GLM-5 Turbo: the first model built for OpenClaw. Is it worth $1.20 per million tokens?
Zhipu just released a model that was trained from scratch for agentic workflows on OpenClaw. Not fine-tuned. Not adapted. Built for it. At $1.20/1M input, it costs 4x more than MiniMax M2.5. Here's whether that premium buys you anything real.
Image source: Pandaily
TL;DR
- -Model:
glm-5-turbo, released March 16, 2026. - -Pricing: $1.20/1M input, $4.00/1M output. About 4x more than MiniMax M2.5.
- -Architecture: Built on GLM-5 (744B params, MoE, ~44B active). 200K context, 128K max output.
- -The pitch: First general-purpose LLM optimized for OpenClaw from the training phase. Not retrofitted.
- -SWE-bench: 77.8% (base GLM-5). Strong, but MiniMax M2.5 hits 80.2 at a quarter of the price.
What Zhipu actually built
GLM-5 Turbo is a variant of Zhipu's GLM-5 model, tuned during training for the specific demands of OpenClaw agent workflows. Most companies take a general-purpose model and adapt it for agent tasks after the fact. Zhipu says they baked the agentic optimization into the training run itself.
In practice, that means more stable tool invocation and fewer errors during multi-step tasks. If you've used OpenClaw with other models, you've probably hit the failure mode where the agent calls a tool with slightly wrong arguments three times in a row before getting it right. Zhipu is claiming GLM-5 Turbo has that problem less often.
Zhipu also launched something called ZClawBench, a proprietary benchmark for OpenClaw agent tasks covering environment setup, software development, and data analysis. They haven't published the scores yet, which makes it hard to evaluate independently.
Pricing and how it compares
At $1.20/1M input and $4.00/1M output, GLM-5 Turbo sits in an awkward middle ground. It's far cheaper than Claude Opus 4.6 ($5/$25) but meaningfully more expensive than the budget options that OpenClaw users tend to reach for.
| Model | Input / 1M | Output / 1M | SWE-bench |
|---|---|---|---|
| MiniMax M2.5 | $0.15 | $0.60 | 80.2 |
| Kimi K2.5 | $0.35 | $1.40 | -- |
| GLM-5 (base) | $1.00 | $3.20 | 77.8 |
| GLM-5 Turbo | $1.20 | $4.00 | ~77.8 |
| DeepSeek R1 | $0.55 | $2.19 | -- |
| Claude Opus 4.6 | $5.00 | $25.00 | 80.9 |
MiniMax M2.5 is 8x cheaper on input and actually scores higher on SWE-bench. Kimi K2.5 is 3.4x cheaper and free on the OpenClaw platform. The value proposition for GLM-5 Turbo has to come from something the benchmarks don't capture: agentic reliability.
The case for paying more
The bull case for GLM-5 Turbo is that standard benchmarks don't measure what matters for agents. SWE-bench tests whether a model can fix a single issue in isolation. OpenClaw workflows are longer, messier, and involve chaining multiple tool calls where one bad invocation can derail the entire run.
If GLM-5 Turbo genuinely fails less on multi-step tool chains, that reliability gap could offset the 4x price difference over MiniMax. A failed agent run that retries three times is more expensive than a successful run with a pricier model. The math depends on your failure rate.
The other angle is the 128K max output. That's unusually high. Most models cap at 8K-32K output tokens. For agent tasks that require generating long files or extensive code, that headroom matters.
The case against
The GLM-5 base model has a reported 30% invalid output rate versus 15% for some competitors. If GLM-5 Turbo inherits that tendency, the "reliability" pitch gets harder to believe. Zhipu hasn't published ZClawBench scores, so we're taking their claims on faith.
There's also the Apiyi analysis that found the best OpenClaw strategy is a hybrid approach: MiniMax M2.5 for routine tasks, Claude Opus 4.6 for the hard stuff. GLM-5 Turbo doesn't obviously fit into that framework. It's too expensive for routine work and not as capable as Opus for the hard problems.
One more thing. Zhipu recently took heat for removing flagship model updates from their pro plan and raising prices. That's not directly relevant to the Turbo model, but it signals a company moving toward extracting more revenue per customer.
Who is Zhipu?
If you haven't heard of them: Zhipu AI (international brand: Z.ai) is a Beijing-based AI lab spun out of Tsinghua University in 2019. They IPO'd on the Hong Kong Stock Exchange in January 2026, making them China's first major LLM company to go public. Current valuation is around $34.5 billion.
Their GLM-5 base model, released February 2026, is a 744B-parameter MoE model under MIT license. It scored 77.8 on SWE-bench Verified, putting it at the top of open-source coding models until MiniMax M2.5 edged past it. Zhipu's stock jumped 8-16% on the GLM-5 Turbo announcement, which tells you the market thinks OpenClaw optimization is a real differentiator.
When GLM-5 Turbo makes sense
- Your OpenClaw agents fail often on tool invocations
- You need 128K output tokens per response
- You want a model purpose-built for agentic work
- You're already in the Zhipu/Z.ai ecosystem
- Cost is your primary concern (MiniMax M2.5 is 8x cheaper)
- You need the highest coding accuracy (Opus 4.6 still wins)
- You want published benchmark proof (ZClawBench scores are MIA)
- Your current model already handles tool calls reliably
Bottom line
GLM-5 Turbo is an interesting bet on a specific thesis: that training a model for OpenClaw from scratch produces better agent behavior than adapting a general-purpose model. The thesis is plausible. The proof is thin.
At $1.20/1M input, it's not a budget option. MiniMax M2.5 and Kimi K2.5 both cost less and have comparable or better benchmark scores. The value of GLM-5 Turbo comes down to whether its agentic-specific training actually reduces failure rates enough to justify the premium. Zhipu hasn't published the data to answer that question yet.
If you're running OpenClaw agents and hitting reliability issues with cheaper models, it's worth testing. See how it stacks up on our best models for OpenClaw page, or compare the full pricing on the pricing table.