Claude Fable 5.2 vs Gemini 3.6 Flash
Claude Fable 5.2 and Gemini 3.6 Flash score 86.5% vs 77.1% averaged across five Vision Evals tasks (#2 and #13 of 67 models), so Claude Fable 5.2 leads overall.
- Claude Fable 5.2 wins 4 of the 5 tasks, with its widest lead on object detection (81.6% vs 57.1%).
- Gemini 3.6 Flash is about 1/9 of the cost: $0.0033 vs $0.031 overall average cost per sample.
- Claude Fable 5.2 is faster: 9.3s vs 10.4s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Fable 5.2 vs Gemini 3.6 Flash 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 Gemini 3.6 Flash on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Fable 5.2 vs Gemini 3.6 Flash Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Claude Fable 5.2 | Gemini 3.6 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| 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 | $0.750 |
| Output $/1M | $40.00 | $3.75 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Video Classification | Not listed | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 86.5% | 77.1% |
| Avg cost / sample | $0.031 | $0.0033 |
| Avg speed / sample | 9.33s | 10.43s |
| By task | ||
| Object Detection (low) | 81.6% ±0.3, Mean of 3 runs, range 81.2 to 81.9 | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 |
| Object Detection (high) | 83.3% ±0.5, Mean of 3 runs, range 82.8 to 83.7 | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 |
| Counting (low) | 86.5% ±0.0, Mean of 3 runs, range 86.5 to 86.5 | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 92.8% ±1.4, Mean of 3 runs, range 91.9 to 94.6 | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| Identification (high) | 94.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 |
| OCR (low) | 79.5% | 71.8% |
| by category |
|
|
| OCR (high) | 84.8% | 77.4% |
| by category |
|
|
| Reasoning (low) | 88.1% ±1.3, Mean of 3 runs, range 86.8 to 89.4 | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 |
| Reasoning (high) | 92.0% ±1.0, Mean of 3 runs, range 91.4 to 93.4 | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 |
Claude Fable 5.2 vs Gemini 3.6 Flash: 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.
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Fable 5.2 performed better. It scores higher on 4 of the five vision tasks and averages 86.5% (#2 of 67) against 77.1% (#13 of 67) for Gemini 3.6 Flash. 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 57.1%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.031. Claude Fable 5.2 is priced at $8.00 per 1M input tokens and $40.00 per 1M output; Gemini 3.6 Flash is priced at $0.75 per 1M input tokens and $3.75 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 10.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.