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Cerebras vs Z.ai
Z.ai sells cheap GLM models and a flat coding plan from mostly China-based servers. Cerebras sells top speed on a tiny open catalog.
By The Subconscious Team · Updated
Cerebras vs Z.ai: key differences
Z.ai competes on price and packaging. GLM-5.3 costs $1.40 in and $4.40 out, GLM-5.3-Flash costs $0.075 in and $0.25 out, older Flash models are free, and the GLM Coding Plan starts at $18 a month with an Anthropic-compatible endpoint for Claude Code. Cerebras competes on raw speed, serving GPT-OSS 120B near 3,000 tokens per second at $0.35 in and $0.75 out. Its shared catalog as of August 2026 is that model and Gemma 4 31B, with no GLM. The two rarely win the same deal.
Latency cuts in different directions. Z.ai's servers sit mostly in China, which adds 100 to 200ms from the US or Europe and raises data concerns for enterprises. Cerebras removes latency at the generation step, though that does little when an agent is waiting on tools. Budget coding inside Claude Code, and self-hosting under GLM's MIT license, point to Z.ai. Voice agents and streaming UIs that need the fastest tokens available point to Cerebras.
What Cerebras and Z.ai do
Cerebras
Cerebras builds a single chip the size of a silicon wafer, and its inference cloud is the fastest public host on the models it serves. Its developer table lists GPT-OSS 120B near 3,000 tokens per second, about six times Groq on the same weights, at $0.35 in and $0.75 out per million. The public shared catalog is thin, just GPT-OSS 120B and Gemma 4 31B as of August 2026. More model families live on dedicated endpoints and through partners like OpenRouter, Hugging Face, Vercel and AWS Marketplace.
Example models: GPT-OSS 120B, Gemma 4 31B
Full Cerebras profileZ.ai
Z.AI is the international brand of Chinese lab Zhipu AI, maker of the GLM models. Its current flagship, GLM-5.3, shipped August 17, 2026 at $1.40 in and $4.40 out per million tokens, with cached input at $0.26. GLM-5.3-Flash costs $0.075 in and $0.25 out, and several older Flash models are priced at zero, a real free tier instead of trial credits. GLM-5, released in February 2026, is a 744B mixture-of-experts model under an MIT license, and at launch it ranked first among open-weight models on the Artificial Analysis index with a record-low hallucination score.
Example models: GLM-5.3, GLM-5.3-Flash
Full Z.ai profileShould you choose Cerebras or Z.ai?
Cerebras
Choose Cerebras for
- Voice and streaming UIs that need maximum output speed
- US or EU teams avoiding China-based servers
- Low-cost fast outputs on GPT-OSS 120B
Z.ai
Choose Z.ai for
- Budget Claude Code sessions on a flat monthly plan
- Free experimentation on older GLM Flash models
- Self-hosting MIT-licensed GLM weights
Cerebras vs Z.ai at a glance
| Attribute | Cerebras | Z.ai |
|---|---|---|
| Model access | Open weights | Open weights (MIT) |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | GLM-5.3, GLM-5.3-Flash |
| Speed | ~3,000 tok/s on GPT-OSS 120B | ~80 tok/s on GLM-5.3 |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | $1.40 in, $4.40 out (GLM-5.3); free Flash tier |
| Customization | Unknown | Open weights, no license limits |
| Deployment | Shared API, dedicated, partners | API, GLM Coding Plan |
| Long context | Unknown | 1M (GLM-5.3) |
Frequently asked questions
What is the difference between Cerebras and Z.ai?
Z.ai sells cheap GLM models and a flat coding plan from mostly China-based servers. Cerebras sells top speed on a tiny open catalog.
When should I choose Cerebras over Z.ai?
Voice and streaming UIs that need maximum output speed; US or EU teams avoiding China-based servers; Low-cost fast outputs on GPT-OSS 120B.
When should I choose Z.ai over Cerebras?
Budget Claude Code sessions on a flat monthly plan; Free experimentation on older GLM Flash models; Self-hosting MIT-licensed GLM weights.
Is Cerebras or Z.ai cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). Z.ai: $1.40 in, $4.40 out (GLM-5.3); free Flash tier. The cheaper choice depends on the model and workload.
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Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.