GLM 5 Turbo

The 302.AI real-world test excerpt confirms Zhipu AI released GLM-5-Turbo on March 16, 2026, positioning it as a native execution engine for the OpenClaw ecosystem rather than a general-purpose conversational model. The piece emphasizes training-time specialization for environment deployment, development, and analysis scenarios. Per the excerpt, GLM-5-Turbo targets four engineering goals for agent reliability: precision tool calling across complex long-task chains, instruction decomposition for multi-level planning, temporal awareness for timed and continuous tasks, and high-throughput processing for long-link workloads where other models commonly break.

Z.ai introduced GLM-5-Turbo on March 15, 2026, as a proprietary, non-open-source variant of its GLM-5 model tuned during training for agent-driven workflows such as OpenClaw tool use and long-chain execution. Per the VentureBeat excerpt, it lists roughly a 202.8K-token context window with 131.1K max output, positioned by Z.ai as faster and more stable for persistent automation than its predecessor. The excerpt reports launch pricing of $0.96 per million input tokens and $3.20 per million output via OpenRouter, about $0.04 cheaper overall than base GLM-5. Z.ai is rolling the model into its GLM Coding subscription across Lite, Pro, and Max tiers, with Pro subscribers receiving it in March while Lite subscribers wait until April for access.

TokenCost's analysis describes GLM-5 Turbo as a training-time-tuned variant of Zhipu's GLM-5 with 744B total parameters using a Mixture-of-Experts architecture and 44B active parameters. The excerpt reports a 200K context window, 128K max output, and a SWE-bench score of 77.8 percent, matching the base GLM-5. Per the excerpt, Zhipu positions GLM-5 Turbo as the first general-purpose LLM optimized for OpenClaw from the training phase rather than retrofitted, with pricing of $1.20 per million input and $4.00 per million output tokens. Zhipu also introduced a proprietary ZClawBench for agent tasks, though the excerpt notes scores have not yet been published.

Z.ai released GLM-5 Turbo on March 15, 2026 as a fast-inference model aimed at agent-driven environments such as OpenClaw scenarios. It is described as deeply optimized for real-world agent workflows with long execution chains. The model supports text input and output modalities with a 203K token context window, positioning it for extended AI applications. According to the model description, GLM-5 Turbo brings improved complex instruction decomposition, tool use, and scheduled and persistent execution, alongside stronger overall stability across extended tasks. These capabilities target developers building agentic AI systems that chain many steps and need consistent behavior over long AI workflows. Creator attribution to Z.ai is preserved.

Zhipu AI launched GLM-5-Turbo on March 16, 2026, alongside "OpenClaw Packages" subscription plans, sending shares up 16 percent. The model was optimized during training for agent-driven tasks, improving tool invocation, instruction following, scheduled and persistent task execution, and long-chain workflows critical to agent systems. On Zhipu's proprietary ZClawBench, GLM-5-Turbo ranked first among domestic models, and major internet companies gave strong feedback during internal Lobster scenario testing. Developer and enterprise access is available through the BigModel.cn platform via API and the new Lobster subscription plans, delivering what Zhipu describes as "token freedom" for agent usage. Zhipu simultaneously released ZClawBench, an end-to-end evaluation benchmark built from real-world OpenClaw use cases covering environment setup, coding, information gathering, data analysis, and content creation. GLM-5-Turbo has also been integrated into Mechanical Revolution's "OpenClaw Box," the first Mechanical Revolution device globally with a built-in GLM model for a native agent experience.

GLM-5-Turbo is Z.AI's foundation model explicitly optimized during training for OpenClaw agent scenarios, enhancing tool invocation, command following, scheduled and persistent task execution, and long-chain workflows. It supports text input and output modalities, offers multiple thinking modes, function calling, structured output, MCP integration, and streaming. The model provides a 200K context length with a 128K maximum output token cap, positioning it as a long-context agent-native LLM. The model delivers capabilities including real-time streaming, intelligent context caching for long conversations, and flexible MCP tool integration to extend agent use cases. Optimization was driven by real-world agent workflow data, ensuring stable multi-step tool calls and reliable decomposition of complex, multi-layered instructions. The documentation frames GLM-5-Turbo as moving OpenClaw tasks from dialogue to dependable execution, supported by training-stage rather than post-hoc tuning.

BenchGecko's model page lists GLM 5 Turbo as a proprietary text model from Z.ai released March 2026 with a 203K-token context window, priced at $1.20 per million input and $4.00 per million output tokens, noting it was tested on three benchmarks with no fully populated score. It reports Artificial Analysis composite sc The page situates GLM 5 Turbo within the z-ai GLM 5 family alongside base GLM 5 (Feb 2026), highlighting a $0.60/M input price increase and a 59K larger context versus its sibling. It links to z-ai pricing, developer documentation, a research/technical report, an API playground, and community channels, while flagging t

The Opper AI release tracker explicitly lists GLM-5-Turbo among Z.ai's dated model releases, confirming its launch date as March 15, 2026. The entry shows a 203K context window and an intelligence index score of 27, corroborating the VentureBeat coverage of the model as a Turbo-branded variant within Z.ai's GLM family. The tracker entry lists pricing for GLM-5-Turbo at $1.20 per million input tokens and $4.00 per million output tokens — figures that diverge from VentureBeat's OpenRouter-cited $0.96/$3.20 pricing, a discrepancy likely reflecting different aggregator snapshots or routing markups. Despite the pricing difference, both so