GLM-5.1 Review 2026: #1 SWE-bench Pro, MIT License, $1/M Tokens

GLM-5.1 is the first open-weight model to claim the top spot on SWE-Bench Pro, scoring 58.4 as of April 2026 — ahead of GPT-5.4 at 57.7 and Claude Opus 4.6 at 57.3. Released on April 7, 2026 by Z.AI (formerly Zhipu AI), it combines a 754B-parameter Mixture-of-Experts architecture with an MIT license and API pricing starting at $1.40 per million input tokens. For teams running high-volume code generation pipelines, that combination is difficult to ignore.

GLM-5.1 Review 2026: The Open-Weight Model That Topped SWE-Bench Pro

GLM-5.1 landed on April 7, 2026 as Z.AI’s flagship open-weight release, and within weeks it had claimed the number-one position on SWE-Bench Pro with a score of 58.4 — a benchmark that measures an AI model’s ability to autonomously resolve real GitHub issues. That score edges out GPT-5.4 (57.7) and Claude Opus 4.6 (57.3), making GLM-5.1 the first openly licensed model to outperform every closed frontier model on the most rigorous software engineering benchmark available. Z.AI, formerly known as Zhipu AI, rebranded and listed on the Hong Kong Stock Exchange in January 2026, raising approximately $558 million. GLM-5.1 is the company’s first major model release post-IPO, and it signals an aggressive push into the global developer market. Model weights are available on HuggingFace under the MIT license, meaning anyone can download, fine-tune, and commercially deploy the model without restriction. The OpenAI-compatible API on the z.ai platform provides immediate access without any infrastructure overhead, and the model supports a 203K context window with up to 128K output tokens per response.

Architecture: 754B Parameters, 40B Active, and Why MoE Matters

GLM-5.1 uses a Mixture-of-Experts architecture with 754 billion total parameters, but only 40 billion are active during any single inference pass. That distinction matters enormously: with 58.4 on SWE-Bench Pro, the model is delivering frontier-level reasoning at a computational cost closer to a 40B dense model than a 700B-plus monolith. The MoE design routes each input token through a subset of specialized expert networks rather than activating the full parameter space. GLM-5.1 uses a top-8 routing strategy across 256 experts, selecting the eight most relevant expert networks per token. This architecture allows the model to accumulate knowledge across a massive parameter space while keeping per-token inference costs tractable. For self-hosting, it means an 8xH100 cluster can serve a model with the effective knowledge capacity of something far larger. The training infrastructure itself is notable: GLM-5.1 was trained entirely on Huawei Ascend 910B chips using the MindSpore framework, with no NVIDIA hardware involved. This is a meaningful demonstration that frontier-quality models can now be produced outside the CUDA ecosystem — significant context for teams thinking about long-term supply chain dependencies in their AI infrastructure. Post-training applied RLHF tuned specifically on SWE-Bench-style code repair tasks and extended reasoning mode, which directly explains the model’s outsized performance on software engineering evaluations.

SWE-Bench Pro #1: Beating GPT-5.4 and Claude Opus 4.6

GLM-5.1’s SWE-Bench Pro score of 58.4 is the headline number for this review, and it is worth unpacking what that benchmark actually measures. SWE-Bench Pro presents a model with real-world GitHub issues — bugs reported on production repositories — and evaluates whether the model can autonomously produce a correct code patch. Unlike simpler coding benchmarks that test isolated functions or toy problems, SWE-Bench Pro demands multi-file reasoning, understanding of existing codebases, and iterative debugging. GPT-5.4 scores 57.7 on the same benchmark, and Claude Opus 4.6 scores 57.3 — both strong results, but both below GLM-5.1’s 58.4. The margin is narrow (less than 1.5 points separates all three models), which reflects how competitive the frontier has become, but GLM-5.1 holds the top position as of April 2026. GLM-5.1 also performs competitively on LiveCodeBench and HumanEval+, reinforcing that its coding strength is not an artifact of benchmark overfitting. On agent-oriented evaluations it scores 71.8 on MCP-Atlas, which measures reliable tool-chaining behavior with real MCP servers — a practical signal for teams building autonomous coding agents rather than single-shot code generators.

