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GLM-5.1
It aligns with the very best global models like Claude Opus 4.6 while excelling where it matters most: long-horizon planning, iterative optimization, and end-to-end delivery.
Z.AI's most powerful language model yet. GLM-5.1 doesn't just generate answers — it plans, executes, iterates, and delivers. Built for long-horizon autonomous tasks with a 200K context window and 128K token output capacity.
GLM-5.1 is the latest flagship model from Z.AI (formerly Zhipu AI), the Chinese AI lab behind the GLM family of large language models. It marks a meaningful shift in how modern AI models are evaluated, not just on single-turn intelligence, but on how long they can work autonomously on a complex, multi-stage goal.
Where most language models are optimized for fast, isolated interactions, GLM-5.1 is purpose-built for tasks that require sustained effort: multi-hour engineering workflows, iterative optimization loops, and production-grade deliverables that span dozens of dependent steps. The model can plan, execute, encounter errors, course-correct, and finish without needing a human to hold its hand at each checkpoint.
In terms of raw capability, GLM-5.1 is benchmarked against the world's best. On overall performance, it aligns closely with Claude Opus 4.6, making it one of the few models genuinely competitive at the frontier level. On coding specifically, particularly on real-world software engineering tasks, it surpasses all other models on SWE-Bench Pro with a score of 58.4.
GLM-5.1's SWE-Bench Pro score of 58.4 is a new state-of-the-art result, outperforming GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. This benchmark tests real GitHub issue resolution on production codebases, arguably the most demanding proxy for real-world software engineering ability available today.
GLM-5.1 ranks among the world's top-tier models in both overall ability and coding performance. Overall performance aligns with Claude Opus 4.6, while coding performance on SWE-Bench Pro surpasses every other model in the world, setting a new state-of-the-art. Across 12 representative benchmarks spanning reasoning, coding, agents, tool use, and browsing, the model demonstrates a broad, balanced capability profile, not a narrow spike.
GLM-5.1 shows especially strong improvements on long-horizon tasks, with major gains in sustained execution, closed-loop optimization, and engineering delivery under complex objectives. Under standardized evaluation, it is one of the few models capable of 8-hour autonomous execution and the first Chinese model to reach that level. This isn't just about a longer context window. It's about the model staying on-task across hundreds of decisions without losing the plot.
GLM-5.1's most significant breakthrough is its ability to form a genuine "experiment → analyze → optimize" loop in long-horizon tasks, rather than stopping at one-shot generation. The model can proactively run benchmarks, identify bottlenecks, adjust strategy, and iterate. In practice: it built a complete Linux desktop system from scratch in 8 hours, autonomously ran 655 optimization iterations on a vector database, and achieved a 3.6× geometric mean speedup on KernelBench Level 3, dramatically exceeding what torch.compile max-autotune achieves.
Six high-value use cases where GLM-5.1's combination of sustained execution, reasoning depth, and creative output genuinely outperforms lighter-weight alternatives.
Further optimized for agentic coding workflows including environments like Claude Code and OpenClaw. GLM-5.1 handles long-horizon planning, stepwise execution, process adjustment, and final delivery. It performs significantly better on long-running development tasks and complex problems with multiple stages and strong interdependencies, making it the right choice when you need code that actually ships, not just code that compiles.
More robust in open-ended Q&A, complex instruction following, and multi-turn interactions. Responses are richer, more complete, and consistently adhere to instructions, even across long conversation threads. It handles complex information workflows and context-heavy professional assistance with noticeably better quality than previous GLM generations.
Genuine improvements in literary expression, plot development, character portrayal, and style control. GLM-5.1 can sustain a consistent narrative voice across long-form work, a known weakness in earlier models. Suitable for fiction drafts, story development, editorial copywriting, and brand voice tasks that demand expressive consistency over extended output.
Well suited for website generation, interactive pages, and front-end prototyping. GLM-5.1 outputs show less templated structure than typical AI-generated code, with more diverse visual expression and higher overall task completion quality. This translates into faster turnaround from written requirements to usable, deployable deliverables.
Broadly improved across PowerPoint, Word, PDF, and Excel-related tasks. GLM-5.1 handles complex content organization, layout design, and structured output with stronger default visual quality and overall polish. Useful for high-intensity production tasks: long-form reports, research papers, teaching materials, formatted documentation, and executive-level slide decks.
One of GLM-5.1's most compelling capabilities, demonstrated by its 3.6× speedup on KernelBench and 655-iteration vector database optimization loop. In practice, this means using the model to profile, benchmark, hypothesize, patch, rerun, and iterate on performance-critical systems with minimal human intervention. The "experiment–analyze–optimize" loop runs autonomously until a stopping condition is met.
On general benchmarks, GLM-5.1 is overall aligned with Claude Opus 4.6 — one tier above Sonnet-class models. On real-world software engineering (SWE-Bench Pro), it currently leads all published models. For long-horizon autonomous tasks, it's in a class of its own.
It means the model can be given a complex, multi-stage engineering task, like building a system, optimizing a performance bottleneck, or writing a comprehensive codebase — and continue working autonomously without needing human prompts at each step. It plans, executes, tests, finds failures, adjusts course, and repeats until done.
