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GLM-5-Turbo
Launched this week
GLM-5-Turbo is Z.ai’s high-speed variant of GLM-5, deeply optimized for OpenClaw from the training stage. It excels at precise tool calling, complex command following, scheduled and persistent tasks, and long-chain execution with near-zero hallucinations. Faster, more reliable, and purpose-built for real agent workflows.
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Launch Team
Flowtica Scribe
Hi everyone!
GLM-5-Turbo feels like a very intentional and interesting release.
Instead of just calling it a faster GLM-5, Z.ai is positioning it as a model deeply optimized for OpenClaw from training onward. That means stronger tool calling, better breakdown of complex instructions, more stable timed and persistent tasks, and smoother long-chain execution—which is basically exactly what people actually want from an agent model.
It is still experimental and currently closed-source, but Z.ai says the capabilities and findings here will be rolled into the next open-source release.
Also nice to see usage limits tripled for GLM-5-Turbo in the GLM Coding Plan!
I’ve been testing GLM-5-Turbo inside OpenClaw for the past few days, and it’s the first “agent-focused” model that actually feels like it was built for real workflows instead of just benchmarks.
What stands out most is how confidently it calls tools and chains steps together. I’m running fairly complex, multi-step automations (with conditionals, retries, and cross-tool dependencies), and GLM-5-Turbo almost never gets lost or hallucinates APIs. It keeps track of context over long sessions and finishes jobs without me having to babysit it.
In practice, that means:
More reliable long-running agents – it can execute 10–20 step flows without silently drifting off-spec.
Fast iteration loops – responses are noticeably snappy, so iterating on tool schemas and workflows is painless.
Lower cognitive overhead – I don’t have to over-engineer guardrails just to keep it from making things up.
If you’re building production agents (not just chatbots), this is the kind of model you want: optimized for tool use, stable over long chains, and fast enough that you can ship and iterate quickly. Excited to see a model that is clearly tuned around “real-world agent ops” instead of just leaderboard scores.
Really impressed by GLM-4.6V's video understanding capabilities — 128K context for continuous video processing is a game changer for anyone building verification or proctoring workflows.
One thing I've noticed building video infrastructure at Vidtreo: the AI analysis layer (models like GLM-4.6V) keeps getting better, but reliable browser-based video capture is still the bottleneck for most teams. You can have the best vision model in the world, but if the recording drops frames or fails on mobile Safari, the pipeline breaks before the AI ever sees it.
Curious — are you seeing developers combine your vision API with browser capture SDKs for real-time use cases like identity verification or exam proctoring? That capture + analysis combo feels like the next big unlock.
Congrats on making these models open and accessible.
GLM-5-Turbo isn’t just faster—it’s tuned for real agent operations, not leaderboard vanity metrics. Handling tool calls, conditionals, retries, and long-chain workflows with minimal hallucination is exactly what production agents need.
The MIT-licensed base and reasoning variants also hint at a strong ecosystem for developers who want both speed and transparency.
I’m curious about scaling: how does the model handle multiple concurrent long-chain agents, and what trade-offs exist between speed, context length, and tool orchestration under heavy load?
Very timely launch! Will this model be available on DeepInfra?
MockRabit
Interesting! I hope your efforts helps Openclaw users save tons of tokens and provide more meaningful results. I am certainly going to try it over the weekend and return here with the feedback.
I used OCR by GLM, it was pretty slow, but GLM-5-Turbo looks great