GLM-5.3-Flash
Cost-optimized mixture-of-experts model for multimodal coding and long-context tasks
Context window
1M
Input / 1M tokens
$0.075
Output / 1M tokens
$0.25
Provider
Z.ai (Zhipu AI)
Data verified 2026-08-28
GLM-5.3-Flash is a 320B-parameter mixture-of-experts model with 18B active parameters per token, featuring a hybrid sparse-and-linear-attention design for efficient long-context processing. Natively multimodal supporting text, image, and video inputs, it is optimized for inference on both domestic Chinese chips and standard infrastructure. Released under MIT license as open-weight.
Capability index
Relative estimates (0-100) to place this model against its peers, grounded in published benchmarks.
How to access it
Available via OpenRouter API and open-weight checkpoint under MIT license. API pricing valid until September 9, 2026.
Strengths
- ✓Cost-efficient inference with 18B active parameters vs 320B total
- ✓Strong coding performance (63% on DeepSWE Pro)
- ✓1M-token context window for long documents and codebases
- ✓Native multimodal support (text, image, video)
- ✓3x improvement in attention efficiency vs GLM-5.3 standard
- ✓Proven at scale on Chinese domestic chips
Best for developers who...
When to choose it (and when not to)
Reach for GLM-5.3-Flash when...
- →When inference cost matters more than maximum capability
- →For agentic tasks requiring many tool calls and long prompts
- →When deploying on non-US infrastructure is required
- →For open-weight deployments in isolated environments
Look elsewhere if...
- ✕Training frontier models (inference-only optimization)
- ✕Tasks requiring absolute maximum reasoning depth
- ✕When closed-source commercial support is required
How to use it
- ›Leverage the full 1M context window for multi-file repository analysis
- ›Use structured output (JSON) for agent integration
- ›Enable reasoning with reasoning_effort parameter (low/high/max)
Quickstart
Pythonimport anthropic
client = anthropic.Anthropic(api_key='sk-...')
message = client.messages.create(
model='glm-5-3-flash',
max_tokens=4096,
messages=[{'role': 'user', 'content': 'Code review this repository...'}]
)
print(message.content[0].text)Via OpenRouter or Z.ai API; open weights available at zai-org/GLM-5.3-Flash on HuggingFace
API model id: glm-5.3-flash
Benchmarks
| Benchmark | Score | Notes |
|---|---|---|
| DeepSWE | 63% | Software engineering benchmark; vendor-reported 63.4%, independent Artificial Analysis 57% |
| Terminal-Bench 2.1 | 84.3 | General reasoning benchmark |
| Toolathlon | 78.4 | Tool-calling capability |
| AutomationBench | 48.8 | Large jump from GLM-5.2's 26.2 |
Source: Z.ai Official Documentation
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