GLM-5. #1 open-source model. Served with mathematical proof of inference.
Every major AI provider has the same gaps. Most enterprises don't realize until it's too late.
Enterprise privacy agreements from major providers exclude reasoning content. Your most sensitive chain-of-thought data has no contractual protection.
Providers silently quantize models, change routing, and update versions without notice. The model you tested isn't always the model you get.
No audit trail. No cryptographic receipt. When compliance or legal asks what model ran and what it produced, you have nothing to show.
Every response includes a cryptographic verification receipt. Prove exactly what input ran on what model, what it produced, and when.
GLM-5 competes with the best closed-source models at a fraction of the cost.
Every API response includes a cryptographic proof that the exact model you requested actually ran. No silent substitutions. No quantization. Full audit trail.
GLM-5 served at full precision, always. 744B parameters with 40B active via MoE architecture. The model you benchmark is the model you get in production.
All data protected by default — including reasoning content that other providers explicitly exclude. Optional end-to-end encryption for maximum security.
Reserved GPU capacity without restrictive enterprise agreements. Scale on your terms at 50% less than major providers. No annual commitments required.
Drop-in compatible with OpenAI and Anthropic SDKs. Switch your base URL and you're done.
Major AI providers' enterprise agreements contain a critical gap: reasoning content is explicitly excluded from data protection clauses.
This means your most sensitive chain-of-thought data — the logic, analysis, and decision-making context flowing through your AI — has no contractual protection.
From trading desks to compliance teams, enterprises choose Ambient for verifiable AI.
No restrictive contracts. Scale on your terms.
Verified inference at scale, every single request.
Get access to GLM-5 with mathematical proof of inference. Drop-in compatible with your existing stack.