whatmodel.sh / PaLM 2 Chat (chat-bison@001)
PaLM 2 Chat (chat-bison@001)
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PaLM 2 Chat (chat-bison@001) is a closed-weights language model from Google, released May 2023.
Catalogue identity notes
Comparison-only. Arena entry 'palm-2' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'palm-2' as 'PaLM-Chat-Bison-001' (Google, Proprietary), linking https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models#foundation_models. Released 2023-05: Vertex AI retired-models table: chat-bison release date May 2023. LMArena labels 'palm-2' 'PaLM-Chat-Bison-001'; FastChat registry: 'PaLM 2 for Chat (chat-bison@001) by Google'. Vertex AI retired-models table: chat-bison retired April 21, 2025.
$ how it compares
The LLMs ranked either side of PaLM 2 Chat (chat-bison@001), out of 828 with a score, then more from Google.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 706 |  ChatGLM3 6B 128K | 34±4 | local | ||
| 707 |  Zephyr 7B beta | 34±1 | local | ||
| 708 | PaLM 2 Chat (chat-bison@001) | 34±2 | comparison only | ||
| 709 |  Qwen3 0.6B | 33±1 | local | ||
| 710 |  Llama 2 13B Chat | 33±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
chat & writing▁▁▁▃▅▄▆█▇▇▇▃ #429 of 463 · 98% of its score · 3 tests
Beats Gemma 4 E2B, Code Llama 70B Instruct, Phi-2 (2.7B) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 350M 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) | 997.0 ✎ | ▂▁▂▁▃▇█▆▇▄▂▂ beats 15/95 | 34% |
| LMArena (text) | 1139.1 ✎ | ▁▁▁▃▃▃▃▆▆▇█▃ beats 31/379 | 34% |
| MT-Bench (GPT-4 single-answer grading, LMSYS runs) | 6.4000 MT-Bench score (1-10) ✎ | ▆▂▂▆▆▄██▂▄▄▆ beats 16/28 | 30% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · Polyrating: A Cost-Effective… 34%, LMArena leaderboard dataset 34%, LMSYS Chatbot Arena leaderboa… 30%
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 |
|---|---|---|
 Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3) | 1 | 34% |
 LMArena leaderboard dataset | 1 | 34% |
 LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table | 1 | 30% |
| prior · pull toward the middle | 2% | |
| weighted average | = 33.5 |
$ improve this page
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data updated · sources and licences · json