GLM 5.2 for Data Analysis: Structured Output, SQL Generation, and Python Workflows

Most LLM-powered data workflows hit the same bottleneck: context limits cut off large schemas, CSV files, or log dumps before the model has enough information to generate reliable code. GLM 5.2 removes that bottleneck with a 1,048,576-token context window, a native JSON mode, and an output speed of 158 tokens per second — fast enough for interactive analysis sessions rather than overnight batch jobs.

This guide shows how to set up GLM 5.2 for four practical data tasks: SQL generation from a full database schema, CSV analysis without sampling, structured JSON extraction, and automated Pandas workflow generation.

Prerequisites

• A Z.ai API key (sign up at z.ai; the model identifier is glm-5.2)
• Python 3.9+ with the openai package installed (pip install openai)
• Basic familiarity with the OpenAI Python SDK — GLM 5.2 uses the same interface via a different base URL
• Optionally: pandas and sqlalchemy for the data workflow examples

The API base URL is https://api.z.ai/v1. You can also access GLM 5.2 through OpenRouter using the model string z-ai/glm-5.2 if you prefer a single gateway for multiple providers.

Quick Specs

At $1.40/M input tokens, a 10,000-row CSV file pasted directly into the prompt costs roughly $0.01. That is cheap enough to run hundreds of exploratory analysis calls per day without careful token budgeting.

Step 1: Configure the Client

GLM 5.2 accepts standard OpenAI SDK calls with one change: the base URL.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_ZAI_API_KEY",
    base_url="https://api.z.ai/v1",
)

# Quick sanity check
response = client.chat.completions.create(
    model="glm-5.2",
    messages=[{"role": "user", "content": "Reply with the word READY."}],
    max_tokens=10,
)
print(response.choices[0].message.content)

If you are routing through OpenRouter, change api_key to your OpenRouter key, base_url to https://openrouter.ai/api/v1, and model to z-ai/glm-5.2. Everything else stays identical.

Step 2: SQL Generation from a Full Schema

The standard problem with LLM SQL generation is truncating the schema. With a 1M context window you can paste every table, view, index definition, and sample row without worrying about overflow.

schema = open("full_schema.sql").read()  # entire DB schema, no truncation needed

def generate_sql(natural_language_query: str, schema: str) -> str:
    prompt = f"""You are a SQL expert. Use the schema below to write a precise SQL query.
Return only the SQL — no explanation, no markdown fences.

SCHEMA:
{schema}

QUERY REQUEST:
{natural_language_query}"""

    response = client.chat.completions.create(
        model="glm-5.2",
        messages=[{"role": "user", "content": prompt}],
        temperature=0,  # deterministic output for SQL
    )
    return response.choices[0].message.content.strip()

# Example
sql = generate_sql(
    "Show the top 10 customers by revenue in Q2 2026, including their churn risk score.",
    schema,
)
print(sql)

Setting temperature=0 is important for SQL generation — you want deterministic output, not creative variation. GLM 5.2's 89% GPQA Diamond score reflects strong reasoning across complex schemas with many joins and foreign key relationships.

Step 3: CSV Analysis Without Sampling

Most pipelines sample large CSVs before sending them to an LLM. With GLM 5.2 you can skip that step for files up to a few hundred thousand rows, depending on row width.

import pandas as pd

def analyze_csv(filepath: str, question: str) -> str:
    df = pd.read_csv(filepath)
    csv_text = df.to_csv(index=False)

    prompt = f"""Analyze the following CSV data and answer the question.
Be specific: include exact numbers, percentages, and column names.

DATA:
{csv_text}

QUESTION:
{question}"""

    response = client.chat.completions.create(
        model="glm-5.2",
        messages=[{"role": "user", "content": prompt}],
    )
    return response.choices[0].message.content

result = analyze_csv(
    "sales_q2_2026.csv",
    "Which product category had the highest month-over-month growth in June? "
    "What drove it, based on the data?",
)
print(result)

For very large files, estimate token count first: one token is roughly 4 characters. A 100,000-row CSV with 10 columns of short values is typically 5–10M characters — that exceeds the 1M token window. In those cases, use Pandas to filter to relevant rows before pasting. For most real-world analytical datasets (a few thousand to tens of thousands of rows), the full-paste approach works without sampling.

