TL;DR: Z.AI's GLM-5.1 scored 58.4 on SWE-Bench Pro, clearing GPT-5.4 (57.7) and Claude Opus 4.6 (57.3) by a 0.7-point margin. It's a 754B MoE model with 40B active parameters, released under MIT license on April 7, 2026. The benchmark lead is real. Running this locally requires at minimum 1× NVIDIA HGX B200 or comparable enterprise infrastructure — no consumer hardware path exists at full precision.

• GLM-5.1 scored 58.4 on SWE-Bench Pro — the highest published score by any model, open or closed, as of April 2026.
• The gap over GPT-5.4 (57.7) is 0.7 points — within single-run variance on this benchmark.
• 754B MoE with 40B active parameters per token: inference compute cost is proportional to the active 40B, not total weight.
• Full FP8 deployment requires approximately 1.49 TB storage and enterprise multi-GPU infrastructure; 2-bit quant fits on 1× 24 GB GPU with 256 GB system RAM.
• The 8-hour autonomous execution demo (655 iterations, Linux desktop) is a controlled single-task test — not a general agentic capability guarantee.
• Weights available on HuggingFace under MIT license; API access live on Lambda Labs, Together AI, and Atlas Cloud.

GLM-5.1 SWE-Bench Pro Benchmark Results: What 58.4 Actually Means for Open-Weight AI

Z.AI released GLM-5.1 weights on April 7, 2026. The model scored 58.4 on SWE-Bench Pro, clearing GPT-5.4 at 57.7 and Claude Opus 4.6 at 57.3. SWE-Bench Pro is the most demanding software engineering evaluation currently in wide use — 1,507 real GitHub issues evaluated on whether the model produces a patch that passes the target repository's existing test suite. A 58.4 is the highest published score on that benchmark by any model, open or closed.

What the headline doesn't show: the margin over the second-place model is 0.7 points. The hardware floor for running this model at full precision is enterprise multi-GPU infrastructure that most ML teams don't have. The 8-hour autonomous execution demonstration was a controlled, single-task test in a sandboxed environment. And the 58.4 score was achieved using GLM-5.1's native agentic scaffold — a setup detail that meaningfully affects benchmark performance and is rarely standardized across labs.

If you're evaluating GLM-5.1 for production use or research, the architecture, hardware constraints, and benchmark methodology details matter more than the top-line score. This post covers each of those in order.

The 0.7-Point Lead at the Top of SWE-Bench Pro

The SWE-Bench Pro leaderboard as of April 8, 2026: GLM-5.1 at 58.4, GPT-5.4 at 57.7, Claude Opus 4.6 at 57.3, Gemini 3.1 Pro at 55.1. The spread between first and third is 1.1 points. That's a real gap on a benchmark this difficult — each percentage point on SWE-Bench Pro represents roughly 15 additional passing patches out of 1,507 evaluated issues. But it's not the kind of gap that survives a scaffold change or a different sample of issues.

Benchmark methodology varies across labs in ways that aren't always disclosed. Z.AI ran GLM-5.1 with its native agentic scaffold. Other labs use scaffold implementations that differ in tool call structure, retry logic, and context window management. A model that scores 57.3 with one scaffold might score 58.6 with a different one. SWE-Bench's own documentation notes that scaffold implementation is the single largest source of score variance across submitted results.

The Gemini 3.1 Pro gap — 3.3 points behind GLM-5.1 — is more stable and more interpretable. Gemini 3.1 Pro outperforms every model on ARC-AGI-2 (77.1%) and GPQA-Diamond (94.3%), but SWE-Bench Pro specifically rewards models that can navigate real repository structure, identify minimal diffs, and avoid breaking adjacent tests. That skill is different from abstract reasoning, and the benchmark ordering reflects it. One trade-off the score doesn't surface: wall-clock time and token cost per resolved issue. A 58.4 success rate at 40 minutes per patch is a different operational reality than 57.3 at 8 minutes.

MoE Architecture: Why 40B Active Parameters Out of 754B Actually Matters

GLM-5.1 is a Mixture-of-Experts model with 754B total parameters, 40B active per token, organized across 256 experts and 80 transformer layers. The sparse attention mechanism — DSA, adapted from DeepSeek's architecture — dynamically routes attention resources based on token importance, which reduces memory bandwidth consumption in long-context inference. The 200K context window with 128K maximum output is practically usable at this attention configuration in a way that dense models at comparable total parameter counts typically aren't.

