z.ai debuts faster, cheaper GLM-5 Turbo model for agents and 'claws' — but it's not open-source
Source: VentureBeat
Z.ai Announces GLM‑5‑Turbo
Chinese AI startup Z.ai (formerly Zhipu AI), known for its powerful open‑source GLM family of large language models (LLMs), has introduced GLM‑5‑Turbo – a proprietary variant of its open‑source GLM‑5 model. The company positions Turbo as a faster model tuned for OpenClaw‑style tasks such as tool use, long‑chain execution, and persistent automation.
- Availability: Via Z.ai’s API on the third‑party provider OpenRouter.
- Context window: ~202.8 K tokens
- Maximum output: 131.1 K tokens
- Pricing (OpenRouter):
- $0.96 per M input tokens
- $3.20 per M output tokens
At 1 M total tokens (input + output) GLM‑5‑Turbo costs ≈ $0.04 less than its predecessor, according to our calculations.
Model Cost Comparison
| Model | Input $/M | Output $/M | Total $/M | Source |
|---|---|---|---|---|
| Grok 4.1 Fast | $0.20 | $0.50 | $0.70 | xAI |
| Gemini 3 Flash | $0.50 | $3.00 | $3.50 | |
| Kimi‑K2.5 | $0.60 | $3.00 | $3.60 | Moonshot |
| GLM‑5‑Turbo | $0.96 | $3.20 | $4.16 | OpenRouter |
| GLM‑5 | $1.00 | $3.20 | $4.20 | Z.ai |
| Claude Haiku 4.5 | $1.00 | $5.00 | $6.00 | Anthropic |
| Qwen3‑Max | $1.20 | $6.00 | $7.20 | Alibaba Cloud |
| Gemini 3 Pro | $2.00 | $12.00 | $14.00 | |
| GPT‑5.2 | $1.75 | $14.00 | $15.75 | OpenAI |
| GPT‑5.4 | $2.50 | $15.00 | $17.50 | OpenAI |
| Claude Sonnet 4.5 | $3.00 | $15.00 | $18.00 | Anthropic |
| Claude Opus 4.6 | $5.00 | $25.00 | $30.00 | Anthropic |
| GPT‑5.4 Pro | $30.00 | $180.00 | $210.00 | OpenAI |
GLM‑5‑Turbo in Z.ai’s GLM Coding Subscription
| Tier | Price (per quarter) | Model Access |
|---|---|---|
| Lite | $27 | GLM‑5 (March) → GLM‑5‑Turbo (April) |
| Pro | $81 | GLM‑5‑Turbo (March) |
| Max | $216 | GLM‑5‑Turbo (March) |
- Early‑access for enterprises is being taken via a Google Form; selected users may receive the model ahead of the public schedule.
Key Positioning & Use‑Cases
- Designed for fast inference and deep optimization for real‑world agent workflows involving long execution chains.
- Improves:
- Complex instruction decomposition
- Tool use
- Scheduled & persistent execution
- Stability across extended tasks
Target scenarios (OpenClaw‑style):
- Information search & gathering
- Office & daily‑task automation
- Data analysis
- Development & operations
- General workflow automation
Z.ai frames GLM‑5‑Turbo as a production‑grade model for autonomous agents rather than a simple chat‑oriented LLM.
Background: Z.ai & GLM‑5
- Founded: 2019 as a Tsinghua University spinoff in Beijing; now one of China’s most prominent foundation‑model companies.
- CEO: Zhang Peng
- IPO: Listed on the Hong Kong Stock Exchange (8 Jan 2026) at HK$116.20 (opening HK$120), market cap HK$52.83 B – the largest independent LLM developer in China.
- Adoption (as of 30 Sep 2025): >12 000 enterprise customers, >80 M end‑user devices, >45 M developers worldwide.
GLM‑5 (Feb 2026) – The Flagship
- Open‑source MIT‑licensed model.
- Record‑low hallucination score on the AA‑Omniscience Index.
- Introduced “Agent Mode”: auto‑generates .docx, .pdf, .xlsx files from prompts.
- Scale: 744 B parameters, 40 B active per token (Mixture‑of‑Experts).
- Training: 28.5 T pre‑training tokens; uses asynchronous RL infrastructure “slime” for reduced bottlenecks and enhanced agentic behavior.
GLM‑5‑Turbo builds on this foundation, keeping the long‑context, agentic orientation while emphasizing speed, stability, and execution for real‑world agent chains.
