whatmodel.sh / Gemini 1.5 Pro Experimental (0801)
Gemini 1.5 Pro Experimental (0801)
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Gemini 1.5 Pro Experimental (0801) is a closed-weights language model from Google, released August 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: 'Released gemini-1.5-pro-exp-0801, a new experimental version of' Gemini 1.5 Pro (August 2024); experimental model, shut down. Distinct from the stable gemini-1.5-pro-001/-002.
$ how it compares
The LLMs ranked either side of Gemini 1.5 Pro Experimental (0801), out of 828 with a score, then more from Google.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 314 |  Molmo 2 8B | 55±2 | local | ||
| 315 |  Magistral Medium 1.1 (2507) | 55±2 | comparison only | ||
| 316 | Gemini 1.5 Pro Experimental (0801) | 55±1 | comparison only | ||
| 317 |  Qwen3 4B Instruct 2507 | 55±1 | local | ||
| 318 | Qwen2.5 Coder 14B Instruct | 55±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▁▁▁▃▅▇█▇▅▄▃▁ #230 of 524 * · 17% of its score · 1 test
Beats Qwen3.5 4B, Qwen3-VL 32B Instruct, Falcon-H1R 7B here, though they rank higher overall. Trails QwQ-32B-Preview, Ministral 3 3B Reasoning 2512, DeepSeek-Coder-V2 Instruct (0724) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| CRUXEval (ZeroEval, 800 problems) | 74.9% ✎ | ▁▂▆▃▆█▆▃▂·▁▁ beats 36/47 | 17% |
math▁▂▃▅▇▅▅█▄▁▁▁ #276 of 462 * · 5.1% of its score · 1 test
Beats Command A (03-2025), GPT-4o (2024-11-20), GPT-4o (2024-05-13) here, though they rank higher overall. Trails DeepSeek R1 Distill Qwen 1.5B, Tulu 2 DPO 70B, OpenThinker 7B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GSM8K (ZeroEval, zero-shot) | 95.0% ✎ | ▁▄·▁▄█▅▅▅███ beats 35/43 | 5.1% |
other tests3 not in a task · 77% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LiveBench 2024-07-26 | 52.2% ✎ | ▁▁▁▂▂▃▂█▆▃▅▃ beats 55/61 | 34% |
| MMLU-Redux (ZeroEval, 2,778 questions) | 85.5% ✎ | ▁·▁▁▄▄▄▅█▄▂▁ beats 44/53 | 27% |
| ZebraLogic (ZeroEval, 1,000 puzzles) | 25.2% ✎ | ▁▁▃▆██▁▁·▁▁▁ beats 39/57 | 17% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · WildEval ZeroEval published r… 66%, LiveBench model judgments (HF… 34%
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 |
|---|---|---|
 WildEval ZeroEval published results | 4 | 66% |
| LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets | 1 | 34% |
| prior · pull toward the middle | 0% | |
| weighted average | = 55.2 |
$ 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