COLOMBO, Sri Lanka (AP) — Steve Smith has been added to Australia's squad at the Twenty20 World Cup officially as an injury replacement for fast bowler Josh Hazlewood.
The 36-year-old Smith was rushed to Colombo at the start of the tournament when skipper Mitch Marsh was ruled out of Australia's opening win over Ireland.
Smith, who played his last Twenty20 international two years ago, was cleared by the International Cricket Council to participate if required.
He has played 67 T20 internationals and averages almost 25 at a strike rate of 125.45, with five half-centuries and a highest score of 90.
Australia, coming off an upset loss to Zimbabwe, was playing tournament co-host Sri Lanka later Monday at Pallekele in Group B.
Cricket Australia said Smith trained with the squad on Sunday and selectors decided to formalize his inclusion in the squad.
“With Steve here, along with some uncertainty around Mitch and Marcus Stoinis, it made sense he (Smith) is activated and available for selection in time for (Monday's) match, if required,” Australia selector Tony Dodemaide said.
AP cricket: https://apnews.com/hub/cricket
Australia's players stand up for the national anthems before the start of the during the T20 World Cup cricket match between Australia and Zimbabwe in Colombo, Sri Lanka, Friday, Feb. 13, 2026. (AP Photo/Eranga Jayawardena)
Australia's Marcus Stoinis, center left, celebrates with teammates the wicket of Zimbabwe's Tadiwanashe Marumani during the T20 World Cup cricket match between Australia and Zimbabwe in Colombo, Sri Lanka, Friday, Feb. 13, 2026. (AP Photo/Eranga Jayawardena)
SINGAPORE--(BUSINESS WIRE)--Feb 16, 2026--
GLM-5, newly released as open source, signals a broader shift in artificial intelligence. Large language models are moving beyond generating code snippets or interface prototypes toward building complete systems and carrying out complex, end-to-end tasks. The change marks a transition from so-called “vibe coding” to what researchers increasingly describe as agentic engineering.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20260215030665/en/
Built for this new phase, GLM-5 ranks among the strongest open-source models for coding and autonomous task execution. In practical programming settings, its performance approaches that of Claude Opus 4.5, particularly in complex system design and long-horizon tasks requiring sustained planning and execution.
The model rests on a new architecture aimed at scaling both capability and efficiency. Its parameter count has expanded from 355bn to 744bn, with active parameters rising from 32bn to 40bn, while pre-training data has grown to 28.5trn tokens. These increases are paired with advances in training methods. A framework called Slime enables asynchronous reinforcement learning at a larger scale, allowing the model to learn continuously from extended interactions and improve post-training efficiency. GLM-5 also introduces DeepSeek Sparse Attention, which maintains long-context performance while cutting deployment costs and improving token efficiency.
Benchmarks suggest strong gains. On SWE-bench-Verified and Terminal Bench 2.0, GLM-5 scores 77.8 and 56.2, respectively, the highest reported results for open-source models, surpassing Gemini 3 Pro in several software-engineering tasks. On Vending Bench 2, which simulates running a vending-machine business over a year, it finishes with a balance of $4,432, leading other open-source models in operational and economic management.
These results highlight the qualities required for agentic engineering: maintaining goals across long horizons, managing resources, and coordinating multi-step processes. As models increasingly assume these capabilities, the frontier of AI appears to be shifting from writing code to delivering functioning systems.
Chat & Official API Access
Z.ai Chat:https://chat.z.ai
GLM Coding Plan: https://z.ai/subscribe
Open-Source Repositories
GitHub:https://github.com/zai-org/GLM-5
Hugging Face:https://huggingface.co/zai-org/GLM-5
Blog
GLM-5 Technical Blog:https://z.ai/blog/glm-5
LLM Performance Evaluation: Agentic, Reasoning and Coding