Step 4: Structured JSON Extraction

JSON mode forces the model to return valid JSON every time. This is the foundation of reliable pipelines: you get typed data you can pass directly to downstream functions without parsing hacks.

import json

def extract_structured(text: str, schema_description: str) -> dict:
    response = client.chat.completions.create(
        model="glm-5.2",
        response_format={"type": "json_object"},
        messages=[
            {
                "role": "system",
                "content": f"Extract data from the input and return a JSON object. Schema: {schema_description}",
            },
            {"role": "user", "content": text},
        ],
        temperature=0,
    )
    return json.loads(response.choices[0].message.content)

# Example: extract KPIs from a financial report paragraph
report_excerpt = """
Revenue for Q2 2026 came in at $4.2M, up 18% year over year.
Gross margin improved to 67% from 61% in Q2 2025.
Operating expenses were $2.8M, driven by a 40% increase in R&D headcount.
Net loss narrowed to $380K from $910K in the prior year quarter.
"""

kpis = extract_structured(
    report_excerpt,
    '{"revenue_usd": number, "revenue_yoy_pct": number, "gross_margin_pct": number, '
    '"opex_usd": number, "net_loss_usd": number}',
)
print(json.dumps(kpis, indent=2))
# Output:
# {
#   "revenue_usd": 4200000,
#   "revenue_yoy_pct": 18,
#   "gross_margin_pct": 67,
#   "opex_usd": 2800000,
#   "net_loss_usd": 380000
# }

JSON mode is reliable for extraction tasks because GLM 5.2 is instruction-following by design — the MIT-licensed open weights were trained with strong RLHF on structured output formats. You can also define schemas via function calling if you need more complex nested types.

Step 5: Automated Pandas Workflow Generation

Instead of writing Pandas transformations by hand, describe the transformation and let GLM 5.2 generate the code. This is particularly useful for data cleaning pipelines that handle messy, inconsistent real-world data.

def generate_pandas_code(df_description: str, transformation: str) -> str:
    prompt = f"""Write a Python function using pandas that performs the transformation described.
Use type hints. Handle edge cases (NaN, empty strings, type mismatches).
Return only the function — no imports, no explanation.

DATAFRAME DESCRIPTION:
{df_description}

TRANSFORMATION:
{transformation}"""

    response = client.chat.completions.create(
        model="glm-5.2",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.1,
    )
    return response.choices[0].message.content

code = generate_pandas_code(
    df_description="""
    Columns: customer_id (str), signup_date (str, various formats like '2024-01-15', 'Jan 15 2024', '01/15/24'),
    plan (str, values: 'free', 'pro', 'enterprise', sometimes null), mrr (float, sometimes negative due to credits),
    churn_date (str, null if active)
    """,
    transformation="""
    1. Parse signup_date and churn_date to datetime, coerce errors to NaT
    2. Normalize plan to lowercase, fill nulls with 'free'
    3. Clip mrr to 0 minimum (no negative values)
    4. Add a boolean 'is_churned' column (True if churn_date is not NaT)
    5. Add 'tenure_days' as days from signup_date to churn_date (or today if active)
    """,
)
print(code)

GLM 5.2 scored 90%+ on HumanEval, which means generated Python code is typically syntactically correct and handles the described edge cases without needing manual fixes. For production use, always review generated code — but for exploratory work, the output-test-refine loop runs quickly at 158 t/s.

Step 6: Batch Analysis Pipeline

For processing many records in parallel — survey responses, log files, support tickets — structure the batch calls to use cached context where possible.

import asyncio
from openai import AsyncOpenAI

async_client = AsyncOpenAI(
    api_key="YOUR_ZAI_API_KEY",
    base_url="https://api.z.ai/v1",
)

SYSTEM_PROMPT = """You are a data analyst. Classify the customer feedback and extract:
- sentiment: positive | neutral | negative
- topics: list of up to 3 main topics mentioned
- urgency: low | medium | high
Return JSON only."""

async def classify_feedback(text: str) -> dict:
    response = await async_client.chat.completions.create(
        model="glm-5.2",
        response_format={"type": "json_object"},
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": text},
        ],
        temperature=0,
    )
    return json.loads(response.choices[0].message.content)

async def batch_classify(feedbacks: list[str]) -> list[dict]:
    tasks = [classify_feedback(f) for f in feedbacks]
    return await asyncio.gather(*tasks)

