whatmodel.sh / Gemini 1.5 Flash-8B (001)
Gemini 1.5 Flash-8B (001)
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Gemini 1.5 Flash-8B (001) is a closed-weights language model from Google, released October 2024. Licence: proprietary.
Catalogue identity notes
Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2024-10-03). Stable API version gemini-1.5-flash-8b-001. Gemini API changelog, September 29, 2025: gemini-1.5-flash-8b shut down.
$ how it compares
The LLMs ranked either side of Gemini 1.5 Flash-8B (001), out of 828 with a score, then more from Google.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 452 |  Code Millenials 34B | 48±1 | local | ||
| 453 |  Reka Core (2024-05-01) | 48±1 | comparison only | ||
| 454 | Gemini 1.5 Flash-8B (001) | 48±2 | comparison only | ||
| 455 |  T5 Large | 48±9 | local | ||
| 456 |  WaveCoder-Ultra-6.7B | 48±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
chat & writing▁▁▁▃▅▄▆█▇▇▇▃ #317 of 463 * · 31% of its score · 1 test
Beats Ministral 3 14B Reasoning 2512, Ministral 3 14B, Phi-4 14B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 1.2B, Granite 4.0 H 1B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1258.8 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 95/379 | 31% |
math▁▂▃▅▇▅▅█▄▁▁▁ #320 of 462 * · 7.1% of its score · 1 test
Beats Molmo 2 8B, DeepSeek-Coder-V2 Instruct (0724), Llama 3.1 Nemotron 70B Instruct here, though they rank higher overall. Trails DeepSeek R1 Distill Qwen 1.5B, Granite 8B Code Base (4K), Mixtral 8x7B v0.1 (base) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| OTIS mock AIME 2024–2025 | 4.6% ✎ | ▃▃▄▃▃▃▇▇█▃▂▅ beats 30/193 | 7.1% |
vision & documents▁▁▃▃▃▃▅▄▇▇█▃ #146 of 182 * · 22% of its score · 1 test
Beats DeepSeek V4 Flash Vision (Exp), Gemma 3 12B, Llama 3.2 90B Vision Instruct here, though they rank higher overall. Trails Qwen3.5 2B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Arena vision (style controlled) | 1071.3 ✎ | ▁▁▂▂▂▃▄▅▅▆██ beats 25/145 | 22% |
other tests2 not in a task · 38% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GPQA Diamond | 33.0% ✎ | ▁▁▁▁▃▄▆▄▅▇█▅ beats 22/208 | 31% |
| Open PT-LLM Leaderboard average (9 Portuguese tasks) | 75.4% ✎ | ▃▁▁▁▁▁▁▁▂█▅▁ beats 228/276 | 6.9% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · LMArena leaderboard dataset 53%, Epoch AI — internal evaluatio… 39%, Open Portuguese LLM Leaderboa… 7%
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 |
|---|---|---|
 LMArena leaderboard dataset | 2 | 53% |
 Epoch AI — internal evaluations | 2 | 39% |
 Open Portuguese LLM Leaderboard (archived) | 1 | 7% |
| prior · pull toward the middle | 2% | |
| weighted average | = 48.1 |
$ 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