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

Quick Answer: PaLM 2 vs Gemini

If you're short on time: PaLM 2 is Google's previous generation LLM (2023), while Gemini is the current generation (2023–present). Google has fully transitioned to Gemini, with PaLM 2 being deprecated. For any new project in 2026, you should be using Gemini.

What Is PaLM 2?

PaLM 2 (Pathways Language Model 2) was Google's flagship LLM released at Google I/O 2023. It came in four sizes: Gecko, Otter, Bison, and Unicorn, and powered Google products like Bard (now Gemini) and various Workspace features.

PaLM 2 was trained on a mixture of multilingual text, code, math, and scientific papers, making it stronger at reasoning and coding than its predecessor. At the time, it competed directly with GPT-4.

PaLM 2 Key Strengths (2023)

• Strong multilingual performance (100+ languages)
• Good at coding and formal reasoning
• Available in multiple sizes for different latency/cost tradeoffs
• Integrated into Google Cloud Vertex AI

PaLM 2 Limitations

• Primarily text-only (limited multimodal capability)
• 32K token context window — small by today's standards
• Now deprecated; Google has ended active development
• Cannot use in new Google AI Studio projects

What Is Gemini?

Gemini is Google DeepMind's next-generation multimodal AI model family, launched in December 2023. It was built from the ground up to be natively multimodal — meaning it can process text, images, video, audio, and code in a unified architecture, rather than having modalities bolted on.

As of 2026, the Gemini family includes:

• Gemini 2.5 Pro — Top-tier reasoning model with 1M token context and Deep Think mode
• Gemini 2.5 Flash — Fast and cost-efficient for high-volume use cases
• Gemini 2.0 Flash — Previous generation, still widely used
• Gemini Nano — On-device model for Android and Pixel

PaLM 2 vs Gemini: Key Differences Explained

1. Multimodal Architecture

This is the biggest architectural difference. PaLM 2 was a text-first model. While Google added some image understanding capabilities later, it was not natively multimodal.

Gemini was designed from day one to process text, images, video, audio, and code simultaneously. This makes it fundamentally more capable for AI agent applications that need to perceive the world beyond just text.

2. Context Window

PaLM 2 topped out at 32K tokens. Gemini 1.5 Pro expanded this to 1 million tokens, and Gemini 2.5 Pro maintains this massive context. This is critical for AI agents that need to process long documents, entire codebases, or extended conversations.

3. Reasoning and Coding Performance

Gemini 2.5 Pro introduced a "thinking" mode (extended reasoning chains, similar to OpenAI's o1/o3 series). This dramatically improves performance on complex math, coding, and multi-step reasoning tasks—areas where PaLM 2 struggled against GPT-4.

4. API and Ecosystem

PaLM 2 was accessible via the PaLM API (now deprecated) and Vertex AI. Gemini is available via Google AI Studio (free tier included), the Gemini API, and Vertex AI for enterprise. The developer experience with Gemini is significantly better, with function calling, structured outputs, and grounding built in.

5. Pricing

PaLM 2 pricing is no longer relevant (deprecated). Current Gemini 2.5 Flash pricing is competitive:

• Gemini 2.5 Flash: ~$0.075/M input tokens, ~$0.30/M output tokens
• Gemini 2.5 Pro: ~$1.25/M input tokens (under 200K), ~$10/M output tokens
• Free tier: Available via Google AI Studio with rate limits

Should You Migrate from PaLM 2 to Gemini?

Yes — and you don't have a choice. Google has deprecated the PaLM 2 API and is actively migrating all Vertex AI users to Gemini. If you're still running production workloads on PaLM 2, migrate now before forced deprecation causes downtime.

Migration Guide (PaLM 2 → Gemini)

1. Update your SDK: Replace google-cloud-aiplatform PaLM calls with the google-generativeai Python SDK or Vertex AI Gemini endpoint
2. Remap model names: text-bison → gemini-2.0-flash, chat-bison → gemini-2.5-flash
3. Update prompt format: Gemini uses a contents array with role and parts instead of PaLM's prompt field
4. Test outputs: Gemini's responses may be more verbose or structured differently—run regression tests
5. Leverage new features: Add grounding, function calling, or multimodal inputs now that you're on Gemini

Gemini for AI Agents in 2026

One of Gemini's strongest advantages over PaLM 2 for AI agent development is its native support for agentic patterns:

• Function calling: Reliable JSON-structured tool use for agent actions
• Google Search grounding: Agents can ground responses in real-time web data
• Long context: Process entire codebases or document collections in a single call
• Multimodal perception: Agents can process screenshots, images, and video for browser or desktop automation

For teams building with Google ADK or integrating with LangChain, Gemini is the obvious model choice in 2026.

Verdict: PaLM 2 vs Gemini

There's no real competition here in 2026. PaLM 2 is deprecated and Gemini is Google's current, actively-developed LLM family. The question isn't which to choose—it's which Gemini model to use:

• Best for most use cases: Gemini 2.5 Flash (fast + cheap)
• Best for complex reasoning/coding: Gemini 2.5 Pro with thinking mode
• Best for on-device: Gemini Nano

If you're evaluating Google's AI models for a new 2026 project, start with Gemini 2.5 Flash. It's the best price-performance ratio in Google's lineup and handles most production workloads with ease.

Explore More on AgDex

• Google Gemini — Full Review 2026
• Gemini 2.5 Pro — Deep Dive
• Google ADK — Build Agents with Gemini
• How to Choose an LLM in 2026
• GPT-5 vs Claude vs Gemini 2026