Claude Opus 5 vs Grok 4.6
Compare Claude Opus 5 and Grok 4.6 side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, and Classification.
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Models in this comparison
Claude Opus 5 vs Grok 4.6 on Vision Evals
Claude Opus 5 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Claude Opus 5 leads 54.4% to 20.2%.
Overall, Claude Opus 5 averages 77.1% (#8 of 28) against 67.8% (#16 of 28) for Grok 4.6.
Grok 4.6 is cheaper ($0.0069 vs $0.017 per sample), while Claude Opus 5 is faster (7.4s vs 7.4s per sample).
Claude Opus 5 vs Grok 4.6 Comparison Table
Evals updated August 12, 2026Pricing updated August 13, 2026
| Property | Claude Opus 5 | Grok 4.6 |
|---|---|---|
| Organization | Anthropic | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 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 | ||
| Multi-Label Classification | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Image Tagging | ||
| 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 | 77.1% | 67.8% |
| Avg cost / sample | $0.017 | $0.0069 |
| Avg speed / sample | 7.38s | 7.39s |
| By task | ||
| Object Detection | 54.4% $0.027 | 20.2% $0.0068 |
| Counting | 70.3% $0.0096 | 70.3% $0.0074 |
| Identification | 84.4% $0.0072 | 78.1% $0.0048 |
| OCR | 93.2% $0.020 | 92.0% $0.0086 |
| Data Extraction | 88.7% $0.0080 | 84.5% $0.0042 |
| Reasoning (low) | 71.5% $0.010 | 61.6% $0.0087 |
| Reasoning (high) | 74.2% $0.018 | 61.6% $0.027 |
Claude Opus 5 vs Grok 4.6: 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.
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, Claude Opus 5 performed better. It scores higher on 5 of the six vision tasks and averages 77.1% (#8 of 28) against 67.8% (#16 of 28) for Grok 4.6. 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 5 leads with 54.4% 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 $0.017. Claude Opus 5 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 5 is faster. Across Roboflow's Vision Evals it averaged 7.4s 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.