Claude Sonnet 5.5 vs Gemma 4 31B
Claude Sonnet 5.5 averages 77.6% across five Vision Evals tasks and Gemma 4 31B 57.7% across four (#13 and #44 of 68 models), so Claude Sonnet 5.5 leads overall.
- Claude Sonnet 5.5 wins all 4 tasks both ran, with its widest lead on counting (79.3% vs 51.4%).
- Gemma 4 31B is about a fifth of the cost: $0.0015 vs $0.0073 overall average cost per sample.
- Claude Sonnet 5.5 is faster: 8.3s vs 34.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Claude Sonnet 5.5 vs Gemma 4 31B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Sonnet 5.5 vs Gemma 4 31B Comparison Table
Evals updated November 10, 2026Pricing updated November 12, 2026
| Property | Claude Sonnet 5.5 | Gemma 4 31B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | Unknown | 31B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.200 |
| Output $/1M | $10.00 | $0.400 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 77.6% | 57.7%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0073 | $0.0015 |
| Avg speed / sample | 8.33s | 34.36s |
| By task | ||
| Object Detection (low) | 74.3% ±0.9, Mean of 3 runs, range 73.5 to 75.3 | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 |
| 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 | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 |
| 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 | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| Identification (high) | 90.6% ±0.0, Mean of 3 runs, range 90.6 to 90.6 | – |
| OCR | 66.3% | – |
| by category |
| |
| Reasoning (low) | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 |
| Reasoning (high) | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 | – |
Claude Sonnet 5.5 vs Gemma 4 31B: 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.
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 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Sonnet 5.5 performed better. It scores higher on all 4 vision tasks and averages 77.6% (#13 of 68) against 57.7% (#44 of 68) for Gemma 4 31B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Counting benchmark at low effort, Claude Sonnet 5.5 leads with 79.3% against 51.4%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 31B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0015 per sample against $0.0073. Actual costs depend on your image sizes, prompts, and output length.
Claude Sonnet 5.5 is faster. Across Roboflow's Vision Evals it averaged 8.3s per inference against 34.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.