whatmodel.sh / Gemini Experimental 1114

Gemini Experimental 1114

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Gemini Experimental 1114 is a closed-weights language model from Google, released November 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, November 14, 2024: 'Released gemini-exp-1114, a powerful experimental Gemini API model'; experimental, shut down.

52.6±2.4comparison only: no current run option verifiedfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Gemini Experimental 1114, out of 1132 with a score, then more from Google.

#modelscorewhereRAMreleased
496Qwen3-VL 4B Instruct Alibaba53±1local~4.1GB2025-10
497Hammer2.1-7b MadeAgents53±4local~5.9GB2024-12
498Gemini Experimental 1114 Google53±2comparison only2024-11
499Apertus 1.5 70B Swiss AI53±7local~153GB2026-07
500Claude 3 Opus Anthropic53±0comparison only2024-02
more from Google
9Gemini 4 Argon (limited preview) Google82±1limited preview2026-09
23Gemini 3.8 Flash Google77±1api $1.50/M2026-09
32Gemini 3.7 Flash Google76±1api $1.50/M2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▅▇█▇▅▄▂▁ #289 of 549 * · 97% of its score · 1 test

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails QwQ-32B-Preview, InternLM2.5 20B Chat, Gemma 2 27B IT here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Aider code editing (133 Exercism Python exercises, two attempts)60.9% ✎▁·▁▂▄▁▆▄█▆▂▂ beats 31/62medium · avg 54%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%at its level

$ 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

sourceresultsshare of the scorelicence
Aider code editing benchmark runs (old edit leaderboard)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%Apache-2.0
prior · pull toward the middle▇ 3%
weighted average= 52.6

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Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

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