GLM-5.1 for coding: benchmarks and integration

# GLM-5.1: A Coding Alternative Powered by Huawei Ascend

Zhipu AI has released GLM-5.1 — an update to its flagship model, optimized for code generation and editing. According to the developers' internal benchmarks, the model scores 45.3 on Claude Code, reaching 94.6% of Claude Opus 4.6's result (47.9 points). Compared to GLM-5 from February, that's a 28% improvement — from 35.4 to 45.3 points. On SWE-bench Verified, the base GLM-5 achieved 77.8%, the best result among open models. Benchmarks are still internal, without independent verification.

The model was fully trained on 100,000 Huawei Ascend 910B chips, without using Nvidia GPUs. This ensures independence from sanctions and an alternative to Western infrastructures.

Integration with Claude Code

To test GLM-5.1 in Claude Code, edit ~/.claude/settings.json: set ANTHROPIC_BASE_URL to the Z.ai endpoint and your API key. The model maps to Opus. Max-plan users add "glm-5.1" to the mapping.

An alternative is the glm CLI wrapper from GitHub. It sets temporary environment variables while preserving the main Anthropic config. This lets you run GLM alongside Claude without conflicts.

# Example setup via CLI
glm --model glm-5.1 "your prompt here"

Pricing

GLM Coding Plan: from $3/month (120 prompts, promo) to $30/month (Pro). API rates: $1 per 1M input tokens, $3.20 per 1M output tokens. Compared to Claude Max ($100–200/month), it offers advantages for high-load tasks: prototyping, agentic workflows, repeated experiments.

During peak hours, Claude experiences increased latency and queues — GLM-5.1 provides a stable backup option.

• Price advantages: 3–10 times cheaper than alternatives.
• Context: 200K tokens.
• Access: open API without geoblocks.
• Deployment: weights under MIT license, support for vLLM and SGLang.

Technical Specifications

Architecture: MoE with 744B parameters, 40B active per token. DeepSeek Sparse Attention optimizes long-context processing. Ideal for agentic engineering tasks: from vibe-coding to complex multi-step workflows.

Technical report: “GLM-5: from Vibe Coding to Agentic Engineering”. For self-hosting, weights are available on Hugging Face.

# Deployment via vLLM
vllm serve --model glm-5.1 --tensor-parallel-size 8

The model is geared toward mid/senior developers: precise terminology, focus on efficiency and scalability.

Key Points

• GLM-5.1 reaches 94.6% of Claude Opus 4.6 on internal coding benchmarks.
• Full independence from Nvidia: trained on Huawei Ascend 910B.
• Cost savings: API from $1/1M tokens vs. $100+/month from competitors.
• Integration into Claude Code via simple env or CLI setup.
• Open weights under MIT for custom deployment.

— Editorial Team