Has anyone tried Llama 3.1 70B for SQL query generation? How does it stack up?

I've been conducting a systematic evaluation of several large language models for structured data tasks, specifically focusing on generating SQL queries from natural language prompts. With the recent release of Meta's Llama 3.1 70B on various cloud platforms, I integrated it into my testing pipeline to see if its performance justifies its positioning as a cost-effective alternative to leaders like GPT-4 and Claude 3 Opus.

My methodology uses a curated benchmark of 150 prompts derived from the BIRD and Spider datasets, with modifications to test edge cases like complex joins, nested subqueries, and window functions. Each model receives the same system prompt and schema context. I measure three key dimensions:
* **Syntactic Correctness:** Does the query parse without errors?
* **Semantic Accuracy:** Does the query, when executed against a known test database, return the correct result set?
* **Latency & Cost:** Measured in seconds per token (p95) and cost per 1k output tokens at the provider level.

For Llama 3.1 70B, I used the version hosted on a major cloud provider's inference service. My initial findings are mixed. The model demonstrates a strong grasp of SQL syntax and consistently produces well-structured queries. However, its performance on semantic accuracy lags behind the top-tier models, particularly on prompts requiring nuanced understanding of the question's intent.

Here is a representative example from my test suite:

**Prompt:** "Show me the total sales for each product category in the last quarter, but only for categories where the average sale price was above $50."

**Schema Context:**
```sql
CREATE TABLE sales (
sale_id INT PRIMARY KEY,
product_id INT,
category_id INT,
sale_amount DECIMAL(10,2),
sale_date DATE
);
CREATE TABLE categories (
category_id INT PRIMARY KEY,
category_name VARCHAR(100)
);
```

**Llama 3.1 70B Output:**
```sql
SELECT
c.category_name,
SUM(s.sale_amount) AS total_sales
FROM sales s
JOIN categories c ON s.category_id = c.category_id
WHERE s.sale_date >= DATEADD(quarter, -1, GETDATE())
GROUP BY c.category_name
HAVING AVG(s.sale_amount) > 50;
```
This query is syntactically perfect and logically sound for the given schema. In this case, it passed both syntactic and semantic checks. The failure cases typically arose with more abstract prompts or when the schema involved ambiguous column names.

My quantitative results so far (averaged over 3 runs per model on the same 150 prompts) are as follows:

| Model | Syntactic Correctness | Semantic Accuracy | Avg. Latency (p95) | Cost per 1k Output Tokens |
| :--- | :--- | :--- | :--- | :--- |
| GPT-4 | 98% | 92% | 4.2s | ~$0.12 |
| Claude 3 Opus | 96% | 90% | 5.1s | ~$0.15 |
| **Llama 3.1 70B** | **95%** | **84%** | **3.8s** | **~$0.04** |
| Claude 3 Sonnet | 92% | 82% | 2.1s | ~$0.012 |

The data suggests Llama 3.1 70B offers a compelling trade-off. Its semantic accuracy is notably lower than the frontier models, but its latency is competitive and its cost is dramatically lower. For applications where absolute precision is critical, the additional cost for GPT-4 or Claude Opus may be justified. For lower-stakes internal tooling or scenarios where a human can quickly verify the output, Llama 3.1 70B presents a strong value proposition.

I'm interested to hear if others have run similar comparative tests. Specifically:
* Has anyone else observed a particular class of SQL queries (e.g., those involving `WITH` clauses or complex aggregations) where Llama 3.1 70B consistently fails?
* Are there specific prompting strategies or few-shot examples that have successfully bridged the semantic accuracy gap for this model?
* Has anyone done load testing against the API endpoints for sustained SQL generation workloads? I'm curious about throughput degradation and error rates under concurrent request patterns.

I will be publishing my full benchmark code, dataset, and results on my blog next week for full reproducibility.

-ck