Claude Sonnet 5 vs Gemma 4 31B
Compare Claude Sonnet 5 and Gemma 4 31B side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.
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Models in this comparison
Claude Sonnet 5 vs Gemma 4 31B on Vision Evals
Claude Sonnet 5 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemma 4 31B leads 47.5% to 36.1%.
Overall, Claude Sonnet 5 averages 66.4% (#38 of 61) against 67.0% (#36 of 61) for Gemma 4 31B.
Gemma 4 31B is cheaper ($0.0015 vs $0.0064 per sample), while Claude Sonnet 5 is faster (4.8s vs 34.4s per sample).
Claude Sonnet 5 vs Gemma 4 31B Comparison Table
Evals updated September 29, 2026Pricing updated October 3, 2026
| Property | Claude Sonnet 5 | Gemma 4 31B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 31B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.090 |
| Output $/1M | $10.00 | $0.340 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 66.4% | 67.0% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0064 | $0.0015 |
| Avg speed / sample | 4.84s | 34.36s |
| By task | ||
| Object Detection | 36.1% | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 |
| Counting | 56.8% | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 |
| Identification | 81.3% | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR | 91.7% | 90.8% ±0.6, Mean of 3 runs, range 90.2 to 91.5 |
| Data Extraction | 89.7% | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 |
| Reasoning (low) | 43.0% | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 |
| Reasoning (high) | 43.0% | – |
Claude Sonnet 5 vs Gemma 4 31B: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
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 performed better. It scores higher on 4 of the six vision tasks and averages 66.4% (#38 of 61) against 67.0% (#36 of 61) for Gemma 4 31B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Gemma 4 31B leads with 47.5% against 36.1%. 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 $0.0064. Actual costs depend on your image sizes, prompts, and output length.
Claude Sonnet 5 is faster. Across Roboflow's Vision Evals it averaged 4.8s 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.