Roboflow

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.

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

Score by taskClaude Fable 5.2Grok 4.7
Object Detection
81.6
40.4
Counting
86.5
61.7
Reasoning
88.1
64.2
OCR
79.5
63.6
Identification
96.9
87.5

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

PropertyClaude Fable 5.2Grok 4.7
OrganizationAnthropicSpaceXAI
Categoryclosedclosed
Modalitymultimodal—
Release DateNov 2026Sep 2026
Context Window1.0M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$8.00$2.00
Output $/1M$40.00$6.00
Vision Tasks
CaptioningSupportedDemo
ClassificationSupportedDemo
Object DetectionSupportedDemo
OCRSupportedDemo
Visual Question AnsweringSupportedDemo
Both support 5 more tasks
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot 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 / sample9.33s33.31s
By task
Object Detection (low)
81.6%
±0.3, Mean of 3 runs, range 81.2 to 81.9
$0.048
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
Object Detection (high)
83.3%
±0.5, Mean of 3 runs, range 82.8 to 83.7
$0.055
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
Counting (low)
86.5%
±0.0, Mean of 3 runs, range 86.5 to 86.5
$0.015
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
Counting (high)
92.8%
±1.4, Mean of 3 runs, range 91.9 to 94.6
$0.021
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.011
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
Identification (high)
94.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.012
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
OCR (low)
79.5%
$0.030
63.6%
$0.023
by category
Single value
64.8%
Transcription
94.7%
Structured JSON
92.8%
Text localization
74.5%
Single value
54.4%
Transcription
89.5%
Structured JSON
86.4%
Text localization
14.8%
OCR (high)
84.8%
$0.043
–
by category
Single value
77.4%
Transcription
97.9%
Structured JSON
94.1%
Text localization
72.9%
Reasoning (low)
88.1%
±1.3, Mean of 3 runs, range 86.8 to 89.4
$0.016
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
Reasoning (high)
92.0%
±1.0, Mean of 3 runs, range 91.4 to 93.4
$0.024
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024

Claude Fable 5.2 vs Grok 4.7: Overview

Claude Fable 5.2

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

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.