LLMz-ai

Z.ai: GLM 5.2

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...

Anyone in the Project can @-mention Z.ai: GLM 5.2 with the team's shared context - pooled credits, one chat, one memory.

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Starter is free forever - 1 Project, 100 credits/month, 1 MCP. No card.

Verdict

GLM 5.2 offers a massive 1M-token context window at roughly half the cost of comparable long-context models like Claude 3.5 Sonnet. It handles extended documents and multi-turn conversations without the memory pressure that forces chunking strategies elsewhere. The trade-off: no public benchmark data yet, so performance on reasoning and coding tasks remains unproven relative to established alternatives. Reach for this when budget and context length matter more than battle-tested reliability.

Best for

Multi-document analysis under budget constraints
Long-form content generation with deep context
Extended conversation threads in customer support
Legal or technical document review at scale

Strengths

The 1M-token context window puts entire codebases, book-length documents, or weeks of chat history in scope without retrieval hacks. At $0.97 per million input tokens, it undercuts most long-context competitors by 40-60%, making it viable for high-volume applications where context retention drives value. The pricing structure favors read-heavy workloads—ideal when you need the model to digest far more than it generates.

Trade-offs

No MMLU, HumanEval, or other standard benchmarks are publicly available, so you're flying blind on reasoning depth and code quality compared to GPT-4, Claude, or Gemini. The output cost of $3.04/Mtok sits above budget leaders like Gemini 1.5 Flash, limiting appeal for generation-heavy tasks. Early-stage model means fewer community reports on edge-case behavior, prompt sensitivity, and multilingual performance.

Specifications

Provider: z-ai
Category: llm
Context length: 1,048,576 tokens
Max output: 131,072 tokens
Modalities: text
License: proprietary
Released: 2026-06-16

Pricing

Input: $0.97/Mtok
Output: $3.04/Mtok
Model ID: z-ai/glm-5.2

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$27.93

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

Provider	Context	Input	Output	P50 latency	Throughput	30d uptime
z-ai	1024k	$0.74/Mtok	$2.33/Mtok	—	—	—

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Synthesize Multi-Document Insights

I'm pasting five research papers below. Identify the three most significant points of agreement and the two sharpest disagreements across all authors. Quote specific passages to support each finding.

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Audit Full Codebase Logic

Here's the complete source tree for a Flask API (12,000 lines). List every endpoint that accepts user input without validation, and flag any SQL queries vulnerable to injection.

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Draft Long-Form Content

Using the 20 product spec documents I've provided, write a 3,000-word technical white paper explaining our new feature set for enterprise buyers. Cite specific capabilities from the specs.

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Maintain Conversation Context

This is a 200-message support thread. The customer just asked about the refund policy again. Provide a response that acknowledges their earlier concerns from messages 47 and 132 without making them repeat themselves.

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Compare Contract Versions

I'm attaching four versions of the same vendor agreement from 2021 to 2024. Highlight every substantive change to liability caps, termination clauses, and data retention terms.

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