What is GLM-Legal?
Z.ai announced GLM-Legal on October 12, 2026 for legal agent workflows. Its examples cover contract review, evidence analysis and compliance research. The announcement describes legal mid-training and reinforcement learning with an agent harness.
Official benchmark highlights include a 19.2% Harvey LAB all-pass rate, plus LegalBench and PLawBench results. These are provider-reported evaluations, not a guarantee for an individual matter.
GLM-Legal API access and AutoClaw
The launch article links to an API application for the beta. It describes AutoClaw integration as coming soon; the announcement alone does not confirm that integration is available to every account.
| Next step | Official destination |
|---|---|
| Read the announcement | GLM-Legal research article |
| Request API access | Z.ai legal API application |
| Check desktop availability | AutoClaw |
GLM-Legal pricing and model specifications
The reviewed launch article does not specify a GLM-Legal price schedule, context limit, API model ID or downloadable tokenizer. Confirm these with Z.ai before integration. Do not substitute the base model’s specifications or rates.
GLM-Legal tokenizer and token counting
A model name is not enough to select a tokenizer. For a reproducible count, you need the exact vocabulary, normalization rules, special tokens and request formatting used by the serving endpoint. Tiktokenizer has not verified those artifacts for GLM-Legal, so this page opens as a guide rather than an interactive model counter.
You can use the linked OpenAI encoding explorer to study how text splits in its selected encoding. Record the encoding alongside the result and keep that measurement separate from GLM-Legal usage. A reference count cannot establish whether a legal document fits a different model’s context window.
Plan a legal document workflow
The following is a Tiktokenizer integration checklist, not an additional product feature list. Prepare a small evaluation set with expected outputs before committing to a production workflow. Include difficult documents and unsuccessful cases so the evaluation measures more than a polished demonstration.
| Workflow | Evaluation input | Acceptance check |
|---|---|---|
| Contract review | A document with numbered clauses and a written review brief | Each proposed edit identifies its source clause and explains the requested change |
| Evidence analysis | An indexed document bundle with conflicting records | Every factual claim resolves to a document ID; inferences are marked separately |
| Compliance research | A defined market, product scope and research date | References can be checked and outstanding questions have owners |
Budget the complete request
- Keep extracted text, document identifiers and system instructions in your test payload. Include tool definitions and conversation history when your integration sends them.
- Ask the provider how documents and retrieved passages contribute to input usage. Count a complete request rather than only the text visible in a document editor.
- Record returned usage, elapsed time, retries and whether the output met your acceptance criteria. Cost per accepted result is more useful than a token rate alone.
- Keep model version, harness settings and evaluation documents fixed when comparing runs. A change in retrieval or document extraction can change both quality and usage.
- Confirm data handling and access permissions before using client documents. For an initial evaluation, use synthetic or appropriately cleared material and retain a reviewer’s sign-off.
Questions to resolve before integrating
Request a working API example with the exact model identifier and supported request format. Confirm authentication, rate limits, document input support, context and output limits, usage fields, and any separately billed tools. Save the provider’s answers with the date so future changes are easy to reconcile.
Review citations against the original sources and decide who accepts the final work product. A benchmark score should inform your test plan; it should not replace the acceptance criteria for your own documents, jurisdictions and delivery requirements.
Tiktokenizer Reference Sources
Tokenizer behavior and model limits change. Verify production decisions with current provider documentation: