TL;DR
DeepSeek V3 (deepseek/deepseek-chat) appears in 2 published studies. 2025-04-10, Seven models generating financial SQL queries: 0.617 for average score. 2025-04-15, Models generating SQL for financial questions, April 2025: 0.617 for average score. Each result belongs to its own task, date and grading method.
- Exact tested ID
- deepseek/deepseek-chat
- Published studies
- 2
- Provider
- deepseek
Inspect this exact model
Seven models generating financial SQL queries
DeepSeek V3: source evidence dated 2025-04-15.
- Average score
- 0.617
- Median score
- 1.000
- Std deviation
- 0.460
- Success rate
- 75.0%
- Avg execution time (ms)
- 26,892
- Perfect score (1.0) share
- 52.5%
The displayed values belong to the identified model versions and this published task, with their original metric units.
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2025-04-10: Seven models generating financial SQL queriesSelect a model for the specific tested task. A profile with one study does not establish performance on other jobs.
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Seven models generating financial SQL queries
See the complete seven models generating financial sql queries
DeepSeek V3 recorded 0.617 for average score in the 2025-04-10 publication. 7 models on a shared SQL question set, the article does not state the number of questions.
| Published metric | Value |
|---|---|
| Average score | 0.617 |
| Median score | 1.000 |
| Std deviation | 0.460 |
| Success rate | 75.0% |
| Avg execution time (ms) | 26,892 |
| Perfect score (1.0) share | 52.5% |
No matching rows. Clear the filter to see all records.
Seven models generating financial SQL queries: read these measurements
View task, sample and measurement units
7 models on a shared SQL question set, the article does not state the number of questions
Generate SQL queries that answer natural-language financial questions over NexusTrade's stock data.
Scoring
Each query receives a 0 to 1 score. The table reports the distribution in buckets 0.0-0.2, 0.3-0.5, 0.6-0.7, 0.8-0.9 and 1.0. The grader is not described in this article.
Cost units
The article publishes no per-model cost.
Time units
Average execution time in milliseconds per model, from 1,296 ms (Gemini 2.0 Flash) to 26,892 ms (DeepSeek V3). The article does not define whether this includes model generation time.
Models generating SQL for financial questions, April 2025
Open every published row
DeepSeek V3 recorded 0.617 for average score in the 2025-04-15 publication. 60 financial questions per the article text, 11 models.
| Published metric | Value |
|---|---|
| Average score | 0.617 |
| Median score | 1.000 |
| Std dev | 0.460 |
| Success rate | 75.0% |
| Avg execution | 26,892 ms |
| Input cost | $0.27/M |
| Output cost | $1.10/M |
No matching rows. Clear the filter to see all records.
Models generating SQL for financial questions, April 2025: read these measurements
View task, sample and measurement units
60 financial questions per the article text, 11 models
Generate SQL queries that correctly answer financial analysis questions.
Scoring
The article reports scores and success rates but refers readers to an earlier article for the methodology. The grader is not described in this article.
Cost units
List prices per million tokens, from $0.10/M input for GPT-4.1 Nano and Gemini 2.0 Flash to $3.00/M input and $15.00/M output for Claude 3.7 Sonnet and Grok-3-Beta. No measured run spend is published.
Time units
Average execution time in milliseconds, from 1,296 ms (Gemini 2.0 Flash) to 38,614 ms (GPT-4.1 Nano).
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