Claude Opus 4.8 vs Grok 4.6
Compare Claude Opus 4.8 and Grok 4.6 side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, and Open Prompt.
Compare Claude Opus 4.8 vs Grok 4.6 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Compare image classification labels and confidence scores side-by-side.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Claude Opus 4.8 vs Grok 4.6 on Vision Evals
Claude Opus 4.8 scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where Claude Opus 4.8 leads 38.6% to 20.2%.
Overall, Claude Opus 4.8 averages 66.8% (#19 of 30) against 67.8% (#17 of 30) for Grok 4.6.
Grok 4.6 is cheaper ($0.0069 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 7.4s per sample).
Claude Opus 4.8 vs Grok 4.6 Comparison Table
Evals updated August 14, 2026Pricing updated August 15, 2026
| Property | Claude Opus 4.8 | Grok 4.6 |
|---|---|---|
| Organization | Anthropic | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Aug 2026 |
| Context Window | 1.0M | 500K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $2.00 |
| Output $/1M | $25.00 | $6.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Object Detection | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 66.8% | 67.8% |
| Avg cost / sample | $0.016 | $0.0069 |
| Avg speed / sample | 5.20s | 7.39s |
| By task | ||
| Object Detection | 38.6% $0.026 | 20.2% $0.0068 |
| Counting | 52.7% $0.0076 | 70.3% $0.0074 |
| Identification | 75.0% $0.0067 | 78.1% $0.0048 |
| OCR | 93.8% $0.020 | 92.0% $0.0086 |
| Data Extraction | 87.6% $0.0076 | 84.5% $0.0042 |
| Reasoning (low) | 53.0% $0.0078 | 61.6% $0.0087 |
| Reasoning (high) | 52.3% $0.0078 | 61.6% $0.027 |
Claude Opus 4.8 vs Grok 4.6: 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.
Grok 4.6 is a proprietary reasoning model from xAI aimed at long-running agentic workflows, coding, and knowledge work. It accepts text and image input and returns text, with a 500,000 token context window and a knowledge cutoff of February 1, 2026. The model exposes an adjustable reasoning budget with low, medium, high, and xhigh settings, where high is the default, and it supports function calling, structured outputs, web and X search, and code execution as documented tool behaviors. Its visual capability covers interpreting images supplied alongside text prompts, which places it in the visual question answering and document understanding family rather than producing pixel level outputs such as boxes or masks.
xAI characterizes Grok 4.6 as the result of an extended post-training run over the Grok 4.5 lineage rather than a new pretrained base. The described recipe combines curated model-generated reasoning and technical data, engineering data, a revised optimizer, regenerated supervised fine-tuning trajectories, and reinforcement learning across agent environments spanning knowledge work, coding, kernel optimization, web development, and computer-aided design. Parameter count and architecture specifics are not disclosed. Independent measurement from Artificial Analysis places the model at 61 on its Intelligence Index, five points above Grok 4.5.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.6 performed slightly better overall. The two split the six vision tasks 3 to 3, but Grok 4.6 averages 67.8% (#17 of 30) against 66.8% (#19 of 30) for Claude Opus 4.8. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark, Claude Opus 4.8 leads with 38.6% against 20.2%. This is the widest gap between the two models across the benchmark's tasks.
Grok 4.6 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0069 per sample against $0.016. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; Grok 4.6 is priced at $2.00 per 1M input tokens and $6.00 per 1M output. 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 7.4s. 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 image captioning and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.