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Claude Fable 5.2 vs GLM 5.3 Flash

Claude Fable 5.2 and GLM 5.3 Flash score 86.5% vs 55.8% averaged across five Vision Evals tasks (#3 and #49 of 68 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 33.1%).
  • GLM 5.3 Flash is under a tenth of the cost: $0.0001 vs $0.031 overall average cost per sample.

Compare Claude Fable 5.2 vs GLM 5.3 Flash live

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AnthropicClaude Fable 5.2
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Z.aiGLM 5.3 Flash
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Claude Fable 5.2 vs GLM 5.3 Flash on Vision Evals

Score by taskClaude Fable 5.2GLM 5.3 Flash
Object Detection
81.6
33.1
Reasoning
88.1
51.0
Counting
86.5
55.4
OCR
79.5
55.4
Identification
96.9
84.4

Roboflow Vision Evals. Full breakdown below.

Claude Fable 5.2 vs GLM 5.3 Flash Comparison Table

Evals updated November 10, 2026Pricing updated November 11, 2026

PropertyClaude Fable 5.2GLM 5.3 Flash
OrganizationAnthropicZ.ai
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateNov 2026Aug 2026
Context Window1.0M1.0M
ParametersUnknown320B total, 18B active
LicenseProprietaryMIT
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$8.00$0.040
Output $/1M$40.00$0.078
Vision Tasks
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
86.5%
55.8%
Avg cost / sample$0.031$0.0001
Avg speed / sample9.33s9.29s
By task
Object Detection (low)
81.6%
±0.3, Mean of 3 runs, range 81.2 to 81.9
$0.048
33.1%
$0.0002
Object Detection (high)
83.3%
±0.5, Mean of 3 runs, range 82.8 to 83.7
$0.055
–
Counting (low)
86.5%
±0.0, Mean of 3 runs, range 86.5 to 86.5
$0.015
55.4%
$0.0001
Counting (high)
92.8%
±1.4, Mean of 3 runs, range 91.9 to 94.6
$0.021
–
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.011
84.4%
$0.0001
Identification (high)
94.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.012
–
OCR (low)
79.5%
$0.030
55.4%
$0.0002
by category
Single value
64.8%
Transcription
94.7%
Structured JSON
92.8%
Text localization
74.5%
Single value
47.4%
Transcription
81.5%
Structured JSON
72.0%
Text localization
18.6%
OCR (high)
84.8%
$0.043
55.3%
$0.0002
by category
Single value
77.4%
Transcription
97.9%
Structured JSON
94.1%
Text localization
72.9%
Single value
50.9%
Transcription
72.2%
Structured JSON
72.0%
Text localization
14.5%
Reasoning (low)
88.1%
±1.3, Mean of 3 runs, range 86.8 to 89.4
$0.016
51.0%
$0.0001
Reasoning (high)
92.0%
±1.0, Mean of 3 runs, range 91.4 to 93.4
$0.024
59.6%
$0.0001

Claude Fable 5.2 vs GLM 5.3 Flash: 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.

GLM 5.3 Flash

GLM-5.3-Flash is the first natively multimodal model in Z.ai's GLM-5 series, a mixture-of-experts transformer with roughly 320 billion total parameters and 18 billion activated per token. It routes each token through 8 of 288 experts across 45 language layers that interleave KDA linear attention with sparse multi-head latent attention, and pairs them with a 24-layer vision encoder that handles image and video input. The checkpoint declares a maximum context length of 1,048,576 tokens, ships in native FP8, and includes a multi-token prediction draft layer for speculative decoding. Z.ai reports that the hybrid attention design reduces attention computation by 3.01x and KV cache size by 4.44x relative to GLM-5.3.

The model starts from a newly trained base built on a 30 trillion token multimodal pre-training corpus and adopts Manifold-Constrained Hyper-Connections to improve scaling efficiency. Vision is integrated into the coding and agent loop, so the model can inspect interfaces, rendered output, and images while operating across code, browsers, and graphical user interfaces.

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% (#3 of 68) against 55.8% (#49 of 68) for GLM 5.3 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 33.1%. This is the widest gap between the two models across the benchmark's tasks.

GLM 5.3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0001 per sample against $0.031. Claude Fable 5.2 is priced at $8.00 per 1M input tokens and $40.00 per 1M output; GLM 5.3 Flash is priced at $0.04 per 1M input tokens and $0.08 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GLM 5.3 Flash is faster. Across Roboflow's Vision Evals it averaged 9.3s per inference against 9.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.