Claude Fable 5.2 vs Grok 4.7
Claude Fable 5.2 and Grok 4.7 score 86.5% vs 63.5% averaged across five Vision Evals tasks (#2 and #35 of 67 models), so Claude Fable 5.2 leads overall.
- Claude Fable 5.2 wins all 5 tasks, with its widest lead on object detection (81.6% vs 40.4%).
- Grok 4.7 is about half the cost: $0.020 vs $0.031 overall average cost per sample.
- Claude Fable 5.2 is faster: 9.3s vs 33.3s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Fable 5.2 vs Grok 4.7 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 Fable 5.2 vs Grok 4.7 on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Fable 5.2 vs Grok 4.7 Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Claude Fable 5.2 | Grok 4.7 |
|---|---|---|
| Organization | Anthropic | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Nov 2026 | Sep 2026 |
| Context Window | 1.0M | 500K |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $8.00 | $2.00 |
| Output $/1M | $40.00 | $6.00 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 86.5% | 63.5% |
| Avg cost / sample | $0.031 | $0.020 |
| Avg speed / sample | 9.33s | 33.31s |
| By task | ||
| Object Detection (low) | 81.6% ±0.3, Mean of 3 runs, range 81.2 to 81.9 | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 |
| Object Detection (high) | 83.3% ±0.5, Mean of 3 runs, range 82.8 to 83.7 | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 |
| Counting (low) | 86.5% ±0.0, Mean of 3 runs, range 86.5 to 86.5 | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 |
| Counting (high) | 92.8% ±1.4, Mean of 3 runs, range 91.9 to 94.6 | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | 94.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 79.5% | 63.6% |
| by category |
|
|
| OCR (high) | 84.8% | – |
| by category |
| |
| Reasoning (low) | 88.1% ±1.3, Mean of 3 runs, range 86.8 to 89.4 | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 |
| Reasoning (high) | 92.0% ±1.0, Mean of 3 runs, range 91.4 to 93.4 | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 |
Claude Fable 5.2 vs Grok 4.7: Overview
Claude Fable 5.2 is a proprietary multimodal model from Anthropic, released on November 9, 2026. It accepts text and images and produces text, with a one million token context window and up to 128,000 output tokens. Adaptive thinking is always enabled, with an effort setting that controls reasoning. Anthropic positions Fable for demanding reasoning and long-running technical work, including coding and analysis of visual inputs.
Fable 5.2 succeeds Fable 5.1. Anthropic reports improvements in coding, reasoning, and vision, with output generation up to 30% faster than its predecessor. Fable and Mythos 5.2 share an underlying model, with additional cybersecurity and biology safeguards applied to Fable. Model weights are not publicly released.
Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.
The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Fable 5.2 performed better. It scores higher on all five vision tasks and averages 86.5% (#2 of 67) against 63.5% (#35 of 67) for Grok 4.7. 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 at low effort, Claude Fable 5.2 leads with 81.6% against 40.4%. This is the widest gap between the two models across the benchmark's tasks.
Grok 4.7 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.020 per sample against $0.031. Claude Fable 5.2 is priced at $8.00 per 1M input tokens and $40.00 per 1M output; Grok 4.7 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 Fable 5.2 is faster. Across Roboflow's Vision Evals it averaged 9.3s per inference against 33.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.