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Claude Sonnet 5.5 vs GLM 5.3 Flash

Claude Sonnet 5.5 and GLM 5.3 Flash score 77.6% vs 55.8% averaged across five Vision Evals tasks (#14 and #50 of 69 models), so Claude Sonnet 5.5 leads overall.

  • Claude Sonnet 5.5 wins all 5 tasks, with its widest lead on object detection (74.3% vs 33.1%).
  • GLM 5.3 Flash is under a tenth of the cost: $0.0001 vs $0.0073 overall average cost per sample.
  • Claude Sonnet 5.5 is faster: 8.3s vs 9.3s per sample.

Compare Claude Sonnet 5.5 vs GLM 5.3 Flash live

Run the same image across every model that supports a task and compare their outputs side-by-side.

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AnthropicClaude Sonnet 5.5
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Z.aiGLM 5.3 Flash
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Claude Sonnet 5.5 vs GLM 5.3 Flash on Vision Evals

Score by taskClaude Sonnet 5.5GLM 5.3 Flash
Object Detection
74.3
33.1
Reasoning
76.4
51.0
Counting
79.3
55.4
OCR
66.3
55.4
Identification
91.7
84.4

Roboflow Vision Evals. Full breakdown below.

Claude Sonnet 5.5 vs GLM 5.3 Flash Comparison Table

Evals updated November 12, 2026Pricing updated November 13, 2026

PropertyClaude Sonnet 5.5GLM 5.3 Flash
OrganizationAnthropicZ.ai
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 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$2.00$0.040
Output $/1M$10.00$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
77.6%
55.8%
Avg cost / sample$0.0073$0.0001
Avg speed / sample8.33s9.29s
By task
Object Detection (low)
74.3%
±0.9, Mean of 3 runs, range 73.5 to 75.3
$0.0098
33.1%
$0.0002
Object Detection (high)
76.8%
±0.4, Mean of 3 runs, range 76.5 to 77.3
$0.014
–
Counting (low)
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0042
55.4%
$0.0001
Counting (high)
82.9%
±1.4, Mean of 3 runs, range 81.1 to 83.8
$0.0053
–
Identification (low)
91.7%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0029
84.4%
$0.0001
Identification (high)
90.6%
±0.0, Mean of 3 runs, range 90.6 to 90.6
$0.0033
–
OCR (low)
66.3%
$0.0075
55.4%
$0.0002
by category
Single value
48.7%
Transcription
76.3%
Structured JSON
83.8%
Text localization
60.6%
Single value
47.4%
Transcription
81.5%
Structured JSON
72.0%
Text localization
18.6%
OCR (high)–
55.3%
$0.0002
by category
Single value
50.9%
Transcription
72.2%
Structured JSON
72.0%
Text localization
14.5%
Reasoning (low)
76.4%
±0.7, Mean of 3 runs, range 75.5 to 76.8
$0.0049
51.0%
$0.0001
Reasoning (high)
83.9%
±1.7, Mean of 3 runs, range 82.1 to 85.4
$0.0061
59.6%
$0.0001

Claude Sonnet 5.5 vs GLM 5.3 Flash: Overview

Claude Sonnet 5.5

Claude Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.

Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.

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 Sonnet 5.5 performed better. It scores higher on all five vision tasks and averages 77.6% (#14 of 69) against 55.8% (#50 of 69) 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 Sonnet 5.5 leads with 74.3% 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 $0.0073. Claude Sonnet 5.5 is priced at $2.00 per 1M input tokens and $10.00 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.

Claude Sonnet 5.5 is faster. Across Roboflow's Vision Evals it averaged 8.3s 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.