Nano Banana 2 vs Claude 4.5 Opus

1. High-efficiency counterpart to Gemini 3 Pro Image

• Google describes Nano Banana 2 as the high-efficiency counterpart to Gemini 3 Pro Image.
• Optimized for speed and high-volume developer use cases rather than maximum pro-grade fidelity.

2. Native image generation + understanding

• Accepts text and image inputs and can output both text and images in a conversational workflow.
• Useful for quick iteration, editing, remixing, and interactive visual applications.

3. Strong throughput with practical image controls

• Supports up to 14 input images per prompt, 128 k input tokens, and 32,768 output tokens.
• Handles multiple aspect ratios and can generate or edit images while keeping latency and cost lower than higher-end image models.

4. Grounded, developer-friendly image workflows

• Supports Google Search grounding and Content Credentials (C2PA) for image outputs.
• All generated images include SynthID watermarking as part of Google's native image stack.

1. Maximum capability with more practical pricing

• Anthropic introduced Opus 4.5 as its most intelligent model, combining maximum capability with practical performance.
• It was positioned as the best model in the world for coding, agents, and computer use at launch, with pricing reduced to $5/M input and $25/M output.

2. Step-change gains for coding and advanced agent work

• Anthropic describes Opus 4.5 as state-of-the-art on real-world software engineering tests.
• It also improved everyday knowledge-work tasks like deep research, slides, and spreadsheets while staying strong on long-horizon agent workflows.

3. Better control over reasoning depth

• Opus 4.5 introduced the effort parameter, letting developers trade off response thoroughness against token efficiency.
• This made it easier to use one flagship model across both high-depth analysis and more cost-sensitive production workloads.

4. Stronger computer use and continuity

• Added enhanced computer use with a zoom action for inspecting detailed screen regions.
• Preserves prior thinking blocks across turns, helping the model maintain reasoning continuity in extended multi-step tasks.