GLM-5.1 Complete Review: Benchmarks, Pricing & Use Cases (2026)

GLMZhipu AIReviewBenchmarks

Published July 20, 2026

GLM-5.1, developed by Zhipu AI (formerly Z.ai), is one of the most underrated Chinese AI models. While DeepSeek and Kimi dominate headlines, GLM-5.1 quietly delivers excellent multilingual performance, strong structured output, and competitive pricing that makes it the best-value flagship model in many scenarios.

What Sets GLM-5.1 Apart

128K context window — matches DeepSeek V4 Pro and GPT-4o
Native multilingual — trained on 100+ languages with strong English, Chinese, Japanese, Korean, and European language support
Structured output excellence — best-in-class JSON schema compliance, making it ideal for tool-calling and data extraction pipelines
Balanced performance profile — no dramatic weaknesses, consistently solid across all benchmarks

Benchmark Performance

Benchmark	GLM-5.1	DeepSeek V4 Pro	GPT-4o
HumanEval (Coding)	87.3%	92.1%	90.2%
MMLU (Knowledge)	86.8%	88.7%	88.7%
MATH (Reasoning)	62.7%	68.4%	63.2%
MCRE (Multilingual)	89.4%	87.1%	88.9%
JSON Schema Compliance	96.2%	93.8%	95.1%
Function Calling	91.7%	90.2%	94.3%

GLM-5.1’s sweet spot: multilingual applications and structured output. It outperforms both DeepSeek and GPT-4o on multilingual comprehension (MCRE) and matches them on JSON schema compliance. If your application processes text in multiple languages or relies on precise structured output, GLM-5.1 is the optimal choice.

Pricing

Model	Input $/1M	Output $/1M	Cache Hit $/1M
GLM-5.1	$2.10	$6.60	$0.68
GLM-5	$1.55	$4.96	$0.40
GLM-4.7	$0.93	$3.41	$0.22
GLM-4.5	$0.70	$2.17	$0.18
GLM-4.5-air	$0.65	$2.03	$0.162

GLM-5.1 sits at a sweet spot in the GLM lineup: $2.10 input (same range as DeepSeek V4 Pro) with $6.60 output (34% more than DeepSeek but 34% less than GPT-4o). The 68% cache hit savings ($0.68 vs $2.10) is particularly generous.

API Integration

from openai import OpenAI

client = OpenAI(
    base_url="https://aiwave.live/v1",
    api_key="your-api-key"
)

# Multilingual translation
response = client.chat.completions.create(
    model="glm-5.1",
    messages=[
        {"role": "system", "content": "Translate accurately preserving tone."},
        {"role": "user", "content": "Translate to Japanese: Our AI platform provides unified access to top models."}
    ]
)

# Structured data extraction
response = client.chat.completions.create(
    model="glm-5.1",
    messages=[
        {"role": "user", "content": "Extract company name, revenue, and growth rate from this text: ..."}
    ],
    response_format={"type": "json_object"}
)

Full API docs available. GLM-5.1 supports streaming, function calling, JSON mode, and vision through the OpenAI-compatible endpoint.

Tutorial: Structured Data Extraction with GLM-5.1

GLM-5.1 excels at extracting structured data from unstructured text. Here’s a real-world example — extracting product data from reviews:

from openai import OpenAI
import json

client = OpenAI(base_url="https://aiwave.live/v1", api_key=***

review = """I bought the Model X Pro last month. The battery life is incredible - 
I get 2 full days on a single charge. Screen quality is amazing at 1440p.
However, the speakers could be better. Price was $899 from Amazon.""""

response = client.chat.completions.create(
    model="glm-5.1",
    messages=[
        {"role": "system", "content": """Extract product attributes as JSON.
Schema: {"product_name": str, "battery_life": str, "screen": str, "speakers": str, "price": str, "sentiment": str}"""},
        {"role": "user", "content": review}
    ],
    response_format={"type": "json_object"},
    temperature=0.1
)

result = json.loads(response.choices[0].message.content)
print(json.dumps(result, indent=2))
# Output:
# {
#   "product_name": "Model X Pro",
#   "battery_life": "2 days on single charge",
#   "screen": "amazing at 1440p",
#   "speakers": "could be better",
#   "price": "$899",
#   "sentiment": "mostly positive"
# }

Why GLM-5.1 for this task: Its 96.2% JSON schema compliance means you get well-formed JSON nearly every time, reducing your error handling code. At $2.10/$6.60, it’s significantly cheaper than GPT-4o ($2.50/$10.00) for the same task.

GLM-5.1 vs GLM-5: When to Upgrade

GLM-5 is 26% cheaper on output ($4.96 vs $6.60) but loses 10-15% on benchmarks. Use GLM-5 for general chat and simple tasks. Upgrade to GLM-5.1 for multilingual workloads, structured output, and any task where accuracy matters.

Best Use Cases

Multilingual applications — best multilingual comprehension among all Chinese models
Structured data extraction — highest JSON schema compliance rate
API tool-calling systems — reliable function calling for agent frameworks
General production workloads — balanced quality, no dramatic weaknesses
Cost-conscious teams — cheaper than GPT-4o with competitive quality

Conclusion

GLM-5.1 is the Swiss Army knife of Chinese AI models. It may not lead any single benchmark, but its consistency across coding, reasoning, multilingual, and structured output tasks — combined with competitive pricing — makes it the smart default choice for teams that need reliability over specialization.

Try GLM-5.1 with $1 credit

Access GLM-5.1, DeepSeek V4 Pro, Kimi K3 through one API.

Get Started →