The compute efficiency argument for MoE is concrete: you pay per-token compute proportional to 40B active parameters, not 754B total. At low-to-medium concurrency — the operating range for most enterprise and research deployments — a well-optimized MoE serving setup running GLM-5.1 at FP8 costs meaningfully less per token than a 70B dense model under equivalent load. The efficiency advantage narrows at high batch sizes where GPU utilization becomes the bottleneck rather than memory bandwidth, but for typical use cases the gap is real.

The trade-off is memory, not compute. MoE inference requires all expert weights loaded simultaneously, because the routing decision happens at forward pass time — you don't know which experts activate until the token is processed. That's why the full FP8 footprint is 1.49 TB despite only 40B active parameters per token. The MoE efficiency gain is entirely compute efficiency. Memory efficiency is worse than an equivalent dense model at the same active parameter count.

The 8-Hour Autonomous Execution Demo — What It Shows and Doesn't

Z.AI's most-cited demonstration: GLM-5.1 built a complete Linux desktop environment from scratch over 8 hours, running 655 autonomous iterations without human intervention. The environment included a file browser, terminal emulator, text editor, system monitor, and playable games. The model executed an experiment–analyze–optimize loop throughout, identifying performance bottlenecks and rewriting components across the full session.

What this establishes: GLM-5.1 can sustain coherent goal-directed behavior across 655 sequential steps without context collapse or goal drift severe enough to abandon the task. That's technically meaningful. Most models attempting this kind of long-horizon autonomous execution fail significantly earlier — typically in the 50–150 step range — due to context window saturation, repeated tool call failures, or drifting task representation. Sustaining 655 iterations is a real capability signal.

What it doesn't establish: how well GLM-5.1 performs on arbitrary long-horizon tasks your team defines. The Linux desktop task has properties that favor controlled demonstration — a finite, verifiable goal, a sandboxed environment with known state, and clear per-step success criteria. Production agentic workflows have ambiguous stopping conditions, non-deterministic external state, and error modes the model wasn't specifically trained to handle. Z.AI hasn't published the number of runs attempted before the reported result, or the success rate across repeated runs of the same task. Those numbers would substantially change how to interpret the demo.

Hardware Requirements: Who Can Actually Run This

Full FP8: approximately 1.49 TB storage, minimum 1× NVIDIA HGX B200 or equivalent multi-GPU infrastructure. This is not a configuration available on consumer hardware. It's also not available on most university compute clusters or standard cloud GPU instances. The practical access path for most researchers and practitioners is via API endpoints — Lambda Labs, Together AI, and Atlas Cloud had GLM-5.1 available within days of the open-weight release.

Quantized configurations reduce the hardware requirement significantly, with corresponding quality trade-offs. The 2-bit dynamic quantization (UD-IQ2_M) brings disk footprint to approximately 236 GB and runs on a single 24 GB GPU with MoE offloading and 256 GB system RAM. Performance on complex multi-step reasoning tasks at 2-bit quantization degrades visibly — this configuration is useful for research exploration, evaluating model behavior, and running targeted experiments. It's not a viable configuration for production code generation pipelines where patch quality matters.

Supported inference frameworks: SGLang (v0.5.10+), vLLM (v0.19.0+), xLLM (v0.8.0+), Transformers (v0.5.3+), and KTransformers (v0.5.3+). The MIT license on the weights means you can deploy commercially without restriction, which matters for teams evaluating closed versus open-weight options from a licensing standpoint.

GLM-5.1 vs Competing Frontier Models — By Benchmark

The GPQA-Diamond gap between GLM-5.1 (86.2) and Gemini 3.1 Pro (94.3) is 8.1 points — substantially larger than the SWE-Bench Pro gap. For tasks requiring deep scientific domain reasoning, Gemini 3.1 Pro holds a clear advantage. For agentic software engineering specifically, GLM-5.1's SWE-Bench Pro lead is the most directly relevant signal. The open-weight availability under MIT is the most distinctive characteristic: it's the only model in this comparison you can audit, fine-tune, and deploy without API dependency.

