Google Gemini 2.0 Flash - solid API performance, great quality, and cheaper than GPT-4-mini. The new workhorse?
Published: February 23, 2025
LLM usage hierarchy for my day-to-day live use cases
- gpt-4o-mini - default choice, a workhorse with solid pricing
- gpt-4o - when mini struggles or for complex cases
- claude-3.5-sonnet - when gpt-4o struggles
Point to note: gpt-4o and sonnet API pricing is approx. 15x mini.
And now: gemini-2.0-flash - getting excellent results in early testing for my use cases. Earlier Gemini models had higher API call failures and issues with structured output, but that seems to have changed. Stress testing now. If Gemini's API reliability and structured output consistency hold up, I may move to gemini-2.0-flash as primary and start migrating clients.
Typical use cases
Requiring LLM API calls or agent setups:
- Automation
- Web scraping
- Structured outputs
- OCR
- Database-connected micro-apps
As an aside, coding is separate:
- JavaScript and React micro-apps: Cursor
- Python work: Colab, now moving to Mito-AI for its co-pilot and Cursor-like experience
What's new with gemini-2.0-flash?
Production release: first week of Feb. Based on initial live testing with my typical use cases:
- Quality: equal to or better than gpt-4o
- Reasoning: better than o3-mini, comparable to deepseek-r1
- Cost: generous free tier and paid pricing lower than gpt-4o-mini
API pricing
| Model | USD per M input tokens | USD per M output tokens | Free tier |
|---|---|---|---|
| gpt-4o-mini | 0.15 | 0.60 | none |
| gpt-4o | 2.5 | 10 | none |
| claude-3.5-sonnet | 3 | 15 | none |
| gemini-2.0-flash | 0.10 | 0.40 | 15 req/min, 1M tokens/min, 1500 req/day |
Flash 2.0 is now priced below mini.
Want to test yourself?
Try my open-source AI analytics apps, no API key needed, live on rex.tigzig.com. Compare between LLMs. Source code available on the site in the Help and Build sections.
1. Multi (sequential) agentic app for advanced analytics
Compare reasoning between o3-mini, flash-2.0, and deepseek-r1. Sample files available on the site. Temporary Postgres databases created on the fly, no login required. Tigzig Analyzer
2. AI-enabled mutual fund portfolio processor
Compare file schema identification between gpt-4o and other LLMs with live runs. Use your choice of mutual fund monthly file or a sample file from Google Drive (link in Help section). Tigzig MF Portfolio Processor