whatmodel.sh / Gemini 2.5 Pro Preview (06-05)
Gemini 2.5 Pro Preview (06-05)
↗ share
+ contribute
Gemini 2.5 Pro Preview (06-05) is a closed-weights language model from Google, released June 2025. Licence: proprietary.
Catalogue identity notes
Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2025-06-05). Preview API model gemini-2.5-pro-preview-06-05. Gemini API deprecations page: shutdown date December 2, 2025.
$ how it compares
The LLMs ranked either side of Gemini 2.5 Pro Preview (06-05), out of 828 with a score, then more from Google.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 84 |  MiniMax-M3 | 70±1 | api $0.48/M +local | ||
| 85 |  Grok 4.3 | 70±1 | api $1.56/M | ||
| 86 | Gemini 2.5 Pro Preview (06-05) | 70±2 | comparison only | ||
| 87 |  o3-pro | 70±2 | api $35.00/M | ||
| 88 |  KAT-Coder-V2.5-Dev (35B-A3B) | 70±2 | 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▁▁▁▃▅▇█▇▅▄▃▁ #86 of 524 · 60% of its score · 3 tests
Beats Grok 4.20 (0309 reasoning), GPT-5.4 mini, GPT-5.1 here, though they rank higher overall. Trails Llama 3.1 Nemotron Ultra 253B v1, GLM-4.7, Gemini 3.5 Flash-Lite here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Aider polyglot (225 exercises, two attempts) | 83.1% ✎ | ▂·▂▃▅▂▃█▆▄▂▃ beats 47/49 | 39% |
| LiveCodeBench official (Aug 1, 2024–May 1, 2025) | 73.6% ✎ | ▃▃▅▃··▃▅█▅▅▃ beats 13/15 | 13% |
| WebApp1K-Duo-React pass@1 (onekq-ai) | 70.4% ✎ | ▁··▁·▁▁▁▆█▇█ beats 31/45 | 8.2% |
other tests3 not in a task · 39% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GPQA Diamond | 84.8% ✎ | ▁▁▁▁▂▄▆▄▅▇█▅ beats 145/208 | 25% |
| EQ-Bench 3 (emotional intelligence, judge Elo) | 1304.7 ✎ | ▁·▂▁▄▅▅▃█▄▁▁ beats 47/62 | 13% |
| Vectara hallucination leaderboard, 2025 dataset (HHEM-2.3, factual consistency rate) | 97.4% ✎ | ▁▁▂▃▄▄█▆▃▂▂▁ beats 113/140 | 0.5% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · Aider polyglot benchmark runs 39%, Epoch AI — internal evaluatio… 25%, LiveCodeBench official websit… 13% and 3 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 |
|---|---|---|
 Aider polyglot benchmark runs | 1 | 39% |
 Epoch AI — internal evaluations | 1 | 25% |
| LiveCodeBench official website results (2025-08-01) | 1 | 13% |
 EQ-Bench 3 canonical leaderboard results | 1 | 13% |
| WebApp1K models leaderboard (onekq-ai) | 1 | 8% |
| Vectara hallucination leaderboard (vectara/hallucination-leaderboard repository) | 1 | 1% |
| prior · pull toward the middle | 1% | |
| weighted average | = 70.2 |
$ 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