GLM-5.1 is the GLM model that made long-horizon work a serious proposition for open weights. It shipped a 200,000-token context, multi-hour autonomous execution and engineering-grade deliverables at a point when most open models were still being judged on single-file tasks. It is also the only other GLM model with a full published benchmark column, which makes it the reference point every GLM-5.2 improvement is quoted against.
Reading the GLM-5.1 column is the fastest way to understand what the following generation actually changed. Where GLM-5.1 posts 63.5 on Terminal-Bench 2.1, 58.4 on SWE-bench Pro and 30.5 on FrontierSWE Dominance, GLM-5.2 posts 81.0, 62.1 and 74.4. The gap is uneven on purpose: standard coding moved a few points, long-horizon agentic execution moved by tens of points. All of these are figures as published by the model authors.
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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.
Two hundred thousand tokens is enough for a substantial service, its tests and a long conversation about them — roughly the working set of a focused feature, not an entire monorepo. In practice that means GLM-5.1 handles well-scoped agentic work comfortably and starts needing a retrieval layer once a task spans many modules. It is also the window at which the previous multi-token-prediction design ran into a training and inference KV-cache mismatch, capping speculative-decoding acceptance length at 4.56; removing that mismatch is one of the specific things the next generation fixed. If your workload fits inside 200K tokens, GLM-5.1 remains a capable, fully open GLM model; if it does not, the jump to the 1M window is the whole reason GLM-5.2 exists.
Scores as published by the model authors.
GLM-5.1's published column is respectable across reasoning and mid-length coding: 95.3 on AIME 2026, 86.2 on GPQA-Diamond, 83.8 on IMOAnswerBench, 71.8 on the MCP-Atlas public set and 58.4 on SWE-bench Pro. Where it visibly runs out of room is ultra-long-horizon work — 30.5 on FrontierSWE Dominance, 20.1 on PostTrainBench and 1.0 on SWE-Marathon. Those three rows are precisely the workloads the following generation was trained for, which is why the deltas there are so much larger than on standard coding evaluations.
Comparison on Terminal-Bench 2.1 (Terminus-2), as published by the model authors.
On Terminal-Bench 2.1 under the Terminus-2 harness, GLM-5.1's 63.5 sits below Qwen3.7-Max at 75.0 and roughly level with DeepSeek-V4-Pro at 64.0, while GLM-5.2 clears the open field at 81.0 and Claude Opus 4.8 leads at 85.0. Read as a lineage, the chart shows a generation of open models bunched in the low sixties and one release breaking out of that band — which is the single clearest argument for treating GLM-5.2 rather than GLM-5.1 as the current open-weight reference.
GLM-5.1 followed GLM-5 and GLM-5-Turbo and was succeeded by GLM-5.2. It is the last GLM model without explicit effort levels, so anything you build against it spends a fixed amount of computation per task rather than letting you dial the trade-off per request. It remains a sensible choice when you want a smaller, more predictable memory footprint than the 1M-context flagship and your tasks genuinely fit inside 200K tokens.
The 1M-token context flagship. It is the open-source state of the art on coding and long-horizon evaluations, with explicit Non-Thinking, High and Max effort levels.
The release that opened the 5 series: stronger coding fundamentals, more reliable multi-step execution and noticeably better behaviour on complex agent tasks.
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.