whatmodel.sh / GPT-3.5 Turbo 16K (0613)
GPT-3.5 Turbo 16K (0613)
↗ share
+ contribute
GPT-3.5 Turbo 16K (0613) is a closed-weights language model from OpenAI, released June 2023. Licence: proprietary.
Catalogue identity notes
Comparison-only: retired or no longer listed API model, kept so its independent runs can be scored. Dated OpenAI API snapshot gpt-3.5-turbo-16k-0613 (16K-context variant of the June 2023 GPT-3.5 Turbo). OpenAI deprecations page: deprecated with the November 6, 2023 GPT-3.5 Turbo update, shutdown date 2024-09-13. Distinct from the catalogued gpt-3.5-turbo-0613/1106/0125 snapshots.
$ how it compares
The LLMs ranked either side of GPT-3.5 Turbo 16K (0613), out of 1132 with a score, then more from OpenAI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 791 |  Phi-3.5-mini-instruct | 43±1 | local | ||
| 792 |  Hermes 2 Theta Llama 3 8B | 43±2 | local | ||
| 793 | GPT-3.5 Turbo 16K (0613) | 43±2 | comparison only | ||
| 794 |  Yi 34B Chat | 43±1 | local | ||
| 795 | internlm2-chat-7b-ExPO | 43±2 | local | ||
| more from OpenAI | |||||
| 1 | GPT-6 Astra | 87±1 | api $20.00/M | ||
| 3 | GPT-6.1 Sol | 85±1 | api $4.00/M | ||
| 7 | GPT-6 Sol | 83±1 | api $4.00/M | ||
$ what it's good at
chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #448 of 579 * · 99% of its score · 2 tests
Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails LFM2 350M, Gemma 3 1B, Flan-T5 XXL here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| AlpacaEval 2 length-controlled | 22.7% ✎ | ▁▂▂▃▆▆█▆▄▄▁▁ beats 59/122 | 92% |
| AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge) | 93.4% ✎ | ▂▂·▆·▇▃▆█▆▄▂ beats 37/45 | 6.1% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 99%
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 |
|---|---|---|
 AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository) | 2 | 99% |
| prior · pull toward the middle | 1% | |
| weighted average | = 43.3 |
$ 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