This article was originally published on aifoss.dev
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 API | Llama 4 Scout | |
|---|---|---|---|
| Best for | Data sovereignty, batch workloads | Easiest quality access | Consumer hardware, multimodal |
| Min VRAM | 24 GB + 256 GB RAM | API only | ~8 GB |
| SWE-Bench Pro | 58.4% | 58.4% | Not primary benchmark |
| License | MIT | Proprietary API | Llama 4 Community |
| Context window | 200K tokens | 200K tokens | 10M tokens |
| Cost | Hardware only | Free / $0.45/1M input | Hardware 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:
| Benchmark | GLM-5.1 | GPT-5.4 | Claude Opus 4.6 | Gemini 3.1 Pro |
|---|---|---|---|---|
| SWE-Bench Pro | 58.4% | 57.7% | 57.3% | 54.2% |
| Terminal-Bench 2.0 | 63.5% | — | 68.5% | — |
| AIME (math) | 95.3% | 98.7% | 98.2% | — |
| GPQA | 86.2% | — | — | — |
| NL2Repo | 42.7% | — | — | — |
| CyberGym | 68.7% | — | — | — |
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:
| Quantization | VRAM Required | System RAM | Estimated Size | Hardware |
|---|---|---|---|---|
| BF16 full precision | ~1.65 TB | 2 TB+ | 1.65 TB | 8× H200/B200 |
| FP8 | ~860 GB | 1 TB | ~880 GB | 8× H100/H200 |
| AWQ INT4 | ~377 GB | 512 GB | ~380 GB | 4–5× A100 80GB |
| Unsloth UD-IQ2_M (2-bit) | 24 GB GPU | 256 GB RAM | ~236 GB | 1× RTX 4090 + server RAM |
| Unsloth UD-IQ2_M (2-bit) | 0 GPU VRAM | 256 GB unified | ~236 GB | 256 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.
Top comments (0)