PaLM 2 by Google 
PaLM 2 is a historical Google large language model family introduced for multilingual, reasoning, coding, and generative AI tasks and later superseded by newer Google model families.
Starting price Historical model / API pricing varies
Tool Information
PaLM 2 is a historical Google large language model family introduced for multilingual, reasoning, coding, and generative AI tasks and later superseded by newer Google model families.
Developers should use the official historical documentation for context, identify the currently supported replacement model, review its current API terms, test behavior, and migrate rather than starting a new PaLM 2 deployment.
There is no current standalone PaLM 2 subscription to recommend; historical API usage and supported successor models use separate cloud pricing.
The model may be deprecated, unavailable, or unsuitable for new systems. Language models can hallucinate, leak data, or generate unsafe output and require migration planning and safeguards.
F.A.Q (3)
Pros and Cons
Pros
- PaLM 2 was designed as a stronger multilingual language-model family than Google's first PaLM
- Its training emphasized more than one hundred human languages
- The model improved translation; idiom; riddle; and nuanced-language capabilities for its generation
- Scientific and mathematical material in training strengthened reasoning over the prior family
- Source-code training covered widely used languages such as Python and JavaScript
- It also included specialist languages such as Prolog; Fortran; and Verilog
- PaLM 2 powered Google's Bard assistant before the transition to Gemini
- Google Workspace generative features used the model under the former Duet AI branding
- Sec-PaLM supported cybersecurity analysis experiments
- Med-PaLM 2 specialized the underlying research for medical question answering
- Different sizes called Gecko; Otter; Bison; and Unicorn targeted varied deployment needs
- The smallest Gecko variant was designed for mobile use and offline operation
- Vertex AI exposed text-bison and chat-bison models through managed APIs
- Generative AI Studio allowed developers to test prompts without first building a complete application
- Tuning support enabled task adaptation on Vertex AI
- Google published a detailed technical report and responsible-AI evaluation results
Cons
- PaLM 2 is a legacy model family rather than Google's current foundation-model offering
- Bard and Duet AI branding moved to Gemini; so old product descriptions are obsolete
- PaLM-specific text-bison and chat-bison integrations require migration to supported Gemini models
- A general Google blog URL is not a usable endpoint for running the model
- Historical benchmarks should not be compared directly with current models without matching tests
- The technical report documents a research generation rather than a consumer subscription
- Model size names do not reveal exact parameter counts
- Multilingual coverage can still vary substantially in quality between languages
- Code generation can introduce vulnerabilities; outdated APIs; or license concerns
- Medical variants were research systems and do not replace qualified clinical judgment
- Cybersecurity output can miss threats or suggest unsafe actions
- Reasoning responses can be confidently wrong despite improved benchmarks
- Training data can reproduce social bias; stereotypes; or copyrighted expression
- Cloud use requires attention to data governance; regional processing; and access controls
- Old tutorials may reference retired SDK classes; endpoints; quotas; and prices
- New projects should choose a currently supported Gemini model and validate the migration rather than adopting PaLM 2
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Overview
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