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Z.ai vs Relace
Relace makes apply, search and compaction models that support a main coding model. Paired with low-cost GLM, it handles the utility work in an agent loop.
By The Subconscious Team · Updated
Z.ai vs Relace: key differences
Relace argues that small specialized models beat frontier LLMs on utility tasks inside a coding agent, and GLM is a natural main model to pair them with. Z.ai's GLM-5.3 costs $1.40 in and $4.40 out and runs in Claude Code through an Anthropic-compatible endpoint. Relace's relace-apply-3 merges a lazy edit snippet from that model into the original file at about 10,000 tokens per second, with 128K tokens of input and output. Its agentic search explores large codebases in parallel, and a compaction model runs at 50,000 tokens per second.
The two differ on deployment and data. Relace offers self-hosted deployment with guided onboarding, which suits enterprises that keep code in-house. Z.ai's servers sit mostly in China, which raises data concerns, but GLM's MIT-licensed weights can be self-hosted too, so a team could keep both the main model and the utility models inside its own walls. Relace is a point solution with no general model serving, and it errors past 128K tokens. GLM covers the reasoning and code writing that Relace does not attempt.
What Z.ai and Relace do
Z.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 profileRelace
Relace trains small, fast models that act as tools for coding agents. Its best-known product is Instant Apply: a frontier model writes a lazy edit snippet, and relace-apply-3 merges it into the original file at about 10,000 tokens per second with 128K tokens of input and output. Relace says this runs over 3x faster and cheaper than having the big model rewrite the file. It exposes both a REST endpoint and an OpenAI-compatible one, and the model is also listed on OpenRouter.
Example models: relace-apply-3, Relace agentic search
Full Relace profileShould you choose Z.ai or Relace?
Z.ai vs Relace at a glance
| Attribute | Z.ai | Relace |
|---|---|---|
| Model access | Open weights (MIT) | Specialist models |
| Flagship models | GLM-5.3, GLM-5.3-Flash | relace-apply-3, agentic search |
| Speed | ~80 tok/s on GLM-5.3 | ~10,000 tok/s apply |
| Price | $1.40 in, $4.40 out (GLM-5.3); free Flash tier | 3x+ cheaper than full rewrites |
| Customization | Open weights, no license limits | Unknown |
| Deployment | API, GLM Coding Plan | Hosted API or self-hosted |
| Long context | 1M (GLM-5.3) | 128K max |
Frequently asked questions
What is the difference between Z.ai and Relace?
Relace makes apply, search and compaction models that support a main coding model. Paired with low-cost GLM, it handles the utility work in an agent loop.
When should I choose Z.ai over Relace?
The main model that writes code in a budget agent; Self-hosting a coding model with no license limits; Flat-rate use in Claude Code.
When should I choose Relace over Z.ai?
Fast merges of GLM's edit snippets; Parallel search across large repos; Self-hosted utility models for in-house code.
Is Z.ai or Relace cheaper?
Z.ai: $1.40 in, $4.40 out (GLM-5.3); free Flash tier. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, Z.ai or Relace?
Z.ai: 1M (GLM-5.3). Relace: 128K max.
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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.