whatmodel.sh / Gemini 1.0 Pro (001)
Gemini 1.0 Pro (001)
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Gemini 1.0 Pro (001) is a closed-weights language model from Google, released December 2023. Licence: proprietary.
Catalogue identity notes
Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2023-12-13). API model gemini-1.0-pro-001. Not listed on the current Gemini API models or deprecations pages (observed 2026-09-28).
$ how it compares
The LLMs ranked either side of Gemini 1.0 Pro (001), out of 828 with a score, then more from Google.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 510 |  Granite 34B Code Instruct (8K) | 46±1 | local | ||
| 511 |  WizardCoder Python 34B V1.0 | 46±1 | local | ||
| 512 | Gemini 1.0 Pro (001) | 46±2 | comparison only | ||
| 513 |  Mistral Medium (2312) | 46±1 | comparison only | ||
| 514 |  GPT-3.5 Turbo (0613) | 46±1 | comparison only | ||
| 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▁▁▁▃▅▄▆█▇▇▇▃ #336 of 463 * · 43% of its score · 2 tests
Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, Ministral 3 8B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024) | 1182.0 ✎ | ▂▁▂▁▃▇█▆▇▄▂▂ beats 77/95 | 22% |
| LMArena (text) | 1236.8 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 87/379 | 21% |
math▁▂▃▅▇▅▅█▄▁▁▁ #407 of 462 * · 34% of its score · 2 tests
Trails Qwen3 0.6B, Tulu 2 DPO 7B, Gemma 7B (base) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| MATH level 5 | 11.2% ✎ | ▂▂▅▆▄█▆▃▅▂▃▄ beats 7/92 | 31% |
| OTIS mock AIME 2024–2025 | 1.1% ✎ | ▃▃▄▃▃▃▇▇█▃▂▅ beats 9/193 | 3.1% |
other tests2 not in a task · 22% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GPQA Diamond | 34.0% ✎ | ▁▁▁▁▃▄▆▄▅▇█▅ beats 26/208 | 17% |
| Open PT-LLM Leaderboard average (9 Portuguese tasks) | 69.9% ✎ | ▃▁▁▁▁▁▁▁▂█▅▁ beats 163/276 | 4.8% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · Epoch AI — internal evaluatio… 51%, Polyrating: A Cost-Effective… 22%, LMArena leaderboard dataset 21% and 1 more
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 |
|---|---|---|
 Epoch AI — internal evaluations | 3 | 51% |
 Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3) | 1 | 22% |
 LMArena leaderboard dataset | 1 | 21% |
 Open Portuguese LLM Leaderboard (archived) | 1 | 5% |
| prior · pull toward the middle | 1% | |
| weighted average | = 45.7 |
$ 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