gemini-1.5-flash-002 (0924): benchmark scores, price and ranking

gemini-1.5-flash-002 (0924) from Google ranks #353 of 743 LLMs on the LLMs Tiger aggregate benchmark score (48.4), based on 1 benchmark results captured up to 2026-10-13.

Organisation: Google
Type: Fast / low-cost (flash) model
Open weights: No
Aggregate rank: #353 of 743 (score 48.4)

Across the 1 benchmark where at least ten models are scored, gemini-1.5-flash-002 (0924) is in the top 10% on 0 and in the top half on 0.

gemini-1.5-flash-002 (0924) benchmark results

gemini-1.5-flash-002 (0924) benchmark scores, ranked against all models measured on each benchmark
Benchmark	Score	Rank	Source	Captured
Aider Code Editing	51.1%	#57 of 92	Aider Aider Code Editing leaderboard	2026-10-13

About gemini-1.5-flash-002 (0924)

gemini-1.5-flash-002 (0924) is a smaller, lower-cost model from Google. No API price is listed for it yet. We hold 1 benchmark result for it across 1 area, and none is in the top tenth of its board.

Where gemini-1.5-flash-002 (0924) stands

On our aggregate score, gemini-1.5-flash-002 (0924) is #353 of 743 ranked models, around the middle of the table. Its score is 48.4; the scale is centred on 50, so it sits 1.6 points below the typical model. That beats 52% of the ranked field. Among the 29 ranked Google models it is #18. The next one up is gemini-1.5-flash-exp-0827 at 48.7. The next one down is Gemini 3.5 Flash-Lite (High) at 48.0.

gemini-1.5-flash-002 (0924), benchmark by benchmark

gemini-1.5-flash-002 (0924) lands below the middle on Aider Code Editing: 51.1%, #57 of 92, with 61% of the field scoring higher. Claude 3.5-Sonnet-20241022 leads at 84.2%; the closest result above is codestral-2405 at 51.1%. It covers Aider official benchmark. The board median is 55.6%, so gemini-1.5-flash-002 (0924) is 4.5 points below it. Source: Aider Aider Code Editing leaderboard, captured 2026-10-13.

Reading these numbers

The 1 score on this page comes from 1 source: Aider Aider Code Editing leaderboard (1). 1 of them come from an official leaderboard and 0 from datasets or third-party runs. With 1 results the aggregate rank can move a lot when one new score arrives.

Models ranked nearby

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