GLM-5.1 Review 2026: MIT 744B MoE That Tops SWE-Bench Pro

By AIFoss · 13 min read
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TL;DR: GLM-5.1 is a 744B MIT-licensed MoE model from Z.ai that scored 58.4% on SWE-Bench Pro in April 2026 — the first open-source model to top the leaderboard ahead of GPT-5.4 and Claude Opus 4.6. Self-hosting requires 24 GB GPU + 256 GB system RAM at minimum (2-bit Unsloth GGUF). For most developers, the Z.ai API free tier at 1,000 requests/day is the smarter starting point.

GLM-5.1 (self-hosted)Z.ai APILlama 4 Scout
Best forData sovereignty, batch workloadsEasiest quality accessConsumer hardware, multimodal
Min VRAM24 GB + 256 GB RAMAPI only~8 GB
SWE-Bench Pro58.4%58.4%Not primary benchmark
LicenseMITProprietary APILlama 4 Community
Context window200K tokens200K tokens10M tokens
CostHardware onlyFree / $0.45/1M inputHardware only

Honest take: Run the Z.ai API free tier first. Self-host only if you hit the request limit or have a compliance reason. The hardware bar for local GLM-5.1 is genuinely high, and the API quality is identical to self-hosted full precision.

What GLM-5.1 Is

Z.ai (formerly Zhipu AI) released GLM-5.1 on April 7, 2026. It’s a post-training upgrade to the GLM-5 base model — the architecture is unchanged (744B total parameters, 40B active per forward pass, Mixture-of-Experts), but tool use, instruction following, and autonomous execution are substantially improved over the base version.

The “agentic” framing is intentional. GLM-5.1 isn’t a general-purpose chat model that also writes code. It’s built for long-horizon software development: reading a codebase, forming a plan, editing across multiple files, running tests, and iterating — Z.ai reports up to 8 hours of sustained autonomous execution in internal benchmarks. That claim’s validity depends on task complexity and setup, but it reflects what the post-training optimizes for.

Key specs as of release:

  • Parameters: 744B total / 40B active (MoE)
  • Context window: 200K input tokens, 128K max output
  • License: MIT — no revenue threshold, no non-commercial clause
  • Released: April 7, 2026 by Z.ai
  • HuggingFace: zai-org/GLM-5.1

The MIT license matters here. Comparable frontier-adjacent models often carry custom licenses with $X million monthly revenue caps or non-commercial restrictions. GLM-5.1’s MIT lets you deploy commercially, fine-tune, and redistribute derivatives without a legal review.

Benchmark Reality Check

SWE-Bench Pro tests models on real GitHub issues from production open-source repositories. A “solve” means the model read the issue, edited the codebase, and passed the existing test suite without being given the fix. Unlike SWE-Bench Verified, Pro uses a harder, hand-curated subset designed to resist contamination from model training data.

GLM-5.1’s scores at release:

BenchmarkGLM-5.1GPT-5.4Claude Opus 4.6Gemini 3.1 Pro
SWE-Bench Pro58.4%57.7%57.3%54.2%
Terminal-Bench 2.063.5%—68.5%—
AIME (math)95.3%98.7%98.2%—
GPQA86.2%———
NL2Repo42.7%———
CyberGym68.7%———

The SWE-Bench Pro margin over closed-source leaders is narrow — 0.7 points over GPT-5.4 — but this is the first time an open-weight model has topped that leaderboard. Earlier open-weight models typically scored 10–20 points below frontier closed-source models on SWE-bench tasks. The narrowing gap reflects both better base pretraining and more targeted post-training on software agent workflows.

Terminal-Bench 2.0 tells a more honest story: 63.5% vs Claude Opus 4.6 at 68.5% on tool-use and shell execution tasks that closely resemble real dev workflows. There’s still a 5-point gap to the best proprietary option in interactive contexts. Math reasoning (AIME 95.3% vs 98.2% for Claude) follows the same pattern — extremely close, not a clean sweep.

For context: SWE-Bench Pro and SWE-Bench Verified are different evaluations. Models like Devstral Small 2 that score 68% on SWE-Bench Verified aren’t directly comparable to GLM-5.1’s 58.4% on Pro — the Pro subset is harder. Keep that in mind when reading benchmark comparisons across articles.

