PaLM 2 vs Gemini: What Changed and Why It Matters in 2026

Google has always been in the AI race. But the distance between PaLM 2 — Google's flagship model of 2023 — and Gemini — its successor that redefined what Google AI could do — is not a small step forward. It is a fundamental architectural rethinking of how Google builds AI systems.

Understanding the difference between PaLM 2 and Gemini matters for two reasons. First, if you are using any Google AI product — Bard, Workspace AI features, Google Search AI Overviews, or the Gemini app — knowing which model powers it and why tells you a great deal about what to expect. Second, the PaLM 2 to Gemini transition illustrates the pace at which frontier AI development is moving — and what it means for every tool built on these foundations.

This comparison covers what PaLM 2 was, what Gemini is, the specific technical and practical differences between them, and what the transition means for users and developers in 2026.

Quick Answer

What is the difference between PaLM 2 and Gemini? PaLM 2 was Google's large language model released in 2023 — a text-focused model with strong multilingual and reasoning capabilities. Gemini is Google's next-generation AI model family released in late 2023 and expanded through 2024 and 2025 — natively multimodal from the ground up, meaning it was trained simultaneously on text, images, audio, video, and code rather than being a text model with added capabilities. Gemini outperforms PaLM 2 on nearly every benchmark and has effectively replaced PaLM 2 across all Google products and services.

What Was PaLM 2?

PaLM 2 — Pathways Language Model 2 — was Google's flagship large language model, announced at Google I/O in May 2023. It succeeded the original PaLM model and represented a significant capability advancement at the time of its release.

PaLM 2 Architecture and Training

PaLM 2 was trained on a dataset of approximately 3.6 trillion tokens — a substantial increase over its predecessor — covering web documents, books, code, mathematics, and scientific papers across more than 100 languages.

The architecture was a transformer-based language model — the same fundamental design that underlies GPT models and most other large language models. PaLM 2 was primarily a text model: it processed text input and generated text output, with multimodal capabilities (handling images) added through additional training rather than being native to the core architecture.

PaLM 2 Model Sizes

Google released PaLM 2 in four sizes designed for different deployment contexts:

Gecko — the smallest and most efficient variant, designed to run on mobile devices and edge hardware where computational resources are constrained.

Otter — a mid-size model balancing capability and efficiency for lighter server-side applications.

Bison — the primary workhorse model, used across most Google products that powered PaLM 2 features in 2023.

Unicorn — the largest and most capable PaLM 2 variant, used for the most demanding tasks requiring maximum reasoning capability.

What PaLM 2 Was Good At

Multilingual capability was PaLM 2's standout strength relative to contemporaries. Trained on text spanning over 100 languages with significant depth in non-English content, PaLM 2 produced genuinely natural multilingual output rather than translated English.

Code generation was strong — PaLM 2 powered GitHub Copilot competitor features and demonstrated competitive performance on coding benchmarks at the time of release.

Reasoning tasks showed improvement over PaLM 1, particularly on multi-step mathematical and logical reasoning problems.

Medical and scientific domains — PaLM 2 was the foundation of Med-PaLM 2, a specialized medical AI model, and showed strong performance on scientific literature tasks.

Where PaLM 2 Fell Short

Text-only architecture. PaLM 2 was fundamentally a text model. Image understanding was added as an extension rather than being native to the model's design — a limitation that affected the depth and quality of multimodal reasoning.

Context window limitations. PaLM 2's context window, while larger than GPT-3.5, did not approach the long-context capability that became a key differentiator in frontier AI competition.

Real-time information access. PaLM 2 lacked built-in web search integration, limiting its usefulness for tasks requiring current information.

Benchmark performance versus competition. By late 2023, GPT-4 had established a clear performance lead over PaLM 2 on several important benchmarks, creating pressure for Google to accelerate its next generation.

Products That Used PaLM 2

• Bard — Google's AI assistant was initially powered by PaLM 2 before transitioning to Gemini
• Google Workspace AI features — Gmail, Docs, Sheets, and Slides AI features used PaLM 2 in their initial releases
• Google Cloud Vertex AI — PaLM 2 was available through Vertex AI for enterprise applications
• Med-PaLM 2 — a specialized fine-tuned version for medical question answering
• Sec-PaLM — a specialized version for cybersecurity applications

What Is Gemini?

Gemini is Google DeepMind's next-generation AI model family — the product of merging Google Brain and DeepMind into a single research organization and directing that combined capability toward building a fundamentally new kind of AI model.

