TL;DR
Gemini 2.0 Flash (google/gemini-2.0-flash-001) appears in 4 published studies. 2025-02-05, Gemini 2.0 Flash, DeepSeek R1 and o3-mini writing financial SQL: 1/1 for q1: rddt vs spy return correlation. 2025-07-11, Models generating financial SQL queries, July 2025: 0.567 for median score. 2025-04-10, Seven models generating financial SQL queries: 0.717 for average score. 2025-04-15, Models generating SQL for financial questions, April 2025: 0.717 for average score. Each result belongs to its own task, date and grading method.
- Exact tested ID
- google/gemini-2.0-flash-001
- Published studies
- 4
- Provider
Inspect this exact model
Gemini 2.0 Flash, DeepSeek R1 and o3-mini writing financial SQL
Gemini 2.0 Flash: source evidence dated 2025-07-11.
- Q1: RDDT vs SPY return correlation
- 1/1
- Q2: biotech revenue growth
- 1/1
- Input price (per 1M tokens)
- $0.10
- Output price (per 1M tokens)
- $0.40
The displayed values belong to the identified model versions and this published task, with their original metric units.
How did this version perform on the job I need?
The first measured row below belongs to this model, not its provider's other models. Open that study to see the other arms and tested input.
2025-02-05: Gemini 2.0 Flash, DeepSeek R1 and o3-mini writing financial SQLSelect a model for the specific tested task. A profile with one study does not establish performance on other jobs.
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Select a model for the specific tested task. A profile with one study does not establish performance on other jobs.
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Gemini 2.0 Flash, DeepSeek R1 and o3-mini writing financial SQL
See the complete gemini 2.0 flash, deepseek r1 and o3-mini writing financial sql
Gemini 2.0 Flash recorded 1/1 for q1: rddt vs spy return correlation in the 2025-02-05 publication. 2 natural-language financial questions, 3 models, one attempt each.
| Published metric | Value |
|---|---|
| Q1: RDDT vs SPY return correlation | 1/1 |
| Q2: biotech revenue growth | 1/1 |
| Input price (per 1M tokens) | $0.10 |
| Output price (per 1M tokens) | $0.40 |
No matching rows. Clear the filter to see all records.
Gemini 2.0 Flash, DeepSeek R1 and o3-mini writing financial SQL: read these measurements
View task, sample and measurement units
2 natural-language financial questions, 3 models, one attempt each
Generate a BigQuery SQL query that answers a natural-language financial question over the NexusTrade stock dataset.
Scoring
Author-assigned per-question scores. On question 1, R1 misspelled adjustedClosingPrice and o3-mini used the ticker REDDIT, so both received 0.7 after a manual fix. On question 2, R1's query failed to execute (0/1), and o3-mini-high graded both the Gemini and o3-mini queries 9/10, which the author recorded as full credit.
Cost units
OpenRouter list prices per million tokens as shown in the article: Gemini Flash 2.0 $0.1 input and $0.4 output. DeepSeek R1 $0.75 input and $2.4 output. O3-mini $1.1 input and $4.4 output. No per-run cost is published.
Time units
The article publishes no per-model timing. It describes Gemini as responding in a couple of seconds, R1 as taking more than 30 seconds on question 1, and o3-mini as taking a few seconds.
Models generating financial SQL queries, July 2025
Open every published row
Gemini 2.0 Flash recorded 0.567 for median score in the 2025-07-11 publication. About 88 financial questions per the article text, 11 models.
| Published metric | Value |
|---|---|
| Median score | 0.567 |
| Avg score | 0.525 |
| Success rate | 74.16% |
| Avg time | 31,140 ms |
| Input cost | $0.10/M |
| Output cost | $0.40/M |
No matching rows. Clear the filter to see all records.
Models generating financial SQL queries, July 2025: read these measurements
View task, sample and measurement units
About 88 financial questions per the article text, 11 models
Generate SQL queries that answer financial questions against a financial database.
Scoring
Three LLM judges assign a 0 to 1 quality score that is separate from whether the query executes. The article states the score measures query and answer assessment, not investment returns.
Cost units
List prices per million tokens, from $0.10/M input and $0.40/M output (Gemini 2.0 Flash) to $15.00/M input and $75.00/M output (Claude Opus 4). Total run spend was reported as over $200, with $171.88 on Requesty.
Time units
Average time in milliseconds, from 31,140 ms (Gemini 2.0 Flash) to 74,812 ms (Grok-4).
Seven models generating financial SQL queries
Open every published row
Gemini 2.0 Flash recorded 0.717 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 | 0.717 |
| Median score | 1.000 |
| Std deviation | 0.392 |
| Success rate | 92.5% |
| Avg execution time (ms) | 1,296 |
| Perfect score (1.0) share | 55.0% |
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
Gemini 2.0 Flash recorded 0.717 for average score in the 2025-04-15 publication. 60 financial questions per the article text, 11 models.
| Published metric | Value |
|---|---|
| Average score | 0.717 |
| Median score | 1.000 |
| Std dev | 0.392 |
| Success rate | 92.5% |
| Avg execution | 1,296 ms |
| Input cost | $0.10/M |
| Output cost | $0.40/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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