Hardware Reality Check

Most GLM-5.1 coverage skips this section or buries it. Here’s the full breakdown:

QuantizationVRAM RequiredSystem RAMEstimated SizeHardware
BF16 full precision~1.65 TB2 TB+1.65 TB8× H200/B200
FP8~860 GB1 TB~880 GB8× H100/H200
AWQ INT4~377 GB512 GB~380 GB4–5× A100 80GB
Unsloth UD-IQ2_M (2-bit)24 GB GPU256 GB RAM~236 GB1× RTX 4090 + server RAM
Unsloth UD-IQ2_M (2-bit)0 GPU VRAM256 GB unified~236 GB256 GB Mac (unified memory)

The Unsloth dynamic 2-bit row is where consumer hardware enters. The compression from 1.65 TB to ~236 GB comes from Unsloth’s dynamic quantization — more aggressive on less critical layers, less aggressive on attention-heavy layers. With llama.cpp’s MoE offloading, you keep the dense attention layers on a 24 GB GPU (RTX 4090 or equivalent) while the MoE expert layers page from 256 GB system RAM. Throughput is 2–5 tok/s — functional for batch jobs, too slow for interactive chat.

The 256 GB unified memory Mac path eliminates the CPU/GPU bandwidth bottleneck because GPU and RAM share the same physical pool. MoE offloading is essentially free on that architecture. The limitation is cost: these machines start above $5,000.

If neither setup describes your hardware, the Z.ai API is the right answer.

Running GLM-5.1 Locally: Unsloth GGUF + llama.cpp

Requirements: Linux or macOS, 24 GB VRAM GPU or 256 GB unified memory Mac, 256 GB system RAM. Windows is not practical here — RAM paging latency makes MoE offloading unusably slow.

Step 1: Build llama.cpp

apt-get update && apt-get install -y build-essential cmake curl libcurl4-openssl-dev
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build \
  -DBUILD_SHARED_LIBS=OFF \
  -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j \
  --target llama-cli llama-server
cp llama.cpp/build/bin/llama-* llama.cpp/

For Mac, replace -DGGML_CUDA=ON with -DGGML_METAL=ON.

Step 2: Download the 2-bit GGUF from Unsloth

pip install -U huggingface_hub hf_transfer
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download \
  unsloth/GLM-5.1-GGUF \
  --include "*UD-IQ2_M*" \
  --local-dir ./GLM-5.1-GGUF

This downloads approximately 236 GB across 6 shards. Budget 2–3 hours on a fast connection.

Step 3: Start the inference server

./llama.cpp/llama-server \
  --model ./GLM-5.1-GGUF/UD-IQ2_M/GLM-5.1-UD-IQ2_M-00001-of-00006.gguf \
  --alias "glm-5.1" \
  --n-gpu-layers 32 \
  --ctx-size 16384 \
  --port 8001

--n-gpu-layers 32 keeps attention layers on your GPU. Adjust this number based on available VRAM — more layers on GPU means faster inference, fewer means more RAM usage. --ctx-size 16384 limits context to 16K tokens; raising it increases RAM pressure proportionally.

Step 4: Query via OpenAI-compatible API

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8001/v1", api_key="none")
response = client.chat.completions.create(
    model="glm-5.1",
    messages=[{"role": "user", "content": "Refactor this function to handle null inputs safely."}],
)
print(response.choices[0].message.content)

The server exposes an OpenAI-compatible endpoint, so any tool that supports custom base URLs (Aider, Continue.dev, Open WebUI) can point at it directly.

If you want deeper control over quantization options — Q4_K_M, Q5_K_M, or other standard GGUF formats — check the GGUF quantization guide for a full breakdown of when each format makes sense.

The Ollama Cloud Tag

For testing without the 236 GB download:

ollama run glm-5.1:cloud

This routes to the Z.ai API through Ollama’s interface using Z.ai’s free tier (1,000 requests/day). The model version served is full-precision GLM-5.1 — no quantization degradation. If you’re already using Ollama’s OpenAI-compatible endpoint (http://localhost:11434/v1), the cloud tag integrates with your existing tooling in minutes.

A practical use: run glm-5.1:cloud through Ollama for interactive coding sessions where response latency matters, and use the local GGUF server for overnight batch jobs where throughput is less critical.

GLM-5.1 vs Llama 4 Scout

Both are 2026 open-weight MoE models. The gap in hardware requirements is the defining difference.

Where Llama 4 Scout wins:

  • Runs on an RTX 3080 or equivalent (~10–12 GB VRAM, quantized) — accessible to any developer with a recent gaming GPU
  • Multimodal: handles image and video inputs natively. GLM-5.1 is text-only.
  • 10M token context window vs GLM-5.1’s 200K — relevant for very long document pipelines. See the local LLM context window guide for when this actually matters in practice.
  • Broader ecosystem support: local quants work in Ollama, LM Studio, text-generation-webui out of the box

Where GLM-5.1 wins:

  • Higher SWE-Bench Pro performance on software agent tasks — the benchmark where both models compete for agentic coding use cases
  • 40B active parameters per forward pass vs Scout’s ~17B means more capacity per inference on complex reasoning
  • MIT license without community license restrictions
  • Better autonomous execution on multi-step coding tasks per Z.ai’s benchmarks

Verdict: If your GPU is under 24 GB or you need multimodal, Scout is the realistic local option. If you’re building a serious local coding agent with appropriate hardware, GLM-5.1 has a real quality advantage. For context window needs beyond 200K, Scout’s 10M is in a different category.

