GLM-5.1 (744B MoE, #1 SWE-Bench Pro)

Deploy GLM-5.1 (744B MoE, 40B active) by Z.ai on Clore.ai — the open-weight model that topped SWE-Bench Pro in April 2026

Status (April 2026): GLM-5.1 was released on April 7, 2026 by Z.ai (formerly Zhipu AI) as an incremental-but-serious upgrade to GLM-5. It is the first open-weight model to top SWE-Bench Pro (58.4%), edging out GPT-5.4 (57.7) and Claude Opus 4.6 (57.3) according to vendor-published numbers. Weights live at huggingface.co/zai-org/GLM-5.1 under the MIT license.

GLM-5.1 is a 744-billion parameter Mixture-of-Experts language model that activates only ~40B parameters per token. Compared to its predecessor GLM-5, the 5.1 release keeps the same MoE skeleton but ships refined expert routing, a 200K-token context window, a 131K-token max output, and training focused on long-horizon agentic coding — the model is explicitly tuned to sustain thousands of tool calls and hundreds of refactor rounds without drifting.

For Clore.ai users, the interesting part is the 40B active number: you don't need a full 8×H200 rack to serve it. A tensor-parallel setup across 2×H100 80GB (FP8) or 4×A100 80GB (BF16 with sharding) is enough for practical throughput — putting frontier-class coding within reach at ~$12–24/day on the marketplace.

Key Specs

Property	Value
Total Parameters	744B (MoE)
Active Parameters	~40B per forward pass
Context Window	200,000 tokens
Max Output	131,072 tokens
License	MIT
Release Date	April 7, 2026
Organization	Z.ai (zai-org on HuggingFace)
Primary Tooling	vLLM, SGLang, llama.cpp (GGUF), xLLM, KTransformers

Why GLM-5.1?

• #1 on SWE-Bench Pro — 58.4% vendor-claimed, ahead of GPT-5.4 and Claude Opus 4.6

• Long-horizon agents — sustains optimization across hundreds of rounds and thousands of tool calls

• 200K context — enough for an entire mid-sized codebase plus test suite

• 40B active MoE — you pay the inference cost of a 40B dense model, not a 744B one

• MIT license — fully open weights, no restrictions on commercial use or fine-tuning

• Open training stack — Z.ai published the model, reportedly trained without Nvidia data-center GPUs

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Requirements

Still a big model. While "40B active" sounds friendly, the full 744B weights must be loaded into VRAM (or offloaded). FP8 weights are ~860GB; BF16 is ~1.5TB. Plan accordingly.

Component	Minimum (Q4 GGUF, offload)	Recommended (FP8)	Full BF16
GPU VRAM	~80GB (Q4 + RAM offload)	2× H100 80GB active, 8× total	8× H200 141GB
RAM	256GB	256GB	512GB
Disk	500GB NVMe	1TB NVMe	2TB NVMe
CUDA	12.4+	12.4+	12.6+

Clore.ai pick: For most teams, 2× H100 80GB running the FP8 checkpoint with aggressive offloading is the sweet spot (~$12–16/day). If you need full BF16 throughput, jump to 8× H200 or use the Z.ai API for occasional calls.

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Option A — Ollama / GGUF (Quantized, community builds)

Heads-up: Community GGUF quants typically land 1–2 weeks after a Z.ai release. If ollama pull fails, check huggingface.co/models?search=glm-5.1+gguf and point llama.cpp at the file directly.

# Once a Q4_K_M build is available
docker exec ollama ollama pull glm-5.1:q4_K_M
docker exec ollama ollama run glm-5.1:q4_K_M

# Or with llama.cpp directly on a GGUF file
docker run --gpus all -it --rm -p 8080:8080 \
  -v $(pwd)/models:/models \
  ghcr.io/ggerganov/llama.cpp:server-cuda \
  -m /models/glm-5.1-q4_k_m.gguf \
  --n-gpu-layers 80 --ctx-size 32768 \
  --port 8080 --host 0.0.0.0

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Option B — vLLM (Production API, recommended)

vLLM is Z.ai's first-class serving target. The FP8 checkpoint (zai-org/GLM-5.1-FP8) is the one you want — same quality as BF16, roughly half the memory.