No, the 200K refers to the input context window. Output tokens are separate, up to a maximum of 128K tokens per response. This makes GLM-5.1 exceptionally capable for tasks requiring both large input ingestion (full codebases, long documents) and extensive output generation (full reports, complete programs).
GLM-5.1 is the latest flagship model from Z.AI (formerly Zhipu AI), the Chinese AI lab behind the GLM family of large language models. It marks a meaningful shift in how modern AI models are evaluated, not just on single-turn intelligence, but on how long they can work autonomously on a complex, multi-stage goal.
Where most language models are optimized for fast, isolated interactions, GLM-5.1 is purpose-built for tasks that require sustained effort: multi-hour engineering workflows, iterative optimization loops, and production-grade deliverables that span dozens of dependent steps. The model can plan, execute, encounter errors, course-correct, and finish without needing a human to hold its hand at each checkpoint.
In terms of raw capability, GLM-5.1 is benchmarked against the world's best. On overall performance, it aligns closely with Claude Opus 4.6, making it one of the few models genuinely competitive at the frontier level. On coding specifically, particularly on real-world software engineering tasks, it surpasses all other models on SWE-Bench Pro with a score of 58.4.
GLM-5.1's SWE-Bench Pro score of 58.4 is a new state-of-the-art result, outperforming GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. This benchmark tests real GitHub issue resolution on production codebases, arguably the most demanding proxy for real-world software engineering ability available today.
GLM-5.1 ranks among the world's top-tier models in both overall ability and coding performance. Overall performance aligns with Claude Opus 4.6, while coding performance on SWE-Bench Pro surpasses every other model in the world, setting a new state-of-the-art. Across 12 representative benchmarks spanning reasoning, coding, agents, tool use, and browsing, the model demonstrates a broad, balanced capability profile, not a narrow spike.
GLM-5.1 shows especially strong improvements on long-horizon tasks, with major gains in sustained execution, closed-loop optimization, and engineering delivery under complex objectives. Under standardized evaluation, it is one of the few models capable of 8-hour autonomous execution and the first Chinese model to reach that level. This isn't just about a longer context window. It's about the model staying on-task across hundreds of decisions without losing the plot.
GLM-5.1's most significant breakthrough is its ability to form a genuine "experiment → analyze → optimize" loop in long-horizon tasks, rather than stopping at one-shot generation. The model can proactively run benchmarks, identify bottlenecks, adjust strategy, and iterate. In practice: it built a complete Linux desktop system from scratch in 8 hours, autonomously ran 655 optimization iterations on a vector database, and achieved a 3.6× geometric mean speedup on KernelBench Level 3, dramatically exceeding what torch.compile max-autotune achieves.
Six high-value use cases where GLM-5.1's combination of sustained execution, reasoning depth, and creative output genuinely outperforms lighter-weight alternatives.
Further optimized for agentic coding workflows including environments like Claude Code and OpenClaw. GLM-5.1 handles long-horizon planning, stepwise execution, process adjustment, and final delivery. It performs significantly better on long-running development tasks and complex problems with multiple stages and strong interdependencies, making it the right choice when you need code that actually ships, not just code that compiles.
More robust in open-ended Q&A, complex instruction following, and multi-turn interactions. Responses are richer, more complete, and consistently adhere to instructions, even across long conversation threads. It handles complex information workflows and context-heavy professional assistance with noticeably better quality than previous GLM generations.
Genuine improvements in literary expression, plot development, character portrayal, and style control. GLM-5.1 can sustain a consistent narrative voice across long-form work, a known weakness in earlier models. Suitable for fiction drafts, story development, editorial copywriting, and brand voice tasks that demand expressive consistency over extended output.
Well suited for website generation, interactive pages, and front-end prototyping. GLM-5.1 outputs show less templated structure than typical AI-generated code, with more diverse visual expression and higher overall task completion quality. This translates into faster turnaround from written requirements to usable, deployable deliverables.
Broadly improved across PowerPoint, Word, PDF, and Excel-related tasks. GLM-5.1 handles complex content organization, layout design, and structured output with stronger default visual quality and overall polish. Useful for high-intensity production tasks: long-form reports, research papers, teaching materials, formatted documentation, and executive-level slide decks.
One of GLM-5.1's most compelling capabilities, demonstrated by its 3.6× speedup on KernelBench and 655-iteration vector database optimization loop. In practice, this means using the model to profile, benchmark, hypothesize, patch, rerun, and iterate on performance-critical systems with minimal human intervention. The "experiment–analyze–optimize" loop runs autonomously until a stopping condition is met.
On general benchmarks, GLM-5.1 is overall aligned with Claude Opus 4.6 — one tier above Sonnet-class models. On real-world software engineering (SWE-Bench Pro), it currently leads all published models. For long-horizon autonomous tasks, it's in a class of its own.
It means the model can be given a complex, multi-stage engineering task, like building a system, optimizing a performance bottleneck, or writing a comprehensive codebase — and continue working autonomously without needing human prompts at each step. It plans, executes, tests, finds failures, adjusts course, and repeats until done.
No, the 200K refers to the input context window. Output tokens are separate, up to a maximum of 128K tokens per response. This makes GLM-5.1 exceptionally capable for tasks requiring both large input ingestion (full codebases, long documents) and extensive output generation (full reports, complete programs).
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