Updated: 2026-07-12

Google launched Bard[1] in February 2023 as a response to ChatGPT’s cultural impact, and in May presented PaLM 2[2] as the model powering it. After several months of intensive industry use, the differences between Bard + PaLM 2 and GPT-4 are clearer: it’s not a race for who generates better text but for who integrates generative AI best into the rest of their ecosystem.

Key Takeaways

  • PaLM 2 comes in four sizes and competes with GPT-3.5/4 on standard benchmarks, winning some and losing others.

  • Google’s real advantage isn’t the model but Workspace integration (Gmail, Docs, Sheets, Meet).

  • Bard has native Google Search access, letting it cite recent sources.

  • Gemini, the multimodal bet from Google Brain + DeepMind, is the next piece on the board.

  • For teams evaluating LLMs, the decision depends more on cloud platform than on the model itself.

PaLM 2 in Context

PaLM 2 is the second generation of Google’s Pathways Language Model[3]. Published with a very condensed technical paper (no model size, no detailed training dataset), it ships in four sizes: Gecko, Otter, Bison, Unicorn. Bard uses Unicorn; developers access Bison via Vertex AI[4].

Trained with a higher share of multilingual, code, and scientific data than PaLM 1, it competes with GPT-3.5/GPT-4 on standard benchmarks: MMLU, BIG-bench, Winograd. Results oscillate: it wins some, loses others.

In real applications, PaLM 2 in Bard is solid for:

  • Summarising and rewriting long text. The expanded context window (up to 38k tokens on Bison) makes work on long documents easier.

  • Search with reasoning. Bard has native Google Search access, so it can cite recent sources, something ChatGPT without browsing can’t.

  • Quality multilingual. Particularly in less common language pairs, PaLM 2 gives more consistent results than other large models.

Where GPT-4 still leads

Two areas where GPT-4 maintains a clear edge:

  • Complex step-by-step reasoning. On competition math, symbolic logic, or detailed legal reasoning, GPT-4 shows higher reliability.

  • Code generation and refactoring. GPT-4’s integration with GitHub Copilot has matured faster than Google’s proposition, though Codey is starting to close the gap.

Developer ecosystem also weighs heavily: there’s an order of magnitude more libraries, tutorials, and products built on OpenAI’s API than on Vertex AI.

Google’s strategic advantage

Despite PaLM 2 not beating GPT-4 on average, Google holds a unique card: suite integration. The "Duet AI" announcement for Workspace (text generation in Gmail, auto-summaries in Meet, drafts in Docs, formulas in Sheets) requires no extra integration for a company already on Workspace: it appears where the team already works.

Microsoft does the same with Microsoft 365 Copilot on GPT-4. The real fight will center on which productivity suite integrates generative AI more usefully, more than which model is bigger.

Google Cloud Platform logo, the infrastructure over which Vertex AI serves PaLM 2 models to developersGoogle Cloud Platform logo, the infrastructure over which Vertex AI serves PaLM 2 models to developers (Image: Laurenbergman, CC BY 4.0, via Wikimedia Commons)

Bard as a product

Bard itself lives in the background. Google uses it as a direct consumer touchpoint and as a channel to experiment with features:

  • Share conversations via public link.

  • Gmail/Docs integration to export generated content directly to an email or document.

  • Colab export for data and code.

Bard isn’t yet a direct competitor to ChatGPT Plus on plugins (limited vs OpenAI’s), but each update closes some of the gap.

Gemini on the Horizon

Google’s next piece is Gemini[5], announced as the fusion of Google Brain and DeepMind teams to produce a native multimodal model. According to statements from Sundar Pichai and Demis Hassabis, Gemini will be trained on a much larger dataset and designed with tool-use (action, not just text) in mind.

If that model meets expectations, the "best model + best integration" architecture could be difficult to counter.

Practical Considerations

For teams evaluating which LLM to bet on:

  • Already on Workspace or GCP: Vertex AI with PaLM 2 has the lowest-friction natural integration.

  • Already on Azure or AWS: OpenAI via Azure OpenAI Service or Bedrock gives more model options (GPT-4, Claude, Llama).

  • New decision: test both with prompts from your real use case. Academic benchmarks rarely predict behaviour in your domain.

Complement this decision with the analysis of LLaMA 2 and open models to see the full ecosystem picture.

Conclusion

Bard with PaLM 2 doesn’t beat ChatGPT with GPT-4 on raw capability, but Google is playing a different game: winning by integration within the Workspace ecosystem. The real fight will be who offers the best end-to-end experience, not who has the biggest model.

This entry is also available in Spanish: Bard y PaLM 2: la apuesta de Google en IA generativa.

Sources

  1. Bard
  2. PaLM 2
  3. Pathways Language Model
  4. Vertex AI
  5. Gemini