GPT-5.5 Hallucinates 3x More Than GLM-5.2 Open

Hallucination Rates on AA-Omniscience

Recent benchmark data shows GLM-5.2, an MIT-licensed model with 753B parameters and roughly 40B active, posting a 28% hallucination rate. GPT-5.5 reached 86% on the same test, while Opus 4.8 scored 36% and Fable 5 scored 48%. DeepSeek V4 Pro, at 1.6T parameters, hit 94%. The results come from the AA-Omniscience benchmark, which measures how often models fabricate answers on questions they cannot resolve. GLM-5.2 therefore produces roughly one-third the confident errors of GPT-5.5 despite using far fewer active parameters.

Asyncio Event Loop Test Case

A concrete prompt asked models to design a custom asyncio event loop policy that overrides get_child_watcher with an atomic, non-yielding read loop that avoids both asyncio.create_task and raw select or poll calls. DeepSeek V4 Pro ran for three minutes and fifty-two seconds, emitted 7.7k reasoning tokens, and returned a design containing a blocking loop on the event loop thread. That implementation would deadlock any subprocess handling. GLM-5.2 completed the same task in twelve seconds with 799 tokens and correctly identified that a literal non-yielding loop on the event loop thread is impossible without breaking the entire asyncio machinery. The shorter trace also included practical engineering notes on why the requested constraints cannot be met.

Practical Trade-offs for Codebases

Teams that integrate large language models into code review or generation pipelines face a direct accuracy cost when choosing models with higher hallucination scores. A model that fabricates API usage or architectural patterns requires additional human review time. GLM-5.2's lower error rate on factual and architectural questions reduces that overhead. At the same time, the model still trails the largest closed models on the Artificial Analysis Intelligence Index by four to nine points, so tasks that reward broad reasoning depth may still favor the bigger proprietary options. Inference cost and licensing also differ: an MIT-licensed weight set permits local deployment and modification without usage restrictions that apply to closed APIs.

Deployment Considerations

Running GLM-5.2 locally demands hardware capable of handling a 753B parameter model with 40B active parameters per forward pass. Quantization and mixture-of-experts routing reduce memory footprint compared with dense 1-2T models, yet the absolute requirements remain substantial. Teams already operating on-premise inference clusters can swap in the open weights without new licensing negotiations. Those relying on hosted APIs must weigh the documented hallucination gap against the convenience of managed endpoints. Monitoring token usage during reasoning traces provides an early signal: models that emit thousands of tokens on simple prompts often mask uncertainty through verbosity rather than admitting limits.

FAQs

Does the lower hallucination rate mean GLM-5.2 is always preferable? No. On broad intelligence benchmarks it still trails the largest closed models, so the choice depends on whether factual precision or maximum reasoning depth matters more for the workload.

How was the hallucination percentage measured? The AA-Omniscience benchmark presents questions the model cannot answer correctly and records whether it states uncertainty or instead supplies a fabricated response.

Can the MIT license be used for commercial internal tools? Yes. The license permits modification and internal deployment without the usage restrictions attached to most proprietary model APIs.

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