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Anthropic vs Z.ai
Z.ai's Anthropic-compatible endpoint lets Claude Code run on GLM models for a flat monthly fee. It is a budget substitute for Claude, with latency and data trade-offs from China-based servers.
By The Subconscious Team · Updated
Anthropic vs Z.ai: key differences
Z.ai competes with Anthropic inside Anthropic's own tooling. Its Anthropic-compatible endpoint lets Claude Code run on GLM with a few environment variables, and the GLM Coding Plan starts at $18 a month on the Lite tier, with a quota Z.ai says equals 15 to 30x the fee at API rates. Per token, GLM-5.3 lists at $1.40 in and $4.40 out, against $10 and $50 for Fable 5.1 and $1 and $5 for Haiku 4.5. GLM-5 weights are MIT-licensed with no license limits, and GLM-5.3-Flash and some older Flash models are cheap or free.
Claude remains the stronger coding model on real-world benchmarks, and its 1M window carries no long-context premium. Z.ai's servers sit mostly in China, adding 100 to 200ms from the US or Europe and raising data concerns for enterprises, and the Coding Plan quota burns 2 to 3x faster on premium models during Beijing peak hours. Individual developers and budget coding setups get a lot from Z.ai. Enterprise teams that need data kept out of China, or the best available coding quality, stay with Anthropic.
What Anthropic and Z.ai do
Anthropic
Anthropic sells the Claude family of closed models through its own API, Amazon Bedrock, Google Vertex AI and Microsoft Foundry. The public lineup today runs from Claude Fable 5.1 at the top, released September 1, 2026, through the Opus and Sonnet tiers down to Haiku 4.5. List prices span a tenfold range, from $10 in and $50 out on Fable to $1 in and $5 out on Haiku. The top three tiers include a 1M token context window at standard pricing with no surcharge past 200K.
Example models: Claude Fable 5.1, Claude Haiku 4.5
Full Anthropic 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 Anthropic or Z.ai?
Anthropic vs Z.ai at a glance
| Attribute | Anthropic | Z.ai |
|---|---|---|
| Model access | Closed | Open weights (MIT) |
| Flagship models | Claude Fable 5.1, Opus, Sonnet, Haiku 4.5 | GLM-5.3, GLM-5.3-Flash |
| Speed | Fable is the slowest tier | ~80 tok/s on GLM-5.3 |
| Price | $1–$10 in, $5–$50 out per 1M | $1.40 in, $4.40 out (GLM-5.3); free Flash tier |
| Customization | N/A | Open weights, no license limits |
| Deployment | API, Bedrock, Vertex AI, Microsoft Foundry | API, GLM Coding Plan |
| Long context | 1M, no surcharge past 200K | 1M (GLM-5.3) |
Frequently asked questions
What is the difference between Anthropic and Z.ai?
Z.ai's Anthropic-compatible endpoint lets Claude Code run on GLM models for a flat monthly fee. It is a budget substitute for Claude, with latency and data trade-offs from China-based servers.
When should I choose Anthropic over Z.ai?
Enterprise coding agents with data residency constraints; Top coding quality inside Claude Code; Low latency from US or European offices.
When should I choose Z.ai over Anthropic?
Budget Claude Code usage on a flat monthly plan; Self-hosting MIT-licensed GLM weights; Free Flash tiers for prototyping.
Is Anthropic or Z.ai cheaper?
Anthropic: $1–$10 in, $5–$50 out per 1M. Z.ai: $1.40 in, $4.40 out (GLM-5.3); free Flash tier. The cheaper choice depends on the model and workload.
Which has more context, Anthropic or Z.ai?
Anthropic: 1M, no surcharge past 200K. Z.ai: 1M (GLM-5.3).
Related comparisons
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.