Zhipu AI·released 2026-06-16✓ 2 sources
GLM-5.2 benchmark scores: 21 benchmarks tracked. 24% of its scores are independently reproduced — the rest are self-reported or unverified.
Leads on 0 of 21 benchmarks·best result 89.33 on DTBench (unverified)·5 of 21 independently reproduced·$1.4/$4.4 per M tokens
Source: models.dev, OpenRouter · See every model’s pricing →
| Benchmark | Score | Evidence status | Measured |
|---|---|---|---|
| DTBench | 89.33accuracy (%) | unverifiedT1 | 2026-06-16https://conceptualreasoning.ai/dtbench |
| GPQA diamond | 89.14accuracy (%) | reproduced· optimizedT1 | 2026-06-16Epoch AI |
| OTIS Mock AIME 2024-2025 | 86.38accuracy (%) | reproduced· optimizedT1 | 2026-06-16Epoch AI |
| SWE-Bench verified | 78.7accuracy (%) | reproduced· optimizedT1 | 2026-06-16Epoch AI |
| ARC-AGI | 77accuracy (%) | unverified· optimizedT1 | 2026-06-16https://arcprize.org/leaderboard |
| WeirdML | 70.12accuracy (%) | unverifiedT1 | 2026-06-16https://htihle.github.io/weirdml.html |
| FrontierMath-Tiers-1-3-v2-Private | 59.21accuracy (%) | unverifiedT2 | 2026-06-16 |
| Surface Evolver Bench | 55.62accuracy (%) | unverifiedT1 | 2026-06-16https://yhenon.github.io/surface-evolver-llm-eval/ |
| LMCA | 53.91accuracy (%) | unverifiedT1 | 2026-06-16https://conceptualreasoning.ai/lmca |
| SimpleBench | 50.56accuracy (%) | unverifiedT1 | 2026-06-16https://lmcouncil.ai/benchmarks |
| APEX-Agents | 45.2accuracy (%) | unverifiedT2 | 2026-06-16 |
| DeepSWE | 43.78accuracy (%) | unverifiedT1 | 2026-06-16https://deepswe.datacurve.ai/ |
| ProofBench | 35accuracy (%) | unverifiedT1 | 2026-06-16https://www.vals.ai/benchmarks/proof_bench |
| SimpleQA Verified | 34.2accuracy (%) | unverifiedT2 | 2026-06-16 |
| PostTrainBench | 31.7accuracy (%) | unverifiedT2 | 2026-06-16 |
| FrontierMath-Tier-4-v2-Private | 29.27accuracy (%) | reproducedT1 | 2026-06-16Epoch AI |
| FrontierCode | 24.5accuracy (%) | unverifiedT1 | 2026-06-16https://cognition.com/frontiercode |
| ARC-AGI-2 | 22.78accuracy (%) | unverifiedT2 | 2026-06-16 |
| Chess Puzzles | 17.93accuracy (%) | unverifiedT2 | 2026-06-16 |
| Mystery Game Puzzles | 10.77accuracy (%) | unverifiedT2 | 2026-06-16 |
| EBR-bench | 9.52accuracy (%) | reproducedT1 | 2026-06-16Epoch AI |
Claims drawn from cited facts, not live model generation.
This model was developed by Zhipu AI and released in 2026. Its recorded parameter count is 753.33 billion.
3 cited facts
This model has scores on 21 tracked benchmarks and currently leads 0 of them. The record’s evidence status is independently reproduced.
3 cited facts
Its mean gap to the best recorded score across its tracked benchmarks is 36.03 points. It currently holds the top score on none of them.
2 cited facts
7 facts cross-checked across data sources: 2 corroborated, 2 single-source, 3 disputed.
Source facts, citations, and refresh stamp for this record.
Sources: curated_capability_claims, epoch_benchmarks, huggingface_models, modelsdev_models +1 more
GLM-5.2 is an AI model developed by Zhipu AI, released 2026-06-16. Its benchmark record below tags every score as independently reproduced, self-reported, or unverified.
GLM-5.2 has recorded scores on 21 benchmarks, each shown with its evidence status.
GLM-5.2 has recorded scores on 21 benchmarks — GPQA diamond, OTIS Mock AIME 2024-2025, DTBench, WeirdML, LMCA, SimpleQA Verified, and 15 more. The full table above shows each score with its evidence status.
5 of 21 recorded scores (24%) are independently reproduced rather than self-reported by the lab.
GLM-5.2 has 21 tracked claims: 5 independently reproduced, 16 unverified.
Listed API pricing: $1.4 per million input tokens, $4.4 per million output tokens (prices disputed across sources). See the pricing block for the full breakdown.