MIT License: What Commercial Use Actually Means for Developers

The MIT license on GLM-5.1 is not a marketing footnote — it has concrete implications for how the model can be deployed and monetized. With a score of 58.4 on SWE-Bench Pro, GLM-5.1 is the first model at this performance tier to ship under a license that places zero restrictions on commercial use, redistribution, or fine-tuning. Compare this to DeepSeek, whose weights are public but whose license restricts certain commercial uses, or to Llama 4, which carries its own commercial use terms tied to user count thresholds. MIT means you can download the weights, fine-tune the model on your proprietary codebase, embed it in a commercial product, and ship it to customers — all without royalties, attribution requirements beyond the license header, or usage caps. For regulated industries — financial services, healthcare, government contracting — this also enables fully private deployments where no data ever leaves your infrastructure. There is no vendor API call, no data retention policy to negotiate, and no audit trail that touches a third-party server. Teams in these sectors have historically been blocked from adopting the best AI models precisely because frontier performance and data sovereignty were mutually exclusive. GLM-5.1 removes that constraint and opens a deployment path that simply did not exist before for models at this benchmark level.

Pricing: $1.40/M Input Tokens vs Claude and GPT-5 Alternatives

GLM-5.1’s API pricing is $1.40 per million input tokens and $4.40 per million output tokens, with cached input tokens available at $0.26 per million — a structure that undercuts every major closed frontier model by a wide margin. Claude Sonnet 4 runs $3 per million input tokens and $15 per million output tokens, making GLM-5.1 roughly 5 to 6 times cheaper on input and more than 3 times cheaper on output. For a team processing one billion input tokens per month through a code generation pipeline, switching from Claude Sonnet 4 to GLM-5.1 represents roughly $1,600 in monthly savings on input costs alone, before accounting for output token volume. At higher throughput, those savings compound quickly and can justify dedicated infrastructure investment. GPT-5.4 pricing is comparable to Claude Sonnet 4, so the cost advantage of GLM-5.1 holds across both primary closed alternatives. The cached input token rate of $0.26 per million is particularly valuable for agentic workflows that repeatedly pass the same system prompt or code context across many calls — a common pattern in coding assistant applications where the repository context is prepended on every turn.

Self-Hosting GLM-5.1: Hardware Requirements and Setup

Running the full GLM-5.1 model locally requires a minimum of 8xH100 80GB GPUs, providing 640GB of total VRAM to hold the active parameter set and the full expert weight matrix in memory. That is a substantial hardware investment, but the 40-billion active-parameter MoE design means inference throughput is meaningfully better than you would expect from a naive 754B dense model on the same cluster. For teams without access to an 8xH100 setup, two practical alternatives exist: a 4-bit quantized version of GLM-5.1 in GGUF format runs on a 4xA100 40GB configuration (160GB total VRAM) with modest quality trade-offs on complex multi-step reasoning, and the Z.AI managed API provides immediate access without any infrastructure commitment, making it the right starting point for evaluation before committing to a self-hosting decision. The model is compatible with vLLM and SGLang inference engines, both of which expose an OpenAI-compatible server interface. That means any existing integration built against the OpenAI SDK can point at a self-hosted GLM-5.1 endpoint with a single base URL change and no code modifications. Weights and tokenizer files are available at zai-org/GLM-5.1 on HuggingFace under the MIT license with no download gate or registration requirement.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_ZAI_API_KEY",
    base_url="https://open.z.ai/api/v1"
)

response = client.chat.completions.create(
    model="glm-5.1",
    messages=[{"role": "user", "content": "Find and fix the bug in this Python function: ..."}],
    extra_body={"thinking": True}  # enable extended reasoning mode
)
print(response.choices[0].message.content)

GLM-5.1 vs Kimi K2.5 vs Qwen 3.5: Open-Weight Coding Model Comparison

GLM-5.1’s SWE-Bench Pro score of 58.4 leads the open-weight field as of April 2026, placing it ahead of Kimi K2.5 and Qwen 3.5 — its two closest open-weight competitors in the autonomous software engineering category. Kimi K2.5, released by Moonshot AI, delivers strong performance on mathematical reasoning and long-context retrieval tasks, but its SWE-Bench Pro score trails GLM-5.1 by several points. Kimi K2.5 also carries a more restrictive license than MIT, which limits its usefulness for commercial self-hosting and derivative products. Qwen 3.5, from Alibaba’s Qwen team, is a capable open-weight model with broad multilingual coverage and solid HumanEval+ numbers, and its Apache 2.0 license is similarly permissive to MIT. However, Qwen 3.5 has not matched GLM-5.1’s SWE-Bench Pro result, and its agentic tool-use benchmarks lag behind GLM-5.1’s MCP-Atlas score of 71.8. For teams specifically optimizing for autonomous software engineering tasks — the kind measured by SWE-Bench Pro — GLM-5.1 currently holds a measurable lead over the entire open-weight field. For teams that need strong multilingual coverage or integration with Alibaba Cloud tooling, Qwen 3.5 remains a solid alternative worth evaluating in parallel.

Who Should Use GLM-5.1?

