Claude 3.7 Sonnet vs Gemma 3 4B
Compare Claude 3.7 Sonnet and Gemma 3 4B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
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Claude 3.7 Sonnet is deprecated and can no longer be run. Details and evals are still available on its model page.
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Claude 3.7 Sonnet vs Gemma 3 4B: Overview
Claude 3.7 Sonnet, released by Anthropic in February 2025, is the company’s first hybrid reasoning model, combining fast response generation with an optional “extended thinking mode” that reveals longer, step-by-step reasoning. Like its predecessors, it is multimodal, handling both text and images, but expands its usability with up to 200,000 input tokens and up to 128,000 output tokens (64K generally available, 128K in beta). This makes it well-suited for analyzing large documents, codebases, or multi-turn conversations.
Typical applications include software development, research workflows, extended reasoning tasks, and enterprise-scale knowledge work where a trade-off between speed and visible reasoning is valuable.
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.
Claude 3.7 Sonnet vs Gemma 3 4B Comparison Table
| Property | Claude 3.7 Sonnet | Gemma 3 4B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2024 | Mar 2025 |
| Context Window | 200K | 128K |
| Parameters | 4B | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | |
| Output $/1M | $0.100 | |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Classification | ||
| Object Detection | ||
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 37.31% | |
| Avg Response Time | 16.80s | |
| Defect Detection | 60%(9/15) | |
| Document Understanding | 55.6%(5/9) | |
| Object Counting | 0%(0/10) | |
| Object Understanding | 42.9%(6/14) | |
| Spatial Understanding | 26.3%(5/19) | |
| OCR | ||
| Overall Score | 64.19% | |
| Avg Response Time | 0.92s | |
| Median input tokensincl. image tokens | 300 | |
| Median output tokens | 12 | |
| Est. cost / taskon this benchmark | <$0.0001 | |
| Focused Scene OCR | 63.6%(63/99) | |
| Handwritten Math | 10%(1/10) | |
| License Plate Recognition | 86.7%(26/30) | |
| Text Recognition | 73.3%(22/30) | |
| VQA & Extraction | 58.3%(35/60) | |