State of AI Report 2026, the ninth edition, says changing GLM-5.1's setup lifted its coding success from 52.5% to 65.5% without retraining. For teams setting AI budgets, the report puts much of the return in setup and user skill, though its introduction's main evidence for that is one 100-task study.
Publishers:stateof.ai
Reality
- Evidence40
- Adoption30
- Hype gap+30
- Incentives65
- Confidence45
buildOne report1 publisher A MATS project ran two tasks inside one context window and measured reward hacking on the second. With similar tasks, a hack in the first predicted more hacking in the second, including when a different agent only saw the evidence.
Reality
- Evidence42
- Adoption
- Insufficient
- Hype gap+12
- Incentives25
- Confidence45
buildOne report1 publisher Ten Chinese model variants now answer an OpenAI tool-calling payload well enough to return a valid response, which is the easy part. The streaming deltas and call arity behind it are where an agent migration spends its budget.
Reality
- Evidence36
- Adoption47
- Hype gap+14
- Incentives
- Insufficient
- Confidence43
buildOne report1 publisher Z.ai's August 14 post claims post-training gains for coding agents, but the company's release notes still stop at GLM-5.1 and there is no API endpoint, model identifier or weight download.
Reality
- Evidence42
- Adoption18
- Hype gap+38
- Incentives68
- Confidence46
buildConfirmed2 publishers Z.ai says all of GLM-5.3's coding gains came from post-training on tenfold more long-horizon task environments. The uneven benchmark jumps tell you where that money actually landed.
Reality
- Evidence48
- Adoption30
- Hype gap+24
- Incentives76
- Confidence55