Claude Sonnet 5.5 vs Gemma 3 27B
Compare Claude Sonnet 5.5 and Gemma 3 27B side-by-side.
Compare Claude Sonnet 5.5 vs Gemma 3 27B live
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Models in this comparison
Claude Sonnet 5.5 vs Gemma 3 27B Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 2026
| Property | Claude Sonnet 5.5 | Gemma 3 27B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Mar 2025 |
| Context Window | 1.0M | 128K |
| Parameters | ||
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $0.080 | |
| Output $/1M | $0.450 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Object Detection | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 83.8% | Not evaluated |
| Avg cost / sample | $0.0065 | – |
| Avg speed / sample | 10.78s | – |
| By task | ||
| Object Detection (low) | 74.3% ±0.9, Mean of 3 runs, range 73.5 to 75.3 | – |
| Object Detection (high) | 76.8% ±0.4, Mean of 3 runs, range 76.5 to 77.3 | – |
| Counting (low) | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 | – |
| Counting (high) | 82.9% ±1.4, Mean of 3 runs, range 81.1 to 83.8 | – |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | – |
| Identification (high) | 90.6% ±0.0, Mean of 3 runs, range 90.6 to 90.6 | – |
| OCR (low) | 90.6% ±0.9, Mean of 3 runs, range 90.0 to 91.7 | – |
| OCR (high) | 90.9% ±1.5, Mean of 3 runs, range 89.2 to 92.3 | – |
| Data Extraction (low) | 90.7% ±1.5, Mean of 3 runs, range 89.7 to 92.8 | – |
| Data Extraction (high) | 93.1% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | – |
| Reasoning (low) | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 | – |
| Reasoning (high) | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 | – |
Claude Sonnet 5.5 vs Gemma 3 27B: Overview
Claude Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.
On visual and agentic evaluations reported at launch, Sonnet 5.5 scores 61.6% on Chartography, a chart recognition test, compared with 15.6% for Claude Sonnet 5, and 80.1% on OSWorld 2.1, a computer use benchmark measuring screenshot driven control of a desktop environment, compared with 57.0% for Sonnet 5. It reports 70.6% on Terminal-Bench 4.0 for agentic coding. Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.
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.
Frequently Asked Questions
Gemma 3 27B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Claude Sonnet 5.5 is released under Proprietary, while Gemma 3 27B uses Custom. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.