GLM‑5.1 Outperforms Claude Opus in Benchmarks – The Open‑Source LLM’s Edge

Model Overview

GLM‑5.1 is Zhipu AI’s flagship open‑source model with 744 B total parameters (40 B activation) built on a Mixture‑of‑Experts architecture. It provides a 200 K token context window, is released under an MIT license, and is available in BF16 full‑precision and FP8 quantized formats on HuggingFace and ModelScope.

Core Capability: Long‑Running Autonomous Tasks

Earlier GLM generations quickly plateaued when given more time. GLM‑5.1 introduces a runtime‑dependent improvement mechanism, demonstrated through three concrete scenarios.

Scenario 1 – VectorDBBench Optimization

VectorDBBench asks a model to build a high‑performance approximate nearest‑neighbor search database in Rust. The best closed‑source baseline (Claude Opus 4.6) achieved 3,547 QPS within 50 tool‑call rounds. GLM‑5.1 removed the round limit, allowing the model to decide when to submit a new version. After more than 600 iterations and 6,000+ tool calls, QPS rose to 21,500 (≈6× the baseline). Performance increased in distinct jumps: around round 90 the model switched from full‑table scans to IVF clustering with f16 compression (≈6.4 k QPS); around round 240 a two‑stage pipeline (u8 pre‑filter + f16 re‑ranking) lifted QPS to ≈13.4 k. Six structural transitions occurred, each triggered after the model analyzed its own performance logs.

Scenario 2 – KernelBench Level 3

KernelBench Level 3 contains 50 tasks that require converting a PyTorch reference implementation into a faster GPU kernel. The default torch.compile yields a 1.15× speedup; max‑autotune reaches 1.49×. GLM‑5.1 achieved a 3.6× acceleration and continued to improve late in the experiment. Claude Opus 4.6 performed better on this benchmark (4.2×), but GLM‑5.1 shows a qualitative leap over GLM‑5, which saturated early.

Scenario 3 – 8‑Hour Linux Desktop Construction

The task provided a prompt to build a Linux‑style desktop environment using only web technologies, without templates or design drafts. Earlier models stopped after producing a static taskbar and placeholder windows. GLM‑5.1 added an outer loop: after each iteration it inspected its output, identified missing features, rough styling, or buggy interactions, and continued. Over eight hours the model produced a complete, visually consistent desktop with a file manager, terminal, text editor, system monitor, calculator, and a simple game, each component refined iteratively.

Official Benchmark Results

SWE‑Bench Pro: 58.4 points (open‑source first, surpassing Claude Opus 4.6 57.3, GPT‑5.4 57.7, Gemini 3.1 Pro 54.2)

CyberGym (network‑security): 68.7 vs Opus 4.6 66.6 (42 % improvement over GLM‑5)

BrowseComp (browser tasks): 68.0 vs GLM‑5 62.0

Math (AIME 2026): 95.3 ≈ GLM‑5 95.4, behind GPT‑5.4 98.7

NL2Repo (repo generation): 42.7 < Opus 4.6 49.8

Third‑Party Arena Rankings

Design Arena placed GLM‑5.1 at 4th (Elo 1352) behind Claude Opus 4.6 and Sonnet 4.6. In Text Arena, GLM‑5.1 is the top open‑source model, scoring 11 points higher than GLM‑5 and 15 points higher than Kimi K2.5.

Horizontal Comparison with Other Open‑Source Models

Programming (SWE‑Bench Pro): GLM‑5.1 58.4 > Claude Opus 4.6 57.3 > GPT‑5.4 57.7 > Gemini 3.1 Pro 54.2

Agent (τ³‑Bench): GLM‑5.1 70.6 ≈ Claude Opus 4.6 70.7 > Kimi K2.5 66.0

Tool‑call (MCP‑Atlas): GLM‑5.1 71.8 < Claude Opus 4.6 74.1

CyberGym (security): GLM‑5.1 68.7 > Claude Opus 4.6 66.6

Math (AIME 2026): GLM‑5.1 95.3 ≈ GLM‑5 95.4 < GPT‑5.4 98.7

Deployment barrier: > 200 GB RAM even with 2‑bit quantization, higher than Qwen‑3.6‑Plus and comparable to DeepSeek‑V3.2.

