Claude Haiku 4.5 vs Gemma 3 27B
We don't benchmark Claude Haiku 4.5 or Gemma 3 27B on Vision Evals. Claude Haiku 4.5 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 $1/M input). Test them side by side on your own images below.
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Claude Haiku 4.5 vs Gemma 3 27B Comparison Table
Pricing updated November 3, 2026
| Property | Claude Haiku 4.5 | Gemma 3 27B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 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 | $1.00 | $0.080 |
| Output $/1M | $5.00 | $0.350 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Claude Haiku 4.5 vs Gemma 3 27B: Overview
Claude Haiku 4.5 is Anthropic’s lightweight model in the Claude 4.5 series, released in October 2025 under a proprietary license. Designed for speed and cost efficiency, it delivers near-frontier performance while maintaining Anthropic’s AI Safety Level 2 standard. Haiku 4.5 supports both text and multimodal (text and image) inputs, integrates tool use and extended reasoning, and features a 200,000 token context window, making it adept at handling long or complex workflows. Haiku 4.5 is ideal for developers and researchers seeking rapid, cost-effective model calls for analysis, coding, or multimodal understanding.
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.