GLM-5.3-Flash (EXL3, TensorFold)
This EXL3 quantization of Z.AI’s GLM-5.3-Flash runs on TensorFold, a young open inference engine with lossless speculative decoding: accelerated draft tokens correspond exactly to the result of serial decoding. The MoE activates around 18 billion of 320 billion parameters per token, processes text, image, and video, and the weights are available under the MIT license.
- Open Weights
- Server
- TensorFold
- Text
- Vision
- Video
- Agentic Orchestrator
- Long Context
- Batch
Sovereign Risk: MEDIUM The model was developed by Z.AI, a company headquartered in China (CN). The risk is rated ‘medium’ rather than ‘high’ because the weights were released under the very permissive MIT license. This reduces vendor dependency and mitigates some of the risks associated with Chinese jurisdiction. Nevertheless, a residual risk remains regarding training data provenance and potential regulatory influences. The TensorFold variant uses the same EXL3 weights as the vLLM baseline (glm-5_3-flash-exl3); the risk refers to the weights, not the serving engine.
Key metrics
Score · Latency · Cost · Quality
- Total Score Gold
- 80.94
- Routine
- 49.31
- Reasoning
- 31.63
- LLM Judge Avg
- 4.19 / 5
- 100 Coverage
- Avg Task Duration
- 119.21s
- Batch
- Token Rate
- 55.51tok/s
- Output Rate
- P95 Latency
- 354.98s
- Top 5 %
- Total Tokens
- 345700
- Output Volume
- Cost per 1K
- $0
- USD / 1K Requests
- Benchmark Cost
- $0
- Total · 345700 tok
Benchmark modules
10 modules · weighted · vs. model median & top performer
Token efficiency & latency
Consumption per module vs. model median
Token consumption per module
Performance profile
Key metrics – legend
Score
- Total Score
- Overall score derived from Routine and Reasoning scores. Basis for the fleet rank and tier assignment (Gold / Silver / Bronze / Standard).
- Routine
- Score for standard tasks: code generation, text transformation, structured outputs. Weighted by task complexity.
- Reasoning
- Score for analytical and logical tasks: multi-step problem solving, mathematical reasoning, structured argumentation.
- Rank
- Position in the fleet comparison, sorted by Total Score. Refers to the snapshot at the time of testing.
LLM Judge
- LLM Judge Ø
- Average quality rating by an independent judge model on a scale of 1–5.
- Coverage
- Share of tasks for which a judge rating is available. 100% means all responses were evaluated.
Latency & speed
- Avg. Task Duration
- Average time per task from the first to the last token of the response, measured across all benchmark tasks.
- Token Rate
- Output speed in tokens per second. Relevant for interactive applications and real-time streaming.
- P95 Latency
- 95th percentile of response times — shows outliers. Lower values mean fewer long wait times in edge cases.
Cost
- Cost per 1K
- API price per 1,000 output tokens in US dollars. Basis for cost calculation in production use.
- Benchmark Cost
- Total cost for the complete benchmark run of this model.