queue (50% faster TTFT)
Unlimited concurrency for continuous IDE integration
Native autonomous sandbox tool execution (Bash/Docker)
Cross-doc synthesis & architecture blueprint export
Priority support & developer community channel

Enterprise Scale

Dedicated compute for engineering orgs

Dedicated SLA

$149/ month

Token Allowance

200,000,000 Tokens / month

Dedicated GPU instances with guaranteed throughput
99.9% uptime SLA with 15-minute response
Team RBAC seats, SSO integration & audit logs
VPC peering & custom model fine-tuning support
Invoicing, corporate wire transfer & dedicated TAM

Frequently Asked Questions

Technical & Commercial FAQ

Everything you need to know about GLM-5.3 architecture, benchmarks, and licensing.

Dense models activate all parameters for every token, resulting in heavy computation. GLM-5.3 uses a 16-expert MoE design where only 48B active parameters are routed per token, achieving the reasoning depth of a 753B model with 70% lower inference latency and compute cost.

Next-Generation Engineering AI

Experience GLM-5.3 Production AI Today

Whether refactoring complex repositories, parsing 1M-token documents, or running autonomous agents, GLM-5.3 delivers unprecedented power.

Launch Chat Studio Hardware Sizing