GLM-5.2 is the flagship open-weight GLM model, built for long-horizon task completion and agentic coding rather than single-turn chat. It is the release that took the family's usable context from 200,000 tokens to a solid one million, introduced explicit effort levels, and rebuilt the sparse-attention stack around a shared indexer. For a team evaluating open-weight alternatives to the closed frontier, this is the GLM model that actually competes on the evaluations those teams care about — and it does so under an MIT license with weights published openly.
Measured against its predecessor GLM-5.1, GLM-5.2 is not an incremental refresh. Terminal-Bench 2.1 moves from 63.5 to 81.0, DeepSWE from 18.0 to 46.2 and FrontierSWE Dominance from 30.5 to 74.4 — all figures as published by the model authors, none of them measured here. The practical consequence is that a coding agent can hold an entire repository, its test output and its own prior attempts inside one trajectory instead of re-reading fragments through a retrieval layer.
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This playground runs a free open-source LLM via OpenRouter. It is not GLM-5.2 and does not use GLM weights — it's here so you can test a live model while you read the published GLM benchmark data below.
A one-million-token context changes what you build, not just how much you can paste. Instead of a retrieval pipeline feeding the model small, lossy snippets, you can load a whole repository, a complete API reference or several days of logs into one prompt and let the model reason across all of it at once. That is what makes cross-file dependencies and systemic failures visible: the bug that only appears when the service boundary, the migration and the test fixture are all in view at the same time. GLM-5.2 was trained on long-context coding-agent trajectories covering large-scale implementation, automated research, performance optimisation and complex debugging, which is why the model authors describe the window as solid rather than merely accepted — quality is meant to hold deep into the context instead of degrading once a run gets messy.
Scores as published by the model authors.
The published GLM-5.2 column is strongest exactly where the model was designed to be: agentic coding and long-horizon execution. It posts 81.0 on Terminal-Bench 2.1 under the Terminus-2 harness and 82.7 under Claude Code, 76.8 on the MCP-Atlas public set, and 74.4 on FrontierSWE Dominance, where individual tasks run up to twenty hours. On pure reasoning it reports 99.2 on AIME 2026 and 91.2 on GPQA-Diamond. Across all three long-horizon evaluations — FrontierSWE, PostTrainBench and SWE-Marathon — it is the highest-ranked open-source model in the published comparison set.
Comparison on SWE-bench Pro, as published by the model authors.
On SWE-bench Pro, GLM-5.2's 62.1 trails Claude Opus 4.8's 69.2 but clears GPT-5.5 at 58.6 and Gemini 3.1 Pro at 54.2 — and it clears its own predecessor GLM-5.1 at 58.4. That shape repeats across the table: the closed frontier still leads several standard coding evaluations, while GLM-5.2 sits at the top of the open-weight field and takes individual rows outright, including Terminal-Bench 2.1 under the Claude Code harness (82.7 against 78.9) and AIME 2026 (99.2 against 95.7).
GLM-5.2 is the current head of a line that runs GLM-4.5-Air and GLM-4.7 through GLM-5, GLM-5-Turbo and GLM-5.1. Each step carried a different priority — GLM-4.7 focused on programming and front-end generation, GLM-5-Turbo on long-chain agent execution, GLM-5.1 on multi-hour autonomous work — and GLM-5.2 folds those gains into one model with a far larger window. If your hardware budget rules out the flagship, the lightweight GLM-4.7-Flash and GLM-4.5-Air tiers are the same family under the same MIT license.
The previous flagship. It brought the first large long-horizon gains to the family and remains the baseline every GLM-5.2 improvement is measured against.
A 5-series base model tuned for dynamic, long-chain agent scenarios where the plan changes repeatedly and the trajectory has to stay executable.
Read the GLM model benchmarks, then put a real model to work. The playground above is free and needs nothing from you; if you want a fuller AI toolkit, our partner's free tier starts here.