Exact capability metadata is available. CLSSAI can prepare the Text chat request without guessing the model contract.
- Evidence
- Exact ID z-ai/glm-5.3 · /chat/completions
- Mapped mode
- Text chat
z-ai/glm-5.3GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves...
OpenRouter testing and CLSSAI API routing are independent decisions with separate sources.
Exact capability metadata is available. CLSSAI can prepare the Text chat request without guessing the model contract.
The exact model ID is listed in the current CLSSAI public directory. This proves current route eligibility, not a completed inference for every mode.
The request contract and frontend path are ready. A real OpenRouter output has not yet been recorded for this exact model and mode.
Compare published prices and endpoint capabilities. Select a provider for context limits, caching prices and supported parameters.
| $0.9 | $3 | $0.15 | -- | -- | 28.5% | |
| $0.9 | $3 | $0.15 | -- | -- | 93.2% | |
| $0.91 | $2.86 | $0.169 | -- | -- | 94.0% | |
| $0.936 | $3.17 | $0.187 | -- | -- | 99.47% | |
| $0.95 | $4.4 | $0.19 | -- | -- | 98.4% | |
| $1.03 | $3.48 | $0.205 | -- | -- | 95.3% | |
| $1.05 | $3.3 | $0.195 | -- | -- | 99.96% | |
| $1.05 | $3.3 | $0.195 | -- | -- | -- | |
| $1.07 | $3.37 | $0.176 | -- | -- | 100.00% | |
| $1.08 | $4.1 | $0.176 | -- | -- | 99.95% | |
| $1.12 | $3.52 | $0.208 | -- | -- | -- | |
| $1.13 | $3.56 | $0.21 | -- | -- | 100.00% | |
| $1.19 | $3.74 | $0.196 | -- | -- | 99.79% | |
| $1.26 | $3.96 | $0.234 | -- | -- | 100.00% | |
| $1.26 | $3.96 | $0.234 | -- | -- | 100.00% | |
| $1.3 | $4.4 | $0.26 | -- | -- | 100.00% | |
| $1.35 | $4.4 | $0.23 | -- | -- | 97.7% | |
| $1.4 | $4.4 | $0.14 | -- | -- | -- | |
| $1.4 | $4.4 | $0.28 | -- | -- | 100.00% | |
| $1.4 | $4.4 | $0.26 | -- | -- | -- | |
| $1.4 | $4.4 | $0.26 | -- | -- | 97.9% | |
| $1.4 | $4.4 | $0.26 | -- | -- | 99.00% | |
| $1.4 | $4.4 | $0.26 | -- | -- | 98.6% | |
| $1.4 | $4.4 | $0.26 | -- | -- | -- | |
| $1.4 | $4.4 | $0.26 | -- | -- | 98.8% | |
| $1.4 | $4.4 | $0.14 | -- | -- | 100.00% | |
| $1.4 | $4.4 | $0.26 | -- | -- | 100.00% | |
| $1.4 | $4.4 | $0.26 | -- | -- | -- | |
| $1.4 | $4.4 | $0.26 | -- | -- | 100.00% | |
| $1.4 | $4.4 | $0.26 | -- | -- | 99.97% | |
| $2.1 | $6.6 | $0.21 | -- | -- | 100.00% |
USD per 1M tokens. Prices and metrics describe OpenRouter endpoints, not CLSSAI routing. “--” means no measurement was supplied. Uptime: last 30 minutes.
Published base rates across providers, in USD per 1M tokens. Actual cost depends on the provider, caching and prompt length.
Effective Pricing: request-weighted costs and historical prices are not supplied by the public source.
Latency and throughput history are not supplied by the public endpoint API. Check OpenRouter for its latest performance charts.
Successful requests in the last 30 minutes, as reported by OpenRouter. A snapshot does not show historical reliability.
No scored evaluations are included in the public model metadata. View the source for benchmarks and their methodology.
Application rankings require verified traffic data, which is not included in this source.
Historical token and request volumes are not supplied by the public API. OpenRouter shows activity on its own network.
Provider availability, context limits and API capabilities.
InferenceNet Fp4, DeepInfra Fp4, Novita Fp8, Reka Fp8, Wafer, Io Net Fp8, Phala, GMICloud Fp8, Morph Fp8, Inceptron Fp4, SiliconFlow Fp8, Sail Research Fp8, Decart Fp4, DigitalOcean, Friendli, AkashML Fp8, Makora Fp4, Mistral Nvfp4, Alibaba, Baidu Fp8, Crusoe Fp4, Venice, Together, Parasail Fp8, Modal, BaseTen Fp4, Fireworks, Cloudflare, AtlasCloud Fp8, Z.AI Fp8, BaseTen Fp8 are listed in the OpenRouter reference data. Check CLSSAI availability above before making a request.
The largest available context window is 1,310,720 tokens.
The providers list 21 distinct supported parameters. Open an endpoint row to inspect them.
Continue comparing models or start integrating.