version: "3.8"
services:
  vllm:
    image: vllm/vllm-openai:latest
    ports:
      - "8000:8000"
    volumes:
      - hf_cache:/root/.cache/huggingface
    command: >
      --model zai-org/GLM-5.1-FP8
      --tensor-parallel-size 8
      --max-model-len 65536
      --gpu-memory-utilization 0.88
      --tool-call-parser glm47
      --reasoning-parser glm45
      --enable-auto-tool-choice
      --served-model-name glm-5.1
      --trust-remote-code
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    shm_size: "16gb"

volumes:
  hf_cache:

# Test the API
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.1",
    "messages": [
      {"role": "system", "content": "You are a senior software engineer."},
      {"role": "user", "content": "Refactor this Go handler to use context.Context properly and add retries."}
    ],
    "max_tokens": 4096,
    "temperature": 1.0
  }'

Use --tensor-parallel-size 2 on 2× H100 if you're running tight on GPU count, but plan for slower prefill on 200K contexts. --enable-chunked-prefill helps a lot.

---

Option C — SGLang (alternative, often faster on Hopper)

docker pull lmsysorg/sglang:latest

python3 -m sglang.launch_server \
  --model-path zai-org/GLM-5.1-FP8 \
  --tp-size 8 \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.88 \
  --context-length 65536 \
  --served-model-name glm-5.1

SGLang's EAGLE speculative decoding typically gives a 1.5–2× throughput boost on long coding completions.

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Clore.ai GPU Recommendations

Setup	VRAM	Expected Performance	Clore.ai Cost
2× H100 80GB	160GB	FP8 with offload, ~15–25 tok/s	~$12–16/day
4× A100 80GB	320GB	BF16 sharded, ~20–30 tok/s	~$15–22/day
8× H100 80GB	640GB	FP8 full, ~60+ tok/s	~$40–55/day
8× H200 141GB	1,128GB	BF16 full, maximum throughput	~$70+/day

Best value: 2× H100 80GB with the FP8 checkpoint. You get frontier-class coding performance for roughly the price of a Claude Opus subscription — and the weights stay on your box.

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Use Cases

• Autonomous SWE agents — GLM-5.1 is explicitly trained for long tool-calling loops; pair it with something like SWE-agent or OpenHands

• Codebase understanding — drop 100K+ tokens of Go/Rust/Python into context and ask for architectural reviews

• Long-context RAG — 200K ctx handles entire product docs + support tickets in one shot

• Refactor pipelines — sustained correctness across hundreds of file edits

• Agent-of-agents orchestration — use GLM-5.1 as a planner and smaller models (Qwen3.5-35B, GLM-4.7) as workers

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Benchmarks

Vendor-claimed — verify independently. The numbers below come from Z.ai's April 7, 2026 announcement. Independent reproductions on SWE-Bench Pro are still rolling in.

Benchmark	GLM-5.1	GPT-5.4	Claude Opus 4.6	GLM-5
SWE-Bench Pro	58.4%	57.7%	57.3%	~52%
SWE-Bench Verified	~79%	~78%	~80%	77.8%
HumanEval	~94%	~95%	~94%	~93%
LiveCodeBench	~72%	~73%	~70%	~68%

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Troubleshooting

Issue	Solution
OutOfMemoryError on load	FP8 checkpoint needs ~860GB total VRAM. Use 8× H100/H200 or drop to GGUF Q4 with RAM offload.
Slow HuggingFace download	Use huggingface-cli download zai-org/GLM-5.1-FP8 --local-dir ./weights --resume-download. Expect 800GB+.
Tool calls silently dropped	Ensure --tool-call-parser glm47 and --enable-auto-tool-choice are both set in vLLM.
Thinking mode empty	Requires temperature=1.0 — zero-temp sampling breaks the reasoning trace.
vLLM rejects the config	GLM-5.1 needs vLLM ≥ 0.7.x (April 2026 release). Use pip install -U vllm --pre if on older versions.
200K context OOMs	Start with --max-model-len 65536 and add --enable-chunked-prefill; raise once stable.

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Next Steps

• Predecessor: GLM-5 — same MoE shape, slightly less coding-focused

• Cheaper alternative: Qwen3.5 — 35B dense fits on a single RTX 4090

• Massive-context alternative: DeepSeek V4 — 1M ctx, multimodal, ~1T params

• Clore.ai Marketplace: clore.ai/marketplace — rent H100/H200/A100 from $0.50/day

Links

• GLM-5.1 on HuggingFace

• Z.ai Blog — GLM-5.1 announcement

• Z.ai Platform (hosted API)

• vLLM docs

• SGLang repo