GLM-5-Turbo is the 5-series GLM model tuned specifically for long-chain agent work: trajectories where the plan is revised repeatedly, tools return unexpected output, and the run has to stay executable for dozens or hundreds of turns. It takes the GLM-5 base and optimises it for that dynamic pattern rather than for one-shot answers.
Long-chain execution is a different problem from raw reasoning quality. A model can be excellent at a single hard question and still fall apart across fifty turns because it loses track of what it already tried, silently drops a constraint, or reformulates the goal halfway through. GLM-5-Turbo exists because that failure mode is what actually blocks agents in production, and it is the trait the 5 series carried forward into GLM-5.1 and GLM-5.2.
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This playground runs a free open-source LLM via OpenRouter. It is not GLM-5.2 and does not use GLM weights — it's here so you can test a live model while you read the published GLM benchmark data below.
A long-chain agent scenario is one where the model does not receive a task and return an answer; it receives a goal, calls a tool, reads a result it did not predict, revises its plan and repeats. The failure modes are specific: repeating an action that already failed, losing a constraint stated forty turns earlier, or producing a plan step that cannot actually be executed by the harness. Tuning for that pattern means optimising for trajectory-level behaviour rather than turn-level fluency — keeping the goal stable, keeping tool calls well-formed, and recovering from an unexpected result instead of restarting. That is the property GLM-5-Turbo was built around, and it is why the same design thread runs through the 1M-context flagship, which was trained on long coding-agent trajectories for exactly the same reason.
The published comparison table covers GLM-5.2 and GLM-5.1. The column below is GLM-5.1's — the closest published reference point to the GLM-5 generation. No benchmark column has been published for GLM-5-Turbo itself.
There is no published GLM-5-Turbo row, so this site quotes none. What the reference column does show is where the generation around it landed on agentic evaluation: 71.8 on the MCP-Atlas public set and 40.7 on Tool-Decathlon for GLM-5.1, against 76.8 and 48.2 for GLM-5.2. Agentic tool use is the benchmark family closest to what Turbo was tuned for, and it is one of the areas the flagship improved most consistently.
Comparison on the MCP-Atlas public set, as published by the model authors.
MCP-Atlas is the closest published proxy for long-chain tool behaviour, and the field is tight: Claude Opus 4.8 at 77.8, GLM-5.2 at 76.8, GPT-5.5 at 75.3, GLM-5.1 at 71.8 and Gemini 3.1 Pro at 69.2. A one-point gap to the closed frontier on agentic tool use is a very different picture from the ten-point gaps that show up on some standard coding rows — and it is the strongest single argument for running an open-weight GLM model inside an agent harness.
GLM-5-Turbo is a sibling of GLM-5 rather than a successor to it, and both were superseded by GLM-5.1 and then GLM-5.2. If long-chain agent execution is your workload, the flagship inherits everything Turbo was designed for and adds a 1M-token window plus explicit effort levels, so it is the release to build on today.
The release that opened the 5 series: stronger coding fundamentals, more reliable multi-step execution and noticeably better behaviour on complex agent tasks.
The 1M-token context flagship. It is the open-source state of the art on coding and long-horizon evaluations, with explicit Non-Thinking, High and Max effort levels.
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