Claude Opus 4.1 vs Gemma 3 27B
We don't benchmark Claude Opus 4.1 or Gemma 3 27B on Vision Evals. Claude Opus 4.1 is proprietary under Proprietary: commercial use per vendor terms. Gemma 3 27B is open-weight under Custom. Gemma 3 27B is cheaper per token ($0.08/M vs $15/M input).
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Claude Opus 4.1 is deprecated and can no longer be run. Details and evals are still available on its model page.
Claude Opus 4.1 vs Gemma 3 27B Comparison Table
Pricing updated November 5, 2026
| Property | Claude Opus 4.1 | Gemma 3 27B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Mar 2025 |
| Context Window | 200K | 128K |
| Parameters | Unknown | Unknown |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $0.080 |
| Output $/1M | $75.00 | $0.350 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Object Detection | Supported | Not listed |
| Model Features | ||
Both support 3 more features | ||
Claude Opus 4.1 vs Gemma 3 27B: Overview
Claude 4.1 Opus, released by Anthropic in August 2025, is the upgraded flagship of the Claude 4 family, building on Opus 4 with stronger reasoning and agentic capabilities. Like its predecessor, it is multimodal and optimized for text, code, and tool use, with support for large context windows suited to multi-file codebases, technical workflows, and long-horizon problem solving.
It demonstrates more precise debugging, refactoring, and orchestration of agentic tasks while maintaining similar safety and alignment safeguards. It is best suited for enterprise-scale software development, research automation, and advanced reasoning workflows where reliability and depth of analysis are critical.
Gemma 3 27B, announced on March 12, 2025, is the largest open-weight model in Google DeepMind’s Gemma 3 family. With around 27 billion parameters, it is multimodal—accepting both text and images as input and producing text outputs. It supports a 128,000-token context window and typically generates up to ~8,192 tokens, enabling it to process multi-page documents, extended conversations, or large batches of images in a single prompt.
The model is instruction-tuned in its “-it” variants for chat, reasoning, and summarization use cases, and it supports structured outputs and function calling. It is multilingual, covering over 140 languages. Deployment is flexible: the full BF16 model requires ~46 GB of VRAM, but quantization-aware training (QAT) versions in 8-bit or 4-bit reduce the footprint significantly, allowing more accessible use outside large-scale clusters. While it delivers stronger reasoning and multimodal performance than smaller Gemma models, it remains lighter and more open than proprietary systems, making it well-suited for research, development, and fine-tuned applications.