GLM-5.1 is not the right fit for every team, but for specific use cases it is the strongest available option as of May 2026. With a SWE-Bench Pro score of 58.4, an MIT license, and $1.40 per million input tokens, it is built for cost-sensitive, high-volume code generation workloads where data sovereignty matters. Cost-sensitive B2B SaaS teams running Claude-based code generation pipelines are the clearest fit: the performance gap between GLM-5.1 and Claude Sonnet 4 on everyday tasks — code completion, unit test generation, small bug fixes — is negligible in practice, while the cost difference runs 5 to 6 times. Enterprises in regulated industries that must keep all data on-premises will find that the MIT license and self-hosting path finally make frontier-quality coding AI accessible within their compliance constraints. AI agent developers building long-horizon automation workflows benefit from GLM-5.1’s strong MCP-Atlas score (71.8) and its demonstrated capability for multi-step tool-calling loops over extended sessions. Teams that regularly need to analyze codebases larger than 200K tokens in a single context window will find Claude’s 1M context window essential — GLM-5.1’s 203K limit is a real constraint for very large monorepos analyzed in one pass. For individual developers and early-stage startups, the Z.AI API’s low entry cost and OpenAI-compatible interface mean there is no migration friction to start experimenting immediately.

FAQ

These are the questions developers most commonly raise after reviewing GLM-5.1’s benchmark results, license terms, and deployment options. GLM-5.1 scored 58.4 on SWE-Bench Pro at launch on April 7, 2026 — the highest score of any model, open or closed, at that point in time. The answers below address practical concerns about score verification, hardware requirements for self-hosting, cost comparison with Claude Sonnet 4, tool compatibility with popular AI coding assistants, and how this release differs from its predecessor GLM-5. Whether you are evaluating GLM-5.1 for a cost-sensitive production pipeline, a fully private on-premises deployment, or an agentic coding workflow that demands extended autonomous sessions, the details here should help you make a well-informed decision. The model’s MIT license, $1.40 per million input token pricing, and HuggingFace weight availability are all confirmed as of May 2026.

Is GLM-5.1’s SWE-Bench Pro score of 58.4 independently verified?

The 58.4 score was reported by Z.AI at the April 7, 2026 launch and reflects their evaluation on the SWE-Bench Pro dataset. As of May 2026, independent third-party replication of the exact number is still limited, which is standard for newly released models. The benchmark protocol for SWE-Bench Pro is publicly defined, so external verification is ongoing in the research community. It is reasonable to treat the number as directionally accurate while waiting for broader independent confirmation from academic and industry evaluators.

How much GPU memory do I need to self-host GLM-5.1?

The full FP16 model requires a minimum of 8xH100 80GB GPUs (640GB total VRAM). A 4-bit quantized GGUF version runs on 4xA100 40GB GPUs (160GB total VRAM) with modest quality trade-offs on complex reasoning tasks. Both configurations are supported by vLLM and SGLang, which expose an OpenAI-compatible server endpoint for easy drop-in integration with existing tooling built against the OpenAI SDK.

How does GLM-5.1 compare to Claude Sonnet 4 on everyday coding tasks?

On routine tasks — code completion, unit test generation, small bug fixes, and documentation — GLM-5.1 and Claude Sonnet 4 are close enough that most developers will not notice a meaningful quality difference. GLM-5.1 leads on SWE-Bench Pro (58.4 vs Claude Opus 4.6’s 57.3), while Claude’s 1M context window gives it a clear advantage when analyzing very large codebases in a single pass. At $1.40/M vs $3.00/M for input tokens, GLM-5.1 is the stronger choice for cost-sensitive pipelines where context windows under 200K are sufficient for the workload.

Can I use GLM-5.1 with Cursor, Cline, or other AI coding tools?

Yes. GLM-5.1 exposes an OpenAI-compatible API endpoint at https://open.z.ai/api/v1. Any AI coding assistant that supports a custom API endpoint — including Kilo Code, Cline, and Continue.dev — can use GLM-5.1 as its backend by changing the base URL and API key in the tool settings. For Cursor, add it as a custom model under the Models settings panel. The OpenAI SDK compatibility means no code changes are required beyond swapping the base URL and credentials in your configuration.

What is the difference between GLM-5.1 and the earlier GLM-5?

GLM-5.1 is a specialized successor to GLM-5 with post-training heavily focused on software engineering and agentic tool use. The base MoE architecture is similar in design, but GLM-5.1 received substantially more RLHF training on SWE-Bench-style code repair tasks and extended reasoning via thinking mode. The result is approximately an 8-point improvement on SWE-Bench Pro compared to GLM-5, along with expanded MCP tool integration and improved reliability on multi-step autonomous coding sessions that span hundreds of iterations.