Long‑duration tasks: only GLM‑5.1 explicitly demonstrates sustained multi‑round improvement.

Model Architecture and Parameters

Parameter scale: 744 B total, 40 B activation (MoE)

Context window: 200 K tokens

License: MIT (commercial‑friendly)

Formats: BF16 full‑precision and FP8 quantized

Weights hosted on HuggingFace and ModelScope

Local Deployment Guide

The full‑precision model requires about 1.65 TB of disk space; practical deployment relies on quantized or FP8 versions.

Option 1 – vLLM (production)

docker run --gpus all \
  -p 8000:8000 \
  --ipc=host \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:glm51 zai-org/GLM-5.1-FP8 \
  --tensor-parallel-size 8 \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --enable-auto-tool-choice \
  --served-model-name glm-5.1-fp8

For CUDA 13+ use the image vllm/vllm-openai:glm51-cu130. Source installation:

uv venv
source .venv/bin/activate
uv pip install "vllm==0.19.0" --torch-backend=auto
uv pip install "transformers>=5.4.0"

FP8 models require the additional DeepGEMM package.

Option 2 – SGLang (high concurrency)

SGLANG_ENABLE_SPEC_V2=1 sglang serve \
  --model-path zai-org/GLM-5.1-FP8 \
  --tp 8 \
  --reasoning-parser glm45 \
  --tool-call-parser glm47 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.85

Tensor‑parallel configurations (TP) for common hardware:

H100: FP8 tp=16, BF16 tp=32

H200: FP8 tp=8, BF16 tp=16

B200: FP8 tp=8, BF16 tp=16

GB300: FP8 tp=4 (BF16 not supported)

MI300X/MI325X: BF16 tp=8 (FP8 not supported)

MI355X: BF16 tp=8 (FP8 not supported)

FP8 reduces GPU memory consumption by roughly half compared with BF16.

Option 3 – Ollama Cloud (simplest)

ollama run glm-5.1:cloud

Option 4 – Unsloth Quantized Builds (consumer‑grade hardware)

Unsloth provides GGUF quantizations. The Dynamic 2‑bit (UD‑IQ2_M) version is ~236 GB and fits a 256 GB unified‑memory Mac or a single 24 GB GPU with MoE off‑loading. Dynamic 1‑bit is ~200 GB; 8‑bit requires ~805 GB.

curl -fsSL https://unsloth.ai/install.sh | sh

unsloth studio -H 0.0.0.0 -p 8888

After downloading the desired GGUF file (e.g., unsloth/GLM-5.1-GGUF:UD-IQ2_M), run with llama.cpp:

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 --target llama-cli llama-server
./llama.cpp/llama-cli -hf unsloth/GLM-5.1-GGUF:UD-IQ2_M \
  --ctx-size 16384 --temp 0.7 --top-p 1.0

API Usage

cURL example:

curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-api-key" \
  -d '{
    "model": "glm-5.1",
    "messages": [{"role": "user", "content": "帮我写一段Python快速排序"}],
    "thinking": {"type": "enabled"},
    "max_tokens": 4096,
    "temperature": 1.0
}'

Python SDK (Z‑AI):

from zai import ZaiClient
client = ZaiClient(api_key="your-api-key")
response = client.chat.completions.create(
    model="glm-5.1",
    messages=[{"role": "user", "content": "帮我写一段 Python 快速排序"}],
    thinking={"type": "enabled"},
    max_tokens=4096,
    temperature=1.0,
)
print(response.choices[0].message.content)

OpenAI‑compatible SDK (compatible with existing OpenAI code):

from openai import OpenAI
client = OpenAI(api_key="your-Z.AI-api-key", base_url="https://api.z.ai/api/paas/v4/")
completion = client.chat.completions.create(
    model="glm-5.1",
    messages=[{"role": "user", "content": "帮我写一段 Python 快速排序"}],
)
print(completion.choices[0].message.content)