GLM-5.1 vs Claude, GPT, Gemini, DeepSeek: How Zhipu AI's Latest Model Stacks Up

Zhipu AI just released GLM-5.1 on March 27, 2026, and the numbers are turning heads. The Chinese AI lab — which IPO’d on Hong Kong’s stock exchange in January at a $31.3 billion valuation — claims their latest model reaches 94.6% of Claude Opus 4.6’s coding performance, all while being open-weights and trained entirely without Nvidia hardware.

Here’s how GLM-5.1 compares to every major frontier model in 2026.

What Is GLM-5.1?

GLM-5.1 is an incremental upgrade to GLM-5, focused on improved coding and reasoning through enhanced post-training. The base architecture is shared with GLM-5:

Spec	Detail
Total parameters	744B (Mixture-of-Experts)
Active parameters	40-44B per token
Expert architecture	256 experts, 8 active per token
Context window	200K tokens
Max output	131,072 tokens
Training data	28.5 trillion tokens
Training hardware	100,000 Huawei Ascend 910B chips
License	MIT (open-weights)

The training infrastructure story is significant: GLM-5 and 5.1 were trained entirely on Huawei Ascend chips — no Nvidia GPUs. Given U.S. export controls on AI chips to China, this is a milestone for China’s AI self-sufficiency.

What’s New in 5.1

GLM-5.1 isn’t a new architecture — it’s a post-training refinement of GLM-5 focused on coding:

Coding benchmark score improved from 35.4 (GLM-5) to 45.3 (GLM-5.1) — a 28% gain
This puts it at 94.6% of Claude Opus 4.6’s coding score (45.3 vs 47.9)
Enhanced through progressive alignment: multi-task SFT → Reasoning RL → Agentic RL → General RL → on-policy cross-stage distillation

The Benchmark Comparison

Here’s how GLM-5/5.1 stacks up against every frontier model with available benchmark data:

Reasoning and Knowledge

Model	GPQA Diamond	AIME 2025	MMLU	HLE
GPT-5.2 (OpenAI)	92.4%	100%	~90%	N/A
Claude Opus 4.6 (Anthropic)	91.3%	99.8%	91.1%	53.1%
Qwen 3.5 (Alibaba)	88.4%	N/A	88.5%	N/A
GLM-5 (Zhipu AI)	86.0%	92.7%	88-92%	30.5
DeepSeek V3.2	N/A	89.3%	~88.5%	N/A
Gemini 2.5 Pro (Google)	84.0%	86.7%	89.8%	18.8%
Llama 4 Maverick (Meta)	84.0%	83.0%	85.5%	N/A

GLM-5 holds its own in reasoning — especially on AIME 2025 (92.7%), where it outperforms DeepSeek, Gemini, and Llama. But it trails Claude Opus 4.6 and GPT-5.2 on GPQA Diamond and Humanity’s Last Exam.

Coding

Model	SWE-bench Verified	LiveCodeBench	Coding Score
Claude Opus 4.6	80.8%	N/A	47.9
GPT-5.2	80.0%	N/A	N/A
GLM-5.1	77.8%	52.0%	45.3
Qwen 3.5	76.4%	83.6%	N/A
DeepSeek V3.2	73.1%	74.1%	N/A
Gemini 2.5 Pro	63.8%	70.4%	N/A
Llama 4 Maverick	N/A	39.7-70.4%	N/A

GLM-5.1’s coding improvement is its headline feature. At 77.8% SWE-bench Verified, it’s competitive with the top closed-source models — only 3 points behind Claude Opus 4.6 (80.8%) and GPT-5.2 (80.0%). For an open-weights model, this is exceptional.

Human Preference (Chatbot Arena)

Model	Arena ELO	Rank
Claude Opus 4.6	~1503	#1
GLM-5	1451	Top-tier

GLM-5 ranks #1 among open-weights models in both Text Arena and Code Arena on LMArena — a strong showing for human preference, even if it trails Opus 4.6 overall.

Pricing Comparison

One of GLM-5.1’s strongest selling points is cost.

Model	Input (per 1M tokens)	Output (per 1M tokens)
GLM-5.1	$1.00	$3.20
DeepSeek V3.2	$0.27	$1.10
Claude Sonnet 4.6	$3.00	$15.00
GPT-5.2	$3.00	$12.00
Claude Opus 4.6	$15.00	$75.00
Gemini 2.5 Pro	$1.25	$10.00

GLM-5.1 offers frontier-adjacent performance at a fraction of the cost of Claude Opus 4.6 or GPT-5.2. Only DeepSeek undercuts it on pure pricing.

Zhipu AI also offers a GLM Coding Plan subscription:

Lite: $3/month for 120 prompts
Pro: $15/month for 600 prompts

Compare that to Claude Max at $100-200/month.

What Makes GLM-5.1 Stand Out

1. Open-Weights at Frontier Scale

GLM-5 is the first open-weights model to reach score 50 on the Artificial Analysis Intelligence Index. The weights are available on HuggingFace under MIT license (zai-org/GLM-5), deployable via vLLM, SGLang, and KTransformers. GLM-5.1 weights are promised but not yet released.

2. No Nvidia Required

Trained on 100,000 Huawei Ascend 910B chips, GLM-5/5.1 proves that frontier AI training is possible without Nvidia hardware. This has geopolitical implications beyond the technical achievement.

3. Aggressive Post-Training

The 28% coding improvement from GLM-5 to 5.1 came entirely from post-training optimization — same base model, better alignment. Zhipu’s “progressive alignment” pipeline (multi-task SFT → multi-stage RL → cross-stage distillation) is producing real gains.

4. Reduced Hallucination

GLM-5 showed a 35-point improvement on the AA-Omniscience Index vs GLM-4.7, with better token efficiency (~110M output tokens vs ~170M for similar tasks). It says less and gets more right.

Limitations

Text-only. No image, audio, or video input. For multimodal tasks, you’ll need Claude, GPT, or Gemini.
Self-reported coding scores. The 94.6%-of-Opus claim uses Claude Code as the evaluation framework. Independent verification is pending.
Storage requirements. The full BF16 model requires ~1.49TB — self-hosting is non-trivial.
GLM-5.1 weights not yet released. Only GLM-5 is currently open-weights.

When to Use Which Model

Choose GLM-5.1 when:

You need frontier-tier coding performance at low cost
Open-weights / self-hosting matters for your deployment
You’re building on Chinese cloud infrastructure (Huawei Ascend)
Budget is a primary constraint and DeepSeek doesn’t meet your needs

Choose Claude Opus 4.6 when:

Maximum capability across all tasks is the priority
You need the best reasoning (GPQA 91.3%, HLE 53.1%, AIME 99.8%)
Agentic workflows and complex multi-step tasks are your use case
You need multimodal capabilities

Choose GPT-5.2 when:

Perfect math scores matter (AIME 100%)
You’re in the OpenAI ecosystem
You need strong multimodal and tool-use capabilities

Choose DeepSeek V3.2 when:

Cost efficiency is the top priority ($0.27/$1.10 per M tokens)
Open-source with strong coding (SWE-bench 73.1%)
You want the cheapest frontier-adjacent option

Choose Qwen 3.5 when:

You need the best open-source LiveCodeBench performance (83.6%)
SWE-bench 76.4% at open-weights is sufficient
Strong GPQA Diamond (88.4%) among open models

The Bottom Line

GLM-5.1 is a genuine frontier-adjacent model. At 94.6% of Claude Opus 4.6’s coding performance, 77.8% SWE-bench Verified, and $1.00/$3.20 per million tokens, it offers a compelling value proposition — especially as an open-weights model.

The bigger story is what GLM-5.1 represents: a Chinese lab producing frontier-competitive AI on domestic hardware, releasing it as open-weights, and pricing it aggressively. The gap between the best closed-source models (Claude Opus 4.6, GPT-5.2) and the best open models (GLM-5.1, Qwen 3.5, DeepSeek) continues to shrink.

For developers, this means more options at lower costs. For the industry, it means the frontier is getting crowded — and that’s good for everyone.