Claude Opus 4.8 vs Gemma 4 31B
Compare Claude Opus 4.8 and Gemma 4 31B side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Models in this comparison
Claude Opus 4.8 vs Gemma 4 31B on Vision Evals
Claude Opus 4.8 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemma 4 31B leads 48.2% to 38.6%.
Overall, Claude Opus 4.8 averages 68.7% (#28 of 53) against 67.0% (#30 of 53) for Gemma 4 31B.
Gemma 4 31B is cheaper ($0.0012 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 28.8s per sample).
Claude Opus 4.8 vs Gemma 4 31B Comparison Table
Evals updated September 5, 2026Pricing updated September 20, 2026
| Property | Claude Opus 4.8 | Gemma 4 31B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 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 | ||
| 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 | 68.7% | 67.0% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.016 | $0.0012 |
| Avg speed / sample | 5.20s | 28.79s |
| By task | ||
| Object Detection | 38.6% | 48.2% ±0.2, Mean of 3 runs, range 48.0 to 48.4 |
| Counting | 54.0% | 51.4% ±1.4, Mean of 3 runs, range 50.0 to 52.7 |
| Identification | 84.4% | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR | 93.8% | 90.8% ±0.2, Mean of 3 runs, range 90.6 to 90.9 |
| Data Extraction | 88.7% | 80.4% ±2.6, Mean of 3 runs, range 77.3 to 82.5 |
| Reasoning (low) | 53.0% | 50.8% ±1.7, Mean of 3 runs, range 49.0 to 52.3 |
| Reasoning (high) | 52.3% | – |
Claude Opus 4.8 vs Gemma 4 31B: Overview
Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.
Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.
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 4.8 performed better. It scores higher on 5 of the six vision tasks and averages 68.7% (#28 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.
No. On the Vision Evals Object Detection benchmark at low effort, Gemma 4 31B leads with 48.2% against 38.6%. 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 $0.016. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 5.2s 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 image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.