Developer Features & Packaging
- Long‑context handling (≈ 200 K tokens)
- Tool integration and reasoning support
- Structured output capabilities
- Packaged via OpenRouter (and Z.ai’s own API) for easy consumption in agent‑centric applications.
Note: The source text ends abruptly after “OpenRouter’s GLM‑”. The remaining details were not provided.
GLM‑5‑Turbo vs. GLM‑5: Performance, Tooling, and Market Context
1. Feature & Tool Support
- Tooling: The Turbo page lists support for tools, tool‑choice logic, and response formatting.
- Live telemetry: OpenRouter’s provider telemetry shows a deployment‑level comparison between GLM‑5 and GLM‑5‑Turbo.
- Note: The comparison isn’t perfectly apples‑to‑apples because GLM‑5 appears across several providers, while GLM‑5‑Turbo is shown only through Z.ai.
2. Throughput & Latency
| Metric | GLM‑5‑Turbo (OpenRouter) | Fastest GLM‑5 Endpoints | Other GLM‑5 Endpoints |
|---|---|---|---|
| Throughput (tokens / s) | 48 | Fireworks – 70 Friendli – 58 | Together – 40 |
| First‑token latency (s) | 2.92 | Friendli – 0.41 Parasail – 1.00 DeepInfra – 1.08 | – |
| End‑to‑end completion time (s) | 8.16 | Fireworks – 9.34 DeepInfra – 11.23 | – |
Takeaway: GLM‑5‑Turbo is slower on first‑token latency but faster overall in completing a full request compared with the listed GLM‑5 endpoints.
3. Tool‑Reliability
- Tool‑call error rate: 0.67 % (GLM‑5‑Turbo)
- GLM‑5 providers: error rates range from 2.33 % to 6.41 %
Implication for enterprise teams: While GLM‑5‑Turbo may not win on initial responsiveness in its current OpenRouter routing, its markedly lower tool‑failure rate makes it attractive for longer‑running agent workloads where stability matters more than the fastest first token.
Benchmarking & Pricing
- ZClawBench radar chart (z.ai): Highlights GLM‑5‑Turbo’s competitiveness in OpenClaw scenarios such as:
- Information search & gathering
- Office & daily tasks
- Data analysis
- Development & operations
- Automation
These visuals are company‑supplied, not independent validation, but they illustrate how Z.ai positions the two models:
- GLM‑5 – Broad coding and open‑flagship model
- GLM‑5‑Turbo – Targeted, agent‑execution variant
Licensing Nuance
- Current status: GLM‑5‑Turbo is closed‑source.
- Future promise: Z.ai states that the model’s capabilities and findings will be folded into its next open‑source model release.
- The company is not promising to open‑source GLM‑5‑Turbo itself, only to incorporate lessons into a future open model.
Historical Context
- Z.ai’s earlier GLM strategy emphasized open releases and open‑weight distribution, which helped it gain visibility among developers.
China’s AI Market: A Shift Toward Hybrid Strategies
Recent Industry Moves
- Alibaba’s Qwen unit – Recent reporting (Reuters, March 16) shows:
- Qwen division head Lin Junyang stepped down (third senior Qwen exec to leave in 2026).
- Alibaba CEO Eddie Wu will directly control a newly formed AI‑focused business group consolidating Qwen and other units.
- The move follows intense price competition and scrutiny over strategy and profitability of open‑model offerings in China.
Emerging Pattern
- Open models continue to drive adoption, developer goodwill, and ecosystem reach.
- High‑value variants (enterprise agents, coding workflows, etc.) are increasingly released first as proprietary products.
This mirrors the U.S. playbook (OpenAI, Anthropic, Google):
- Open = distribution & community building
- Proprietary = primary revenue stream
Implications for GLM‑5‑Turbo
The launch signals a potential shift in China’s AI sector toward a hybrid model:
- Open for broad distribution
- Closed for strategically important, agent‑focused offerings
Future trajectory: Underlying advances from GLM‑5‑Turbo may eventually surface in open releases, but the most commercially relevant work may first appear behind closed access for enterprise‑grade agent systems.
What This Means for Developers
- Product launch: GLM‑5‑Turbo offers solid throughput, competitive end‑to‑end latency, and exceptionally low tool‑error rates.
- Strategic signal: Z.ai continues to speak the language of open models, yet high‑impact, agent‑centric capabilities are now being delivered as proprietary infrastructure.
Bottom line: When evaluating agent platforms, consider both the technical merits (speed, reliability) and the licensing/availability roadmap (closed now, potential open‑source downstream). This dual lens will help you decide whether GLM‑5‑Turbo aligns with your short‑term needs and long‑term openness goals.