Query datasets, generate SQL, interpret statistical output, and summarize findings using Claude, DeepSeek R1, Llama 4, and 37 other models through a single OpenAI-compatible endpoint.
Try Brainiall free for 7 daysData analysis has always involved two distinct layers of work: the mechanical layer (writing queries, cleaning data, running aggregations) and the interpretive layer (explaining what numbers mean, spotting anomalies, communicating findings to non-technical stakeholders). Historically, only the mechanical layer could be automated. The interpretive layer required a human sitting in front of a spreadsheet or notebook.
Large language models changed that equation. A well-prompted LLM can read a CSV sample and identify likely data quality issues, translate a business question into a SQL query, explain why a particular metric spiked on a given date, or draft an executive summary from a pandas DataFrame output. These are not hypothetical capabilities; data teams at companies of every size are already using LLMs as a layer on top of their existing tools.
The challenge has been fragmentation. Different models have different strengths. DeepSeek R1 is exceptionally good at multi-step numerical reasoning. Claude Sonnet 4.6 produces clean, well-structured prose explanations. Llama 4 is fast and cost-efficient for high-volume batch summarization. Qwen3 handles multilingual data reports well. Previously, using all of these required separate API keys, separate SDKs, and separate billing relationships.
Brainiall solves that problem by unifying 104 models under a single OpenAI-compatible API at https://api.brainiall.com/v1. You pick the right model for each task in your pipeline without changing any infrastructure.
Not every model is equally suited to every data analysis task. Here is how the Brainiall catalog maps to common analyst workflows:
Use DeepSeek R1 or DeepSeek V3 for SQL generation. Both models have strong structured-output behavior and handle complex JOIN logic, window functions, and CTEs reliably. DeepSeek R1 is the reasoning variant and is worth the extra latency when the query involves multiple nested subqueries or ambiguous business logic.
Use Claude Sonnet 4.6 for turning raw statistical output into readable prose. Claude models follow instructions precisely and produce well-organized explanations without hallucinating numbers. For longer reports that need to stay within a tight token budget, Claude Haiku 4.6 is faster and cheaper while still producing accurate summaries.
Use Llama 4 for high-volume EDA tasks where you are processing many files or many dataset slices in parallel. Llama 4 is fast and the per-token cost through Brainiall is low, making it practical to run across hundreds of CSV segments in a batch pipeline.
Use Qwen3 for datasets and reports that need to be produced in languages other than English. Brainiall supports 9 languages (pt-BR, en, es, ar, fr, de, id, tr, vi) and Qwen3 is particularly strong across Asian and European language pairs. Mistral Large is a solid alternative for French and other European languages.
Use Claude Opus 4.6 when you need the most thorough possible interpretation of an anomaly, such as explaining a revenue drop across multiple dimensions or reconciling conflicting metrics from two data sources. Opus is the highest-capability model in the Brainiall catalog and is worth the higher cost for complex reasoning tasks.
Brainiall's free tier includes production-ready NLP endpoints for toxicity detection, sentiment analysis, PII detection, and language detection. These are useful in data analysis pipelines where you need to screen or annotate text columns in a dataset before passing them to a more expensive LLM step.
System: You are a senior data analyst. Write production-ready SQL for a PostgreSQL 15 database.
Always use CTEs for readability. Return only the SQL, no explanation.
User: I have a table called orders with columns: order_id, customer_id, order_date, revenue,
status (values: completed, refunded, pending). I want to find the top 10 customers by net
revenue (completed minus refunded) over the last 90 days, excluding customers who placed
fewer than 3 completed orders in that period.A good response from DeepSeek R1 will return a clean CTE-based query that calculates completed revenue and refunded revenue separately, subtracts them, filters on the 90-day window using CURRENT_DATE - INTERVAL '90 days', applies the minimum order count filter in a HAVING clause, and orders by net revenue descending with a LIMIT of 10. It will not add unnecessary explanation or placeholder column names.
System: You are a data analyst writing for a non-technical business audience.
Explain findings clearly. Do not use jargon without defining it. Keep the summary under 200 words.
User: Here is the output of a linear regression predicting monthly churn rate from
three features: average_session_duration (coef: -0.42, p=0.003), support_tickets_opened
(coef: 0.31, p=0.001), nps_score (coef: -0.18, p=0.041). R-squared: 0.67. N=1,240 customers.
Explain what this tells us and what actions we might take.Claude Sonnet 4.6 will produce a concise, accurate interpretation: longer sessions correlate with lower churn, more support tickets correlate with higher churn, and NPS has a smaller but statistically significant protective effect. It will note that the model explains 67% of variance, which is reasonably strong for behavioral data, and suggest actionable directions like improving session engagement and reducing support ticket volume. It will not invent numbers or overstate certainty.
System: You are a data analyst. Given a summary of weekly metrics, identify the most likely
causes of anomalies and suggest 3 hypotheses worth investigating. Be specific.
