Huawei · PanGu-alpha

PanGu-alpha 200B

READER SUMMARY

Huawei's 200-billion-parameter Chinese autoregressive language model, trained with MindSpore auto-parallel computation on 2,048 Ascend 910 processors for few-shot and zero-shot language tasks.

DOOM SCORE37.1out of 100model risk profile, not the overall index
CURRENT ASSESSMENT · REVISION 1

Why this model scores 37.1

Capability reflects the model's major scale and general few-shot and zero-shot language performance. Autonomy is low because no persistent planning or independent tool use was shown. Deployment reflects a public launch with limited demonstrated access. Misuse and control difficulty are moderate because general language generation is dual-use but the system was not broadly open-weight or widely deployed.

Capability50
Autonomy8
Deployment28
Misuse potential34
Control difficulty29
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MODEL-ATTRIBUTED EVIDENCE

News tied to PanGu-alpha 200B

The model score of 37.1 rates this model's risk profile. The overall Doom Index of 61.0 measures the recalibrated complete evidence corpus. These values answer different questions.

NET MODEL-ATTRIBUTED INDEX CONTRIBUTION+0.04

Each article's leave-one-out Doom Index contribution is divided equally among the exact models named on that article. This prevents multi-model evidence from being claimed in full on several model pages. Model risk scores are recalculated from their technical profile and related news, but never feed back into the overall index.

CapabilityTOWARD

Huawei details the 200-billion-parameter PanGu-alpha model

Huawei researchers published the exact PanGu-alpha 200B technical report after Huawei launched the Pangu model family, documenting few-shot and zero-shot Chinese-language generation trained across 2,048 Ascend 910 processors.

Full item contribution
+0.04
PanGu-alpha 200B equal share
+0.04
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Model score history

  1. R1
    Doom Score 37.1

    Initial exact-version model profile supported by the dated technical report and Huawei's preceding Pangu family launch. No total score supplied.

    12 Aug 2026