Claude Opus 4.6 vs Gemma 4 31B
Compare Claude Opus 4.6 and Gemma 4 31B side-by-side. See how these vision models stack up in Open Prompt, OCR, Object Detection, Classification, and Image Captioning.
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Claude Opus 4.6 vs Gemma 4 31B: Overview
Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.
As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.
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.
Claude Opus 4.6 vs Gemma 4 31B Comparison Table
| Property | Claude Opus 4.6 | Gemma 4 31B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 31B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.120 |
| Output $/1M | $25.00 | $0.350 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 64.18% | 67.16% |
| Avg Response Time | 23.35s | 34.59s |
| Median input tokensincl. image tokens | 2.2K | 294 |
| Median output tokens | 130 | 169 |
| Est. cost / taskon this benchmark | $0.014 | $0.0001 |
| Defect Detection | 73.3%(11/15) | 80%(12/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 20%(2/10) | 10%(1/10) |
| Object Understanding | 71.4%(10/14) | 71.4%(10/14) |
| Spatial Understanding | 68.4%(13/19) | 73.7%(14/19) |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology