LLM Reference

GLM-5.3

Released
2026-08-14
Last refreshed
2026-08-24
Status
Researched 1d ago
ProprietaryCommercial use: conditionalCodingRAGAgentsLong contextClassificationJSON / Tool use

GLM-5.3 is worth evaluating for coding, rag, and agents when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, rag, and agents
  • Workloads that can use a 1m context window
  • Buyers comparing 1 tracked provider route

Do not use it for

  • Vision or document-understanding workloads
Specifications
Family
GLM-5
Released
2026-08-14
Context
1m
Max output
131,072
Specialization
coding
Openness
Proprietary
License
ProprietaryCommercial use: conditional
Weights
Not released
Code
Unknown
Training
Fine-tuned
Created by

Chinese AI research lab developing GLM language models.

Beijing, China
Founded 2019
Website
Pricing
Output / 1M
$4.40
Input / 1M
$1.40

Cheapest of 1 route · Z.ai · cache read $0.260

About

GLM-5.3 is Z.ai's August 2026 coding-first post-training release on the same GLM-5.2 base model, focused on complex software engineering, long-horizon agentic coding, and emergent cybersecurity evaluation. It is available through the GLM Coding Plan and Z.ai API; open weights are scheduled after safety hardening. Official API model ID: glm-5.3.

Top use-case fit: coding, agents, and build tasks

Coding

Included by capability and metadata signals in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

Included by capability and metadata signals in the decision map.

Provider price ladder

Compare API pricing across 1 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MCacheRoute
Z.ai$1.40$4.40read $0.260
Serverless

Capabilities

ReasoningJSON / Tool useStructured OutputsPrompt Caching

Benchmark peer barsfor Coding

No task-mapped benchmark peers are available for this model yet.

Benchmark scores(2)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionEvaluationSource
DeepSWE 1.166.9DeepSWE v1.1Observed 2026-08-14Source
Agents' Last Exam28.5Agents' Last Exam (CLI)Observed 2026-08-14Source

Migration checks

No linked migration route is available for this model yet.

API versions

glm-5.3