GLM-5.1 vs ZAYA1-8B

These are different-category models. ZAYA1-8B has 8.4B total parameters (~760M active), runs on any modern GPU with 8–12 GB VRAM, and achieves strong math benchmark scores for its size. It’s not competitive with GLM-5.1 on SWE-bench-style multi-file code repair — 760M active parameters simply can’t hold the full context and planning state that 40B active parameters can.

Where ZAYA1-8B makes sense: laptop inference, edge deployment, quick reasoning tasks, and any scenario where a 236 GB download is a non-starter. Its Apache 2.0 license is equally permissive.

Think of them as serving different slots. GLM-5.1 is the model for a dedicated inference server doing serious autonomous engineering. ZAYA1-8B is the model that runs in the background on whatever machine you already have. There’s no scenario where they’re substitutes for each other at the same task. For the broader landscape of open-source coding agents, the coding agents state-of-the-art overview covers where each category fits.

Z.ai API vs Self-Hosting: The Decision

Self-host when:

  • You have data sovereignty requirements — code or documents cannot leave your infrastructure
  • You’re running batch automation at scale where hardware amortizes cost faster than per-token API pricing
  • You’re above 1,000 requests/day on the free tier and the $0.45/1M input adds up at your volume

Use the API when:

  • You’re evaluating fit before committing to hardware
  • Response latency matters — full-precision API responses are faster than 2-bit local inference
  • Your workload is interactive or irregular rather than batch
  • You don’t have a 256 GB RAM machine available

The $0.45/1M input token pricing is significantly cheaper than most comparable proprietary frontier APIs ($3–$15/1M input). For production workloads that don’t require local inference, the paid API tier is competitive on pure cost.

For cloud GPU options that sit between the managed API and full self-hosting — where you control the model version, quantization, and data routing but don’t own the hardware — RunPod lets you spin up H100/A100 instances and run GLM-5.1 at FP8 or AWQ INT4 precision on demand.

When NOT to Use GLM-5.1

  • GPU under 24 GB VRAM: The 2-bit quantization at 744B introduces quality degradation that smaller purpose-built coding models avoid. Devstral Small 2 (24B, Apache 2.0, 14 GB VRAM) is more practical for local coding agents on single-GPU workstations.
  • Multimodal inputs: GLM-5.1 is text-only. Use Llama 4 Scout or a vision-capable model for image/video tasks.
  • Sub-second interactive latency: 2–5 tok/s on 2-bit local inference is not interactive. The Z.ai API is faster for chat.
  • Multi-user production service on a budget: Serving a 744B model requires enterprise GPU infrastructure. A single A100 80GB can’t run even the INT4 quantized version. For self-hosted multi-user AI stacks, see the self-hosted AI stack for dev teams guide for more practical options.
  • Wide LoRA fine-tune ecosystem: Most fine-tunes target smaller popular models. Training LoRA on a 744B MoE requires cluster-scale infrastructure.

FAQ

Can I run GLM-5.1 on a single RTX 4090?

Yes, with Unsloth UD-IQ2_M and 256 GB system RAM. Inference runs at 2–5 tok/s due to MoE offloading. If you want faster throughput on a single 24 GB card, a smaller model like Devstral Small 2 is more practical for interactive use.

Is the MIT license really unrestricted?

As of the April 2026 release, yes — no revenue caps, no non-commercial clauses. Always verify the current license file at zai-org/GLM-5.1 on HuggingFace before a production deployment. Licenses on large open-weight models have changed between versions before.

Does Ollama run GLM-5.1 locally?

The standard glm-5.1:cloud Ollama tag routes to Z.ai’s API — it does not run the model locally. Community GGUF users run it directly via llama.cpp as described above. Check the Ollama library page for local quant availability as the ecosystem develops.

How does GLM-5.1’s 200K context compare to competitors?

GPT-5.4 defaults to 128K; Claude Opus 4.6 supports 200K. GLM-5.1’s 200K input (128K max output) is in the same tier as the leading closed-source models. For software engineering tasks — codebases, PRs, code reviews — 200K is sufficient for almost all real workflows. Llama 4 Scout’s 10M context is in a different category for ultra-long document pipelines, not a common coding agent requirement.

Is GLM-5.1 good for general chat or creative writing?

It can handle both, but the post-training targets agentic software engineering. For creative writing, models specifically fine-tuned for narrative generation fit better. For general chat, a lighter quantized model (Llama 4 Scout, Qwen series) is more practical given GLM-5.1’s infrastructure requirements.

Sources

  • RTX 4090 — minimum 24 GB VRAM GPU for local GLM-5.1 inference with MoE offloading
  • RTX 3080 — practical GPU for smaller models like Llama 4 Scout (10–12 GB VRAM)

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