whatmodel.sh / GPT-5 Chat (Arena 2026-10-02)
GPT-5 Chat (Arena 2026-10-02)
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GPT-5 Chat (Arena 2026-10-02) is a closed-weights language model from OpenAI. Licence: proprietary.
Catalogue identity notes
Exact Arena entry 'gpt-5-chat' in the October 2026 pinned dataset. Comparison identity; no public run route inferred from leaderboard inclusion.
$ how it compares
The LLMs ranked either side of GPT-5 Chat (Arena 2026-10-02), out of 1132 with a score, then more from OpenAI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 125 |  GLM-5V-Turbo | 69±2 | api $1.90/M | ||
| 126 | GPT-5.2-Codex | 69±2 | comparison only | ||
| 127 | GPT-5 Chat (Arena 2026-10-02) | 69±3 | comparison only | ||
| 128 |  LongCat-Flash-Thinking (560B-A27B) | 69±1 | comparison only | ||
| 129 |  ERNIE 5.0 (0110) | 68±3 | comparison only | ||
| 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▁▁▁▃▅▅▆█▆▆▆▂ #101 of 579 * · 57% of its score · 1 test
Beats LongCat 2.0 (1.6T-A48B), Inkling Small (266B-A12B), Muse Glimmer 30B here, though they rank higher overall. Trails Gemma 4 12B, Gemma 4 26B-A4B, Mistral Medium 3.5 (128B) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1426.7 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 281/379 | 57% |
vision & documents▁▁▃▃▂▃▅▅▇▇█▃ #73 of 199 * · 40% of its score · 1 test
Beats DeepSeek V4 Flash Vision (Exp), GPT-5, Inkling Small (266B-A12B) here, though they rank higher overall. Trails Gemma 4 26B-A4B, Claude Haiku 4.5, Qwen3.6 Flash here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Arena vision (style controlled) | 1225.7 ✎ | ▁▁▂▂▂▂▄▅▅▆██ beats 86/145 | 40% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · LMArena leaderboard dataset 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
| source | results | share of the score |
|---|---|---|
 LMArena leaderboard dataset | 2 | 97% |
| prior · pull toward the middle | 3% | |
| weighted average | = 68.5 |
$ 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