Claude Haiku 4.5 vs Gemma 3 4B
We don't benchmark Claude Haiku 4.5 or Gemma 3 4B on Vision Evals. Claude Haiku 4.5 is proprietary under Proprietary: commercial use per vendor terms. Gemma 3 4B is open-weight under Custom. Gemma 3 4B is cheaper per token ($0.05/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 4B Comparison Table
Pricing updated November 10, 2026
| Property | Claude Haiku 4.5 | Gemma 3 4B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2025 | Mar 2025 |
| Context Window | 200K | 128K |
| Parameters | Unknown | 4B |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | $0.050 |
| Output $/1M | $5.00 | $0.100 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Claude Haiku 4.5 vs Gemma 3 4B: 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 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.
The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.