whatmodel.sh / Gemini 1.5 Flash-8B Experimental (0924)
Gemini 1.5 Flash-8B Experimental (0924)
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Gemini 1.5 Flash-8B Experimental (0924) is a closed-weights language model from Google, released September 2024. Licence: proprietary.
Catalogue identity notes
Comparison-only: retired or no longer listed API model, kept so its independent runs can be scored. Gemini API changelog, September 24, 2024: 'Released gemini-1.5-flash-8b-exp-0924 to replace gemini-1.5-flash-8b-exp-0827'; experimental, shut down.
$ how it compares
The LLMs ranked either side of Gemini 1.5 Flash-8B Experimental (0924), out of 1132 with a score, then more from Google.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 754 |  FuseChat-Llama-3.2-1B-Instruct | 44±2 | local | ||
| 755 |  LLaVA-OneVision Qwen2 72B (ov-sft) | 44±3 | local | ||
| 756 | Gemini 1.5 Flash-8B Experimental (0924) | 44±2 | comparison only | ||
| 757 |  Phi-3-mini-4k-instruct (June 2024 update) | 44±1 | local | ||
| 758 |  Yi 1.5 9B Chat | 44±1 | local | ||
| more from Google | |||||
| 9 | Gemini 4 Argon (limited preview) | 82±1 | limited preview | ||
| 23 | Gemini 3.8 Flash | 77±1 | api $1.50/M | ||
| 32 | Gemini 3.7 Flash | 76±1 | api $1.50/M | ||
$ what it's good at
coding▁▁▁▃▅▇█▇▅▄▂▁ #409 of 549 * · 97% of its score · 1 test
Beats GLM-4.7-Flash, LFM2.5 2.6B, LFM2.5 8B-A1B here, though they rank higher overall. Trails Phi-3-mini-128k-instruct (Apr 2024), DeepSeek LLM 67B Chat, WizardLM 70B V1.0 here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Aider code editing (133 Exercism Python exercises, two attempts) | 38.3% ✎ | ▁·▁▂▄▁▆▄█▆▂▂ beats 7/62 | 97% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · Aider code editing benchmark… 97%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 Aider code editing benchmark runs (old edit leaderboard) | 1 | 97% |
| prior · pull toward the middle | 3% | |
| weighted average | = 44.3 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json