Gemini was announced in December 2023 and has been continuously developed and expanded through 2024 and 2025. In 2026, Gemini 2 represents the current state of the Gemini model family.

The Fundamental Architecture Difference

The most important difference between PaLM 2 and Gemini is not a performance improvement — it is an architectural rethinking.

PaLM 2 was a language model. It was designed and trained to process and generate text, with other modalities (images, code) incorporated as extensions to a text-centric foundation.

Gemini was designed from the beginning as a multimodal model. It was trained simultaneously on text, images, audio, video, and code — not as separate models combined, but as a single unified model that reasons across all of these modalities natively.

This architectural difference is not a technical detail — it has practical consequences for everything Gemini can do that PaLM 2 could not.

When Gemini processes an image, it is not running a separate image model and translating the result into text for the language model to process. It is reasoning about the image using the same neural architecture it uses to reason about text — which produces fundamentally different and more capable multimodal reasoning.

Gemini Model Sizes

Like PaLM 2, Gemini is available in multiple sizes for different deployment contexts:

Gemini Nano — the smallest variant, designed for on-device deployment. Gemini Nano powers AI features on Pixel phones and other Android devices, running entirely on-device without server calls for latency-sensitive applications.

Gemini Flash — a fast, efficient model optimized for high-volume applications requiring quick response times. The free tier of the Gemini app uses Gemini 2 Flash — making it the most widely accessible Gemini model.

Gemini Pro — the standard capable model for complex tasks, balancing performance and efficiency for server-side applications.

Gemini Ultra — the most capable model in the Gemini family, competing directly with GPT-5.5 and Claude Opus at the frontier. Available through Gemini Advanced (Google One AI Premium subscription).

What Makes Gemini Genuinely Different

Native multimodal reasoning. Gemini can process and reason across text, images, audio, and video in a unified way that PaLM 2's extended multimodal capability could not match. Show Gemini a complex data visualization and ask questions about it — the response reflects genuine visual reasoning. Play audio through Gemini and ask for transcription and analysis — it processes the audio natively.

The 1 million token context window. Gemini 1.5 Pro introduced a 1 million token context window — the largest of any commercially available model. This allows processing complete codebases, entire books, hours of video, or extremely long document collections in a single context. PaLM 2 could not approach this.

Real-time Google Search integration. Gemini has live Google Search built into every response — it searches the current web rather than relying solely on training data. PaLM 2 had no native web search capability.

Video understanding. Gemini can process and reason about video content natively — analyzing hours of video, answering questions about specific moments, and synthesizing information across a video's full content. This capability did not exist in PaLM 2.

Google Workspace deep integration. Gemini's integration with Gmail, Docs, Drive, Sheets, and Calendar is significantly deeper than PaLM 2's Workspace features — understanding the full context of your work across Google's ecosystem rather than providing isolated text assistance.

PaLM 2 vs Gemini — Direct Comparison

Architecture

Capabilities

Performance on Key Benchmarks

At the time of their respective releases, the benchmark comparison between PaLM 2 and Gemini Ultra showed Gemini outperforming PaLM 2 across every major category:

MMLU (General knowledge across 57 subjects):

• PaLM 2 Large: 78.3%
• Gemini Ultra: 90.0%

HumanEval (Code generation):

• PaLM 2: 73.9%
• Gemini Ultra: 74.4%

GSM8K (Elementary mathematics):

• PaLM 2: 80.7%
• Gemini Ultra: 94.4%

MATH (Challenging mathematics):

• PaLM 2: 34.3%
• Gemini Ultra: 53.2%

Big-Bench Hard (Reasoning):

• PaLM 2: 78.1%
• Gemini Ultra: 83.6%

The mathematical reasoning improvement is particularly striking — Gemini Ultra more than 50% better than PaLM 2 on challenging mathematics reflects the architectural improvements in reasoning capability.

Practical Performance Differences

Beyond benchmarks, the practical differences that users notice:

Document analysis quality. Gemini's 1 million token context window fundamentally changes what document analysis means — a complete 500-page report can be processed in a single context, questions answered with references to specific sections, inconsistencies identified across the full document. PaLM 2 handled fragments.

Visual reasoning accuracy. Gemini's native image processing produces genuinely different results from PaLM 2's extended multimodal capability. Complex charts, technical diagrams, and multi-element visual content are analyzed with significantly higher accuracy.