User: Week of March 10: revenue $420,000 (down 34% vs prior week), orders 3,100 (down 8%),
average order value $135 (down 28%), new customer signups 890 (up 12%), return customer
orders 1,840 (down 22%). The prior week had a sitewide 20%-off promotion that ended March 9.A strong response from Claude Opus 4.6 will correctly identify that the AOV drop is largely explained by the promotion ending (customers who were buying at a discount stopped or reduced purchases), that the return customer order drop is consistent with promotion-driven pull-forward demand, and that the new customer signup increase may reflect organic growth unrelated to the promotion. It will suggest investigating cohort behavior of customers acquired during the promotion, checking if the AOV drop is uniform across categories, and comparing the week to the same week in the prior year to isolate seasonality.
If your data analysis pipeline already uses the OpenAI Python SDK, switching to Brainiall requires two changes: the base_url and the api_key. Your existing message formatting, streaming logic, and response parsing all work without modification.
from openai import OpenAI
import json
client = OpenAI(
base_url="https://api.brainiall.com/v1",
api_key="brnl-your-key-here" # get yours at app.brainiall.com/signup
)
schema = """
Table: sales
Columns: sale_id (int), product_id (int), sale_date (date),
quantity (int), unit_price (numeric), region (varchar)
"""
question = "What are the top 5 regions by total revenue in Q1 2026, and how does each compare to Q1 2025?"
response = client.chat.completions.create(
model="deepseek-r1",
messages=[
{
"role": "system",
"content": "You are a senior data analyst. Write production-ready PostgreSQL 15 SQL. Use CTEs. Return only SQL."
},
{
"role": "user",
"content": f"Schema:\n{schema}\n\nQuestion: {question}"
}
],
temperature=0.1 # low temperature for deterministic SQL output
)
sql_query = response.choices[0].message.content
print(sql_query)
# Now interpret the results with Claude
results_sample = """
region | q1_2026_revenue | q1_2025_revenue | yoy_change
Northeast | 1420000 | 1180000 | +20.3%
Southwest | 980000 | 1050000 | -6.7%
Midwest | 870000 | 790000 | +10.1%
Southeast | 760000 | 680000 | +11.8%
West | 640000 | 590000 | +8.5%
"""
interpretation = client.chat.completions.create(
model="claude-sonnet-4-6",
messages=[
{
"role": "system",
"content": "You are a data analyst writing for a VP of Sales. Be concise, specific, and actionable. Max 150 words."
},
{
"role": "user",
"content": f"Interpret these regional revenue results:\n{results_sample}"
}
]
)
print(interpretation.choices[0].message.content)deepseek-r1 for SQL generation and claude-sonnet-4-6 for interpretation. You can swap either model string for any of the 104 models available on Brainiall without changing any other code. Use Brainiall Studio to test alternatives before updating your production model string.
| Model | SQL Generation | Statistical Interpretation | Multilingual Reports | Anomaly Reasoning | High-Volume Batch |
|---|---|---|---|---|---|
| DeepSeek R1 | Excellent | Good | Limited | Excellent | Slower |
| DeepSeek V3 | Very Good | Good | Limited | Good | Fast |
| Claude Sonnet 4.6 | Good | Excellent | Good | Very Good | Moderate |
| Claude Opus 4.6 | Very Good | Excellent | Good | Excellent | Slower |
| Claude Haiku 4.6 | Good | Good | Good | Limited | Very Fast |
| Llama 4 | Good | Good | Good | Limited | Excellent |
| Qwen3 | Good | Good | Excellent | Moderate | Fast |
| Mistral Large | Good | Good | Very Good | Moderate | Fast |
LLMs do not need your entire dataset to generate SQL or interpret results. Sending a schema definition and a few representative rows is almost always sufficient for SQL generation. For interpretation tasks, send the aggregated output, not the raw rows. This keeps costs low, latency fast, and reduces the risk of exposing PII unnecessarily.
SQL is deterministic by nature. Set temperature to 0.0 or 0.1 when generating SQL queries. Higher temperatures introduce unnecessary variation and can cause the model to produce syntactically valid but logically incorrect queries. Reserve higher temperatures for narrative and creative interpretation tasks.
SQL syntax varies across PostgreSQL, MySQL, BigQuery, Snowflake, and SQLite. Always include the database name and version in your system prompt. A query that works in BigQuery may fail in PostgreSQL due to differences in date functions, window function syntax, or string handling.
LLMs do not know what "active customer" means in your system, or that your status column uses numeric codes instead of strings. Include business definitions in your system prompt or user message. A few sentences of context dramatically improves the accuracy of generated queries and interpretations.
Always run generated SQL in a read-only environment or against a test database before using it in production. LLMs can produce plausible-looking queries that contain logical errors, especially for complex aggregations or edge cases in your data. Treat LLM-generated SQL as a first draft, not a final artifact.
model parameter in your API call. Your message structure, response parsing, and streaming logic all remain identical. This makes it straightforward to A/B test models in production or route different task types to different models based on cost or latency requirements.The Pro plan is R$29 per month (about US$5.99). Your first 7 days are free. No credit card required to start. Sign up at app.brainiall.com and get your brnl-* API key in under two minutes.
API docs at app.brainiall.com | Chat UI at chat.brainiall.com