The constraint up front

GPT-5.5 costs $35.00 per million tokens (input + output combined). GLM-5.2 costs $5.80. That price gap is only interesting if the cheaper model can actually do the work — and on the benchmarks that matter most for autonomous coding agents, Z.ai's new model largely can.

What GLM-5.2 is

GLM-5.2 is a 753-billion-parameter large language model (LLM) — a neural network whose behavior is shaped by 753 billion learned numerical weights. Z.ai, the Beijing-based lab formerly known as Zhipu AI, released those weights publicly on Hugging Face under an MIT license on June 17, 2026. MIT is the most permissive mainstream open-source license: it imposes no restrictions on commercial use, modification, or redistribution.

The model targets "long-horizon" tasks — multi-step autonomous workflows that require an agent to plan, execute tool calls, and recover from errors over an extended session, rather than answering a single prompt.

The architecture: IndexShare and MTP

Standard transformer attention recalculates relevance scores across every token in the context window at every layer. At 1 million tokens, that becomes expensive fast. Z.ai's solution, IndexShare, reuses a single sparse-attention indexer across every four consecutive attention layers instead of computing a fresh one per layer. The company reports this reduces per-token floating-point operations (FLOPs) by 2.9× at maximum context length.

The model also ships with an upgraded Multi-Token Prediction (MTP) layer for speculative decoding — a technique where the model drafts several tokens simultaneously and verifies them in parallel, rather than generating one token at a time. Z.ai says this improves accepted token length by up to 20% during inference.

Benchmark results

On SWE-bench Pro, an industry-standard test of autonomous software engineering, GLM-5.2 scored 62.1 against GPT-5.5's 58.6 and its own predecessor GLM-5.1's 58.4. On FrontierSWE, which measures long-horizon task completion, GLM-5.2 reached 74.4% versus GPT-5.5's 72.6%; Claude Opus 4.8 led at 75.1%. On PostTrainBench, which simulates extended multi-hour engineering workloads, GLM-5.2 scored 34.3% against GPT-5.5's 25.0%.

GLM-5.2 trails on Terminal-Bench 2.1, scoring 81.0 versus GPT-5.5's 84.0 and Claude Opus 4.8's 85.0. It also finished behind Claude Opus 4.8 on every benchmark where that model was tested. The picture is competitive, not dominant.

One outlier: GLM-5.2 took first place on Design Arena, a crowdsourced design-task benchmark, with an ELO score of 1360, beating Claude Fable 5.

Thinking modes and cost optimization

GLM-5.2 offers two selectable reasoning effort levels. "Max" mode pushes for peak accuracy but consumes roughly 85,000 output tokens per task. "High" mode cuts that approximately in half while sacrificing only a few benchmark points. For teams running high-volume agentic pipelines, that toggle is a direct cost control.

Cached input tokens are priced at $0.26 per million, with free cached input storage available for a limited period — relevant for long-context workloads that repeatedly reference the same documents.

Pricing and availability

The model is available via the Z.ai API and on more than 20 third-party coding environments including Cline, Kilo Code, and Factory. Z.ai's GLM Coding Plan starts at $12.60 per month (billed annually) for lightweight use, scaling to $112.00 per month for heavy workloads with dedicated peak-hour resources.

For enterprises weighing self-hosting, the MIT license removes the legal friction that typically accompanies open-weight models released under custom or dual-use licenses. The weights can be downloaded, fine-tuned, and deployed on private infrastructure without royalty obligations or vendor-imposed usage policies.