When You Should NOT Use GLM-5.1

If your team's infrastructure tops out at consumer GPU hardware. The 2-bit quant path technically runs, but quality degradation on complex reasoning tasks makes it unsuitable for production code generation or research where patch correctness matters. Use the API instead, or evaluate a smaller open-weight model at higher precision.

If GPQA-Diamond performance is the primary benchmark for your use case. Gemini 3.1 Pro's 8.1-point advantage on expert-level science reasoning is stable across independent evaluations. For teams working on scientific literature analysis, chemistry, or biomedical reasoning tasks, the SWE-Bench Pro ordering doesn't predict which model will perform better on your actual workload.

If your agentic workflow requires low-latency, short-horizon tasks at high volume. GLM-5.1 is optimized for long-horizon, complex tasks. The context window and architecture are designed for sustained multi-step execution. For simple, high-throughput tasks — code completion, classification, short-form generation — a smaller, faster model will deliver better cost-per-token economics with equivalent output quality.

• ☐ Does your team have access to enterprise multi-GPU infrastructure or a budget for API-based inference?
• ☐ Is your primary task agentic software engineering, code review, or long-horizon code generation?
• ☐ Do you need open weights for fine-tuning, auditing, or on-premises deployment?
• ☐ Is your workload in the domain where SWE-Bench Pro performance is the most relevant signal?

Three or four checked: GLM-5.1 is worth evaluating seriously, starting with API access before committing to infrastructure investment. One or fewer: a different model at a better hardware fit will serve your use case more efficiently.

FAQ

How does GLM-5.1 differ from GLM-5, released earlier in 2026?

GLM-5.1 is a post-training upgrade to GLM-5. The base architecture is the same 754B MoE with 40B active parameters. GLM-5.1 adds improved agentic scaffolding, better long-horizon task coherence from asynchronous reinforcement learning, and the higher SWE-Bench Pro score (58.4 vs the GLM-5 baseline). The open weights were released on April 7, 2026 under the same MIT license.

Can I fine-tune GLM-5.1 on my own data?

Technically yes — the MIT license permits it. Practically, fine-tuning a 754B MoE model requires infrastructure most teams don't have. LoRA fine-tuning on specific expert layers is more feasible than full-model fine-tuning but still requires significant GPU memory. Evaluate your hardware constraints against the expected gain before investing in a fine-tuning run.

Is the SWE-Bench Pro score independently verified?

Z.AI submitted the results and published the scaffold configuration used. As of April 8, 2026, independent replications have not been published. SWE-Bench is a standardized evaluation, but scaffold implementation differences across labs are documented to produce score variance. Treat the 58.4 as a lab-reported result pending independent replication.

What inference frameworks work best with GLM-5.1?

SGLang (v0.5.10+) and vLLM (v0.19.0+) are the recommended paths for production serving. SGLang's MoE-optimized routing generally shows better throughput than vLLM at low concurrency. For research and experimentation at quantized precision, KTransformers (v0.5.3+) with MoE offloading is the most accessible starting point on constrained hardware.

How does the MIT license affect commercial use?

MIT permits commercial use, modification, and redistribution without restriction. You can fine-tune and deploy GLM-5.1 in a commercial product, build services on top of it, and distribute modified versions. No royalties, no usage reporting requirements. The only condition is that the MIT license text is preserved in distributions of the weights or derivative works.

Conclusion: Next Steps

GLM-5.1's SWE-Bench Pro result is the most significant open-weight coding benchmark since DeepSeek-V3. A 754B MoE model available under MIT, outscoring GPT-5.4 and Claude Opus 4.6 on the benchmark most directly relevant to agentic software engineering, is a meaningful data point regardless of the 0.7-point margin. The open-weight release is what makes this worth tracking — it's the first model at this capability tier that researchers can run, audit, and fine-tune without an API contract.

The hardware constraint is the practical limiting factor for most teams. Start with API access on Lambda Labs or Together AI to evaluate task-specific performance before making any infrastructure commitment. If GLM-5.1 consistently outperforms your current model on your actual workload — not on SWE-Bench Pro generically, but on the specific task distribution you care about — then the infrastructure investment has a concrete justification.

If you're running agentic coding workflows, test the 8-hour autonomous execution capability on a task comparable to your production workload before drawing conclusions from the demo. A 655-iteration success on a controlled task tells you what the model can do under ideal conditions. Your task under real conditions is the variable that determines whether the SWE-Bench lead translates to your pipeline.