Code across complete repositories. The context window difference means Gemini can reason about a complete codebase — understanding how files relate to each other, identifying bugs that span multiple files, and suggesting refactoring with full architectural context. PaLM 2 worked on code snippets.

Mathematical problem solving. The benchmark improvements translate directly to practical performance — complex multi-step problems that PaLM 2 handled unreliably are solved consistently by Gemini.

Why Google Replaced PaLM 2 with Gemini

The replacement was not gradual — it was deliberate and comprehensive. Understanding why clarifies both what was wrong with PaLM 2 and what Gemini was designed to fix.

Competitive Pressure

The most immediate driver was GPT-4. When OpenAI released GPT-4 in March 2023, it established a clear capability lead over PaLM 2 that Google could not close with incremental improvements to the existing architecture. Competing effectively required a fundamentally better model, not a better version of the same model.

The Multimodal Imperative

Google's product roadmap required genuine multimodal capability — not multimodality bolted onto a text model. Google Search needed to process images and video. Google Photos needed to understand and reason about visual content. YouTube needed AI that understood video natively. Building these features on a text-first architecture produced limited, awkward results. Building them on a natively multimodal foundation produced genuinely useful capabilities.

The DeepMind Merger

The merger of Google Brain and DeepMind into Google DeepMind in 2023 brought together two of the world's leading AI research organizations. Gemini is the product of that combined research capability — a model that neither organization could have built independently at the same pace.

Context Window Requirements

Enterprise customers evaluating Google's AI for document processing, legal analysis, code review, and other professional applications needed context windows that PaLM 2 could not provide. The 1 million token context window was not an arbitrary technical achievement — it was a direct response to real enterprise use cases that required processing complete long documents.

Current Status — Where PaLM 2 Stands in 2026

PaLM 2 has been effectively sunset across Google's product ecosystem. All major Google consumer products — the Gemini app, Google Workspace AI features, Google Search AI Overviews — run on Gemini models.

PaLM 2 remains available through Google Cloud Vertex AI for developers with existing implementations who have not yet migrated. Google has maintained API access to preserve backward compatibility for enterprise customers, but new development on Google's AI infrastructure targets Gemini models exclusively.

For any new development, integration, or application — PaLM 2 is not the right starting point. The Gemini API through Google AI Studio or Vertex AI is Google's current and forward-looking AI development platform.

Gemini in 2026 — The Current State

Gemini has continued to develop significantly since its initial release. Gemini 2 — the current generation — represents substantial improvements over the initial Gemini release:

Gemini 2 Flash is the free tier model — unlimited access with no message limits. For most everyday AI assistant use cases, Flash's capability is more than sufficient and the unlimited free access is the best free AI plan available.

Gemini 2 Pro handles complex reasoning, extended document analysis, and professional use cases where the highest capability is required.

Gemini 2 Ultra competes directly with GPT-5.5 and Claude Opus at the frontier — available through the Google One AI Premium subscription at $19.99/month.

For a detailed comparison of how Gemini 2 stacks up against GPT-5.5, read the complete Gemini 2 vs GPT-5.5 comparison.

Who Should Care About This Comparison?

Developers Who Built on PaLM 2

If you integrated PaLM 2 through the Google AI or Vertex AI APIs, the migration path to Gemini is well-documented and the capability improvements justify the migration effort. Google provides migration guides and the API interfaces are sufficiently similar that the transition is manageable.

The practical reasons to migrate: significantly better output quality, native multimodal support, longer context windows, and continued development — PaLM 2 is not receiving capability updates.

Researchers Evaluating Google AI Models

Understanding the architectural differences between PaLM 2 and Gemini is important context for evaluating Google's AI research trajectory. The PaLM 2 to Gemini transition demonstrates Google's ability to execute a fundamental architecture change at scale — not just incremental model improvement.

AI Enthusiasts and Learners

The PaLM 2 to Gemini transition is a textbook case study in how frontier AI development works in practice — the competitive dynamics, the architectural decisions, the product implications of different model designs. Understanding this transition builds genuine AI literacy about how these systems actually evolve.

Business Users Evaluating Google AI Products

If you are evaluating Google Workspace AI features, Google Cloud AI services, or the Gemini app for business use, understanding that these products now run on Gemini — not PaLM 2 — clarifies the capability baseline you are evaluating and confirms that the current generation is significantly more capable than what Google shipped in early to mid 2023.

