Claude Fable 5.2 vs GPT-6.1 Sol
Claude Fable 5.2 and GPT-6.1 Sol score 86.5% vs 81.7% averaged across five Vision Evals tasks (#2 and #7 of 67 models), so Claude Fable 5.2 leads overall.
- Claude Fable 5.2 wins all 5 tasks, with its widest lead on counting (86.5% vs 78.8%).
- GPT-6.1 Sol is about a quarter of the cost: $0.0068 vs $0.031 overall average cost per sample.
- Claude Fable 5.2 is faster: 9.3s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Fable 5.2 vs GPT-6.1 Sol 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 GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Fable 5.2 vs GPT-6.1 Sol Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Claude Fable 5.2 | GPT-6.1 Sol |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | undisclosed |
| 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 | $10.00 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 86.5% | 81.7% |
| Avg cost / sample | $0.031 | $0.0068 |
| Avg speed / sample | 9.33s | 12.82s |
| By task | ||
| Object Detection (low) | 81.6% ±0.3, Mean of 3 runs, range 81.2 to 81.9 | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | 83.3% ±0.5, Mean of 3 runs, range 82.8 to 83.7 | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 86.5% ±0.0, Mean of 3 runs, range 86.5 to 86.5 | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | 92.8% ±1.4, Mean of 3 runs, range 91.9 to 94.6 | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 94.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 79.5% | 75.8% |
| by category |
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| OCR (high) | 84.8% | 75.8% |
| by category |
|
|
| Reasoning (low) | 88.1% ±1.3, Mean of 3 runs, range 86.8 to 89.4 | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | 92.0% ±1.0, Mean of 3 runs, range 91.4 to 93.4 | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
Claude Fable 5.2 vs GPT-6.1 Sol: 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.
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
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 81.7% (#7 of 67) for GPT-6.1 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Counting benchmark at low effort, Claude Fable 5.2 leads with 86.5% against 78.8%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6.1 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0068 per sample against $0.031. Claude Fable 5.2 is priced at $8.00 per 1M input tokens and $40.00 per 1M output; GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 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 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.