The first entry is always the pin's source. Overall confidence is a weighted average of how firmly each reference supports the start and end times used above; a reference counts half as much for every 180 days older than the newest.
Z.ai's developer release notes list "2025-09-30 GLM-4.6" ("We've launched GLM-4.6, the flagship coding model"); Wikipedia[2] cites Z.ai's launch post dated 30 September 2025, the Hugging Face[6] weights were uploaded late on 29 September UTC (early 30 September in Beijing) and The Decoder[4] reported it on 1 October.
Records that Z.ai released GLM-4.6 in late September 2025 using domestic Chinese chips such as Cambricon's, citing the Z.ai blog post dated 30 September 2025 and SCMP's 1 October report.[1]
Z.ai's pricing page lists GLM-4.6 at $0.6 per million input tokens, $0.11 cached and $2.2 output.[1]
Independent report: 200,000-token context, 48.6% win rate against Claude Sonnet 4 in real-world tests, ahead of DeepSeek-V3.2-Exp on eight benchmarks but behind Claude Sonnet 4.5 in code generation; available on Z.ai, OpenRouter, Hugging Face[6] and ModelScope.
Z.ai's release-notes page lists GLM-4.6 under 2025-09-30, calls it the flagship coding model and "the leading coding model in China" and notes the 200K context window.[1]
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Z.ai's evaluation chart (eight benchmarks, 128K context), transcribed from the maker's image on docs.z.ai[1]; it is not published as a table.
| Benchmark | GLM-4.6 | GLM-4.5 | DeepSeek-V3.2-Exp | Claude Sonnet 4 | Claude Sonnet 4.5 |
|---|---|---|---|---|---|
| AIME 25 | 93.9 (98.6 w/ tools) | 85.4 | 89.3 | 74.3 | 87.0 |
| GPQA | 81.0 (82.9 w/ tools) | 79.9 | 79.9 | 77.7 | 83.4 |
| LiveCodeBench v6 | 82.8 (84.5 w/ tools) | 63.3 | 70.1 | 48.9 | 57.7 |
| HLE | 17.2 (30.4 w/ tools) | 14.4 | 19.8 | 9.6 | 17.3 |
| BrowseComp | 45.1 | 26.4 | 40.1 | 14.7 | 19.6 |
| SWE-bench Verified | 68.0 | 64.2 | 67.8 | 72.5 | 77.2 |
| Terminal-Bench | 40.5 | 37.5 | 37.7 | 35.5 | 50.0 |
| τ²-Bench (weighted) | 75.9 | 67.5 | 53.4 | 66.0 | 88.1 |