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Claude Fable 5 vs Qwen3.7 Flash

Compare Claude Fable 5 and Qwen3.7 Flash side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Open Prompt, and Object Detection.

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AnthropicClaude Fable 5
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QwenQwen3.7 Flash
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Models in this comparison

Claude Fable 5 vs Qwen3.7 Flash on Vision Evals

Claude Fable 5 scores higher on all six Vision Evals tasks.

The widest gap is Reasoning, where Claude Fable 5 leads 66.2% to 34.4%.

Overall, Claude Fable 5 averages 78.8% (#7 of 25) against 61.7% (#24 of 25) for Qwen3.7 Flash.

Qwen3.7 Flash is both cheaper ($0.0001 vs $0.034 per sample) and faster (6.3s vs 8.7s per sample).

Claude Fable 5Qwen3.7 Flash

Claude Fable 5 vs Qwen3.7 Flash Comparison Table

Evals updated August 6, 2026Pricing updated August 11, 2026

PropertyClaude Fable 5Qwen3.7 Flash
OrganizationAnthropicQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Jul 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$10.00$0.030
Output $/1M$50.00$0.130
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
78.8%
61.7%
Avg cost / sample$0.034$0.0001
Avg speed / sample8.71s6.32s
By task
Object Detection
56.0%
$0.059
42.8%
$0.0001
Counting
63.5%
$0.017
46.0%
<$0.0001
Identification
100.0%
$0.014
84.4%
<$0.0001
OCR
94.0%
$0.039
84.1%
$0.0001
Data Extraction
92.8%
$0.015
78.3%
<$0.0001
Reasoning (low)
66.2%
$0.018
34.4%
<$0.0001
Reasoning (high)
66.2%
$0.024
60.9%
$0.0005

Claude Fable 5 vs Qwen3.7 Flash: Overview

Claude Fable 5

Claude Fable 5 is Anthropic's first generally available Mythos-class large language model, released on June 9, 2026. It is built for long-horizon, asynchronous, and agentic tasks that prior Claude generations could not sustain, including multi-day autonomous coding sessions, complex knowledge work, and document-heavy analysis. The model supports a 1 million token context window with up to 128,000 output tokens per request and uses adaptive thinking as its sole reasoning mode, where the effort level is adjustable but raw chain-of-thought is never returned. Vision capabilities allow the model to parse diagrams, charts, and tables embedded in files and PDFs, and to use visual feedback to evaluate its own coding outputs against design goals. On benchmarks such as SWE-Bench Pro, the model scores 80.3% compared to 69.2% for Claude Opus 4.8, and it leads on CursorBench 3.1 for autonomous coding workflows.

Claude Fable 5 shares the same underlying model weights as Claude Mythos 5, but is deployed with safety classifiers that automatically reroute queries in high-risk domains — including cybersecurity, biology, and chemistry — to Claude Opus 4.8. These classifiers trigger in fewer than 5% of sessions on average. As a designated Covered Model, all traffic is subject to mandatory 30-day data retention to support safety monitoring. The model is available via the Claude API, Amazon Bedrock, Vertex AI, and Microsoft Foundry. Anthropic has not publicly disclosed parameter count, architecture details, or training data composition for this model.

Qwen3.7 Flash

Qwen3.7 Flash is the low-latency, cost-oriented tier of Alibaba's Qwen3.7 series, a vision-language reasoning model that accepts interleaved text and image input and returns text. It is built as a hybrid thinking model: like the rest of the Qwen3.7, Qwen3.6, and Qwen3.5 families served through Alibaba Cloud Model Studio, it can either emit an explicit reasoning trace before answering or respond directly, with thinking behavior controlled by an enable_thinking switch that defaults to on for the Qwen3.7 generation. The model exposes a context window of roughly one million tokens and a maximum generation length of 65,536 tokens, which allows long multi-image sequences, long documents, and extended agent trajectories to be held in a single request.

Functionally, Qwen3.7 Flash targets multimodal agent workloads rather than pure chat. Reported strengths include object recognition, spatial understanding, and perception of real-world scenes, alongside visual coding, search, and computer-use style interaction where the model reads screen content and reasons over interface state. Weights are not published; the model is a proprietary endpoint positioned below Qwen3.7 Plus and Qwen3.7 Max in the same series, and it supports function calling and tool use for agentic pipelines.