GLM 5.3
- Score
- 44.8 · rank 15
- Price
- Paid
- API
- Yes
- Weights
- No
WHAT IT DOES
GLM-5.3 is Z.ai's flagship coding and agentic model, released 14 August 2026. It uses the same 743B-parameter mixture-of-experts base as GLM-5.2, with Z.ai attributing the gains to scaled post-training rather than a new pre-training run. It targets long-horizon agentic coding, business-process automation, defensive security work and tasks that span many steps. The model supports three reasoning-effort levels and a 1M-token route for coding plans.
WHY WE PICKED IT
The Terminal-Bench 3.0 score moved from 4.6% to 28.3%, DeepSWE v1.1 from 46.2% to 66.9%, and CyberGym from 77.2% to 84.5% — all on the same base as GLM-5.2. That is a real signal about post-training returns, even if most numbers are vendor-run and weights are not yet released. It is also reported as one of the fastest models in its class, at roughly 115 tokens per second.
QUICK FACTS
STRENGTHS
- ✓Terminal-Bench 3.0 jumped from 4.6% to 28.3% on the same base
- ✓DeepSWE v1.1 improved from 46.2% to 66.9%
- ✓CyberGym defensive-security score of 84.5%
- ✓Reported 115 tokens per second throughput
- ✓1M-token context route for coding plans
LIMITATIONS
- ⚠Weights not released at launch; promised in roughly two weeks
- ⚠No published per-token price; standard API pricing not confirmed
- ⚠No vision or multimodal capability announced
- ⚠Most benchmarks are vendor-run and not independently reproduced
- ⚠Direct API requires thinking to be enabled, a breaking change from GLM-5.2
GETTING STARTED
Call GLM-5.3 through the Z.ai API or the chat interface at chat.z.ai. Set `thinking.type` to `enabled` and use `reasoning_effort` of `low`, `high` or `max` (default). For the 1M coding-plan route, use the `glm-5.3[1m]` suffix. Check the Z.ai pricing page for current rates before running large workloads.
BENCHMARKS
QUICK TIPS
USE CASE EXAMPLES
Long-Horizon Refactor
Run a multi-file refactoring task that spans many tool calls.
- Describe the refactor in a single detailed prompt
- Set `reasoning_effort` to `max`
- Give the agent access to the relevant files and tests
- Review every file change before applying
- Measure wall-clock time and output tokens against GLM-5.2
PRICING
FEATURED IN GUIDES
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EXPLORE ALTERNATIVES
Compare GLM 5.3 with 5+ similar llms AI tools.
FREQUENTLY ASKED QUESTIONS
Is GLM 5.3 free?
No, GLM 5.3 requires a paid subscription.
Does GLM 5.3 have an API?
Yes, GLM 5.3 offers an API for programmatic integration.
What is GLM 5.3 best for?
GLM 5.3 is best for Post-Training Gains. GLM-5. The Terminal-Bench 3.0 score moved from 4.6% to 28.3%, DeepSWE v1.1 from 46.2% to 66.9%, and CyberGym from 77.2% to 84.5% — all on the same base as GLM-5.2. That is a real signal about post-training returns, even if most numbers are vendor-run and weights are not yet released. It is also reported as one of the fastest models in its class, at roughly 115 tokens per second.
What platforms does GLM 5.3 support?
GLM 5.3 supports web, api.
Is GLM 5.3 open source?
No, GLM 5.3 is not open source.
How do I get started with GLM 5.3?
Call GLM-5.3 through the Z.ai API or the chat interface at chat.z.ai. Set `thinking.type` to `enabled` and use `reasoning_effort` of `low`, `high` or `max` (default). For the 1M coding-plan route, use the `glm-5.3[1m]` suffix. Check the Z.ai pricing page for current rates before running large worklo...
How do I use GLM 5.3?
GLM 5.3 is a large language model for text generation, analysis, and conversation. Access through the web interface. Enter prompts or questions to get responses. It excels at terminal-bench 3.0 jumped from 4.6% to 28.3% on the same base.
FEATURED ON CURATEDAI
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