Claude Opus 4.8 vs Gemma 4 12B
Claude Opus 4.8: 58.3% average across five Vision Evals tasks (#40 of 65 models). We have not run Gemma 4 12B through Roboflow's Vision Evals, so there is no head-to-head score.
Compare Claude Opus 4.8 vs Gemma 4 12B live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Claude Opus 4.8 vs Gemma 4 12B Comparison Table
Evals updated October 26, 2026Pricing updated November 5, 2026
| Property | Claude Opus 4.8 | Gemma 4 12B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | Unknown | 12B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | No published price |
| Output $/1M | $25.00 | No published price |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Chart Question Answering | Supported | Not listed |
| Classification | Demo | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Object Detection | Demo | Not listed |
Both support 1 more task | ||
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
Both support 1 more feature | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 58.3% | Not evaluated |
| Avg cost / sample | $0.021 | – |
| Avg speed / sample | 7.66s | – |
| By task | ||
| Object Detection | 38.6% | – |
| Counting | 54.0% | – |
| Identification | 84.4% | – |
| OCR (low) | 61.5% | – |
| by category |
| |
| OCR (high) | 62.1% | – |
| by category |
| |
| Reasoning (low) | 53.0% | – |
| Reasoning (high) | 52.3% | – |
Claude Opus 4.8 vs Gemma 4 12B: 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. 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 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.
This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.