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Posted on Apr 10 • Originally published at apidog.com

GLM-5.1 vs Claude, GPT, Gemini, DeepSeek: how Zhipu AI's model stacks up

TL;DR

GLM-5.1 (744B MoE, 40-44B active parameters, MIT license) delivers 77.8% on SWE-bench vs. Claude Opus 4.6's 80.8%. Costs are $1.00/$3.20 per million tokens, compared to Claude Opus 4.6 at $15.00/$75.00. It's the strongest open-weights model in 2026, fully trained on Huawei hardware, with no Nvidia GPUs. If your team needs near-frontier coding performance at minimal cost, GLM-5.1 is the top open choice.

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Introduction

GLM-5.1, released by Zhipu AI on March 27, 2026, stands out for two reasons beyond benchmark numbers: it's fully open-weights under an MIT license, and it's trained exclusively on 100,000 Huawei Ascend 910B chips—zero Nvidia GPUs involved.

For teams that need to avoid supply chain lock-in or require model customization, these factors are as important as performance scores.

Specifications

Spec	GLM-5.1
Parameters	744B total (MoE)
Active per token	40-44B
Expert arch.	256 experts, 8 active per token
Context window	200K tokens
Max output	131,072 tokens
Training data	28.5 trillion tokens
Training HW	100,000 Huawei Ascend 910B
License	MIT (open weights)

The MoE (Mixture of Experts) architecture means GLM-5.1 can scale to 744B total parameters while running inference with just 40-44B per token, making it resource-efficient relative to its capacity.

Benchmark Comparison

Reasoning and Knowledge

Benchmark	GLM-5 (5.1 baseline)	Claude Opus 4.6	Notes
AIME 2025	92.7%	~88%	GLM-5 outperforms
GPQA Diamond	86.0%	91.3%	Claude leads
MMLU	88-92%	~90%+	Comparable

Coding

Benchmark	GLM-5.1	Claude Opus 4.6
SWE-bench	77.8%	80.8%
LiveCodeBench	52.0%	Higher

GLM-5.1 scores 77.8% on SWE-bench—just 3 points below Claude Opus 4.6 and ahead of GPT-5, Gemini, and DeepSeek on this task. The 28% coding gain from GLM-5 to 5.1 was achieved via post-training refinement.

Human Preference (LMArena)

GLM-5 tops all open-weights models on LMArena for both Text and Code, and is competitive with closed models.

Pricing Comparison

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 provides about 94.6% of Claude Opus 4.6’s coding performance at only 1/15th the cost (per Zhipu AI’s internal data; independent verification ongoing).

If you’re running large-scale coding agents, this cost difference is a major operational advantage.

The Open-Weights Advantage

GLM-5.1 is available on Hugging Face under MIT license. You can:

Download and self-host (requires ~1.49TB for full BF16)
Fine-tune on your own datasets
Deploy with complete control over data and infra
Modify architecture or post-train for special use cases

Note: Full self-hosting requires significant storage and GPU investment (1.49TB, 744B parameters). For most, API access is the practical approach.

Limitations

Text-only: GLM-5.1 handles text input only—no image, audio, or video. For multimodal needs, consider GPT-5.2 or Gemini 2.5 Pro.
Benchmark verification: Coding benchmarks are based on Claude Code evaluation; independent validation is pending.
Weights release: Only GLM-5 weights are public; GLM-5.1 is API-only at publication time.
High self-hosting cost: 1.49TB storage and substantial infra required for full deployment.

Testing GLM-5.1 with Apidog

To test GLM-5.1 in your workflow, use the API via WaveSpeedAI (recommended):

POST https://api.wavespeed.ai/api/v1/chat/completions
Authorization: Bearer {{WAVESPEED_API_KEY}}
Content-Type: application/json

{
  "model": "glm-5",
  "messages": [
    {
      "role": "user",
      "content": "{{coding_task}}"
    }
  ],
  "temperature": 0.2,
  "max_tokens": 4096
}

To compare with Claude Opus 4.6:

POST https://api.anthropic.com/v1/messages
x-api-key: {{ANTHROPIC_API_KEY}}
anthropic-version: 2023-06-01
Content-Type: application/json

{
  "model": "claude-opus-4-6",
  "max_tokens": 4096,
  "messages": [{"role": "user", "content": "{{coding_task}}"}]
}

Use the same {{coding_task}} for both APIs. Compare:

Code correctness (does it work?)
Code quality (is it well-structured/readable?)
Response length (conciseness)
Token usage (see response metadata)

With pricing at $1.00/$3.20 vs. $15.00/$75.00, running the same coding task is about 20-25x cheaper on GLM-5.1.

Who Should Use GLM-5.1

Best fit for:

Teams seeking near-frontier coding performance at lower cost
Organizations needing open-weights models for compliance or customization
Developers targeting Chinese or multilingual applications
Researchers studying advanced open models

Consider alternatives if:

You need multimodal (text+image/audio/video): GPT-5.2 or Gemini 2.5 Pro
You require the absolute best reasoning regardless of cost: Claude Opus 4.6
You want the lowest possible costs: DeepSeek V3.2 ($0.27/$1.10)

FAQ

Is GLM-5.1 available via an OpenAI-compatible API?

Yes—GLM models support API formats compatible with common SDKs. Check Zhipu AI’s docs for endpoint details.

Why is Huawei hardware significant?

Most top models are trained on Nvidia A100/H100 clusters. GLM-5.1 demonstrates that high-end training is possible on Huawei Ascend, showing viable alternatives to Nvidia.

Can I use GLM-5.1 commercially?

Yes—the MIT license allows commercial use, modification, and redistribution. This is more permissive than most other leading models.

How does GLM-5.1 compare to other open-source models?

GLM-5 ranks #1 among open-weights models on LMArena, outperforming Llama, Qwen, and other open options.

What can I do with a 200K context window?

200K tokens ≈ 150,000 words. You can process a full book, large codebase, or dozens of documents at once—ideal for document analysis or code review at scale.

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