How Gemini Compares to Other AI Models in 2026

PaLM 2's primary competition was GPT-4 and early Claude versions. Gemini's competition is GPT-5.5 and Claude Opus — a fundamentally different competitive landscape.

For the most complete current comparison:

• Gemini 2 vs GPT-5.5 complete comparison
• ChatGPT vs Claude vs Gemini full comparison
• GPT-5.5 vs Claude Opus detailed comparison

The short version: Gemini 2 leads on multimodal tasks, long context processing, real-time information, and free plan generosity. GPT-5.5 leads on writing quality, coding, and ecosystem breadth. Claude Opus leads on instruction following, document analysis, and creative writing quality.

Common Questions About PaLM 2 and Gemini

Is PaLM 2 still available? PaLM 2 remains available through Google Cloud Vertex AI for developers with existing implementations. It is not receiving new capability updates and is not recommended for new development. All new Google AI development targets Gemini models.

What happened to Bard? Bard — Google's AI assistant originally powered by PaLM 2 — was renamed Gemini in February 2024. The current Gemini app uses Gemini model technology, not PaLM 2. The renaming reflected both the model transition and a broader repositioning of Google's AI brand.

Is Gemini built on PaLM 2? No. Gemini is a separate architecture developed from the ground up as a natively multimodal model. It is not an updated version of PaLM 2. The two models share the transformer architecture common to all large language models but differ fundamentally in their training approach and design objectives.

Which is better — PaLM 2 or Gemini? Gemini is significantly more capable than PaLM 2 across virtually every benchmark and practical use case. Gemini outperforms PaLM 2 on text quality, multimodal reasoning, mathematics, code, and context length. There is no use case where PaLM 2 is the better choice for new development or applications.

Can I access PaLM 2 through the Gemini API? No. The Gemini API provides access to Gemini models. PaLM 2 is accessible through the legacy PaLM API on Google Cloud, but Google's current AI development infrastructure is built around Gemini. New developers should use the Gemini API.

What replaced PaLM 2 in Google products? All Google consumer AI products — Gemini app, Google Workspace AI features, Google Search AI Overviews — now run on Gemini models. Gemini Flash powers the free tier. Gemini Pro and Ultra power advanced features.

Does Gemini support all the languages PaLM 2 did? PaLM 2 supported over 100 languages. Gemini officially supports 40+ languages but with significantly higher quality per language than PaLM 2's broader but shallower multilingual capability. For the most important global languages, Gemini produces more natural output despite the narrower official language list.

Key Takeaways

• PaLM 2 was Google's flagship language model of 2023 — a text-first model with extended multimodal capability, strong multilingual performance, and competitive reasoning
• Gemini is Google's next-generation natively multimodal model — trained simultaneously on text, images, audio, video, and code rather than building multimodality on top of a text architecture
• Gemini outperforms PaLM 2 on every major benchmark — with particularly large improvements in mathematical reasoning, long document processing, and multimodal tasks
• The most significant practical differences are Gemini's 1 million token context window, native video understanding, real-time Google Search integration, and deep Google Workspace integration
• PaLM 2 has been effectively replaced across all Google products — Bard became the Gemini app, all Workspace AI features moved to Gemini, and Google Cloud now positions Gemini as its primary AI offering
• For any new development or evaluation of Google AI capabilities — Gemini is the current and relevant model, not PaLM 2

Conclusion

The PaLM 2 to Gemini transition represents something more significant than a model upgrade. It represents Google's answer to a fundamental question about how AI systems should be built — as text models extended to handle other modalities, or as natively multimodal systems that reason across all forms of information from the ground up.

The answer Gemini represents — native multimodality, massive context windows, real-time information access, and deep product integration — has proven significantly more capable and versatile than the text-first architecture PaLM 2 represented.

For users, the implication is straightforward. The Google AI products you use in 2026 are substantially more capable than what Google offered in 2023 — and that improvement is not incremental. It is architectural.

For developers and researchers, the lesson is equally clear. AI development at the frontier is not a linear progression of the same technology. It involves fundamental architectural rethinking when competitive dynamics and product requirements demand it. Understanding those architectural decisions — not just the benchmark numbers — is what builds genuine AI literacy.

For more on Google's current AI capabilities and how Gemini compares to other frontier models, read the Gemini 2 vs GPT-5.5 complete comparison, the ChatGPT vs Claude vs Gemini guide, the complete AI SEO guide, and the best free AI tools overview for a complete picture of the current AI landscape.