# Run
feedbacks = [
    "The dashboard is slow and keeps timing out during peak hours.",
    "Loving the new export feature, saved me hours this week.",
    "Billing charged me twice last month, need a refund immediately.",
]

results = asyncio.run(batch_classify(feedbacks))
for feedback, result in zip(feedbacks, results):
    print(f"{feedback[:50]}... → {result}")

At $1.40/M input tokens and a system prompt that is ~80 tokens, classifying 10,000 feedback entries costs under $2. The cache hit price of $0.26/M applies when the same system prompt prefix is reused across calls — batch jobs with a fixed system prompt qualify automatically.

Pricing Breakdown for Data Workflows

These numbers use the published rates: $1.40/M for input, $4.40/M for output, $0.26/M for cache hits. See GLM 5.2 pricing for a full comparison against other frontier models.

Common Issues

Frequently Asked Questions

Does GLM 5.2 support image inputs for chart analysis?

No. GLM 5.2 is text-only — it does not accept image or audio input. For visual chart analysis you need a multimodal model. GLM 5.2's strength is in text-based data: schemas, CSVs, logs, and structured documents pasted into the context window.

How does the 1M context window compare to other models?

GLM 5.2's 1,048,576-token context is among the largest available. For comparison, most frontier models offer 128K–200K tokens as their extended context. The practical impact for data analysis is that you can paste a full database schema (even complex ones with dozens of tables) plus sample data plus your question in a single call — no chunking or retrieval layer required.

Is the JSON mode reliable enough for production pipelines?

Yes, with a caveat. JSON mode (response_format={"type":"json_object"}) guarantees syntactically valid JSON. It does not guarantee the keys and types match your expected schema. Add a Pydantic validation step after parsing to catch structural mismatches before they propagate downstream.

Can GLM 5.2 handle multi-step data analysis (EDA, then hypothesis, then visualization code)?

Yes. The 1M context window means you can accumulate the full conversation — initial data paste, EDA output, follow-up questions, and generated code — without losing earlier context. For long sessions, monitor token usage via response.usage.total_tokens and start a new session if you approach 900K tokens.

What is the best way to handle confidential data?

Review Z.ai's data processing terms before sending sensitive data to the API. For regulated data (PII, financial records, HIPAA-covered data), consider running the open-weight GLM 5.2 model locally via Hugging Face (THUDM/GLM-5.2) or in a private cloud deployment. The MIT license permits this without restriction.

How does GLM 5.2 compare to GPT-4o for SQL generation?

Both models perform well on SQL generation. GLM 5.2's advantage is the larger context window (1M vs. 128K) — relevant when schemas are large — and lower input price ($1.40/M vs. $2.50/M for GPT-4o). For standard SQL tasks on medium-sized schemas, the quality difference is marginal; the cost and context differences are meaningful at scale. See the full comparison in GLM 5.2 vs GPT-4o.

Does the MIT license cover commercial use of generated outputs?

Yes. The MIT license covers the model weights and does not restrict how you use the model's outputs. Generated SQL, Python code, and extracted JSON are yours to use commercially without attribution or royalty.

Next Steps

The workflows above cover the most common data analysis patterns. From here:

• Log analysis at scale: Use the 1M context to paste multi-day log files and ask for anomaly detection or error pattern summaries.
• Financial report generation: Combine structured JSON extraction with a report-writing prompt in a two-step pipeline.
• Data cleaning automation: Generate a full cleaning script by describing your data quality issues; run it with Pandas and iterate on the output.
• EDA automation: Paste a new dataset and ask GLM 5.2 to produce a full exploratory analysis plan with suggested visualizations.

Try GLM 5.2 — no API key needed: glm5.app/chat.

Sources

• GLM 5.2 on Artificial Analysis — speed, TTFT, and benchmark rankings
• THUDM/GLM-5.2 on Hugging Face — model weights, architecture details, MIT license
• Z.ai API documentation — endpoint reference, JSON mode, pricing
• GLM 5.2 on OpenRouter — alternative API access, live pricing