Claude Opus 5 vs Gemma 4 31B
Compare Claude Opus 5 and Gemma 4 31B side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Models in this comparison
Claude Opus 5 vs Gemma 4 31B on Vision Evals
Claude Opus 5 scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Claude Opus 5 leads 71.5% to 50.8%.
Overall, Claude Opus 5 averages 78.3% (#14 of 53) against 67.0% (#30 of 53) for Gemma 4 31B.
Gemma 4 31B is cheaper ($0.0012 vs $0.017 per sample), while Claude Opus 5 is faster (7.4s vs 28.8s per sample).
Claude Opus 5 vs Gemma 4 31B Comparison Table
Evals updated September 5, 2026Pricing updated September 8, 2026
| Property | Claude Opus 5 | Gemma 4 31B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 31B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.090 |
| Output $/1M | $25.00 | $0.340 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Image Tagging | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 78.3% | 67.0% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.017 | $0.0012 |
| Avg speed / sample | 7.38s | 28.79s |
| By task | ||
| Object Detection | 54.4% | 48.2% ±0.2, Mean of 3 runs, range 48.0 to 48.4 |
| Counting | 70.3% | 51.4% ±1.4, Mean of 3 runs, range 50.0 to 52.7 |
| Identification | 90.6% | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR | 93.2% | 90.8% ±0.2, Mean of 3 runs, range 90.6 to 90.9 |
| Data Extraction | 89.7% | 80.4% ±2.6, Mean of 3 runs, range 77.3 to 82.5 |
| Reasoning (low) | 71.5% | 50.8% ±1.7, Mean of 3 runs, range 49.0 to 52.3 |
| Reasoning (high) | 74.2% | – |
Claude Opus 5 vs Gemma 4 31B: Overview
Claude Opus 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at roughly half the cost, priced at $5 per million input tokens and $25 per million output tokens. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.
Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.
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 Opus 5 performed better. It scores higher on all six vision tasks and averages 78.3% (#14 of 53) against 67.0% (#30 of 53) 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 Reasoning benchmark at low effort, Claude Opus 5 leads with 71.5% against 50.8%. 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 $0.0012 per sample against $0.017. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 5 is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 28.8s. 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.