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Claude Sonnet 5.5 vs Gemini 3.7 Flash

Compare Claude Sonnet 5.5 and Gemini 3.7 Flash side-by-side.

Compare Claude Sonnet 5.5 vs Gemini 3.7 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.

Models in this comparison

Claude Sonnet 5.5 vs Gemini 3.7 Flash on Vision Evals

Claude Sonnet 5.5 scores higher on 3 of the six Vision Evals tasks.

The widest gap is Data Extraction, where Gemini 3.7 Flash leads 96.2% to 90.7%.

Overall, Claude Sonnet 5.5 averages 83.8% (#7 of 60) against 85.2% (#4 of 60) for Gemini 3.7 Flash.

Gemini 3.7 Flash is cheaper ($0.0031 vs $0.0065 per sample), while Claude Sonnet 5.5 is faster (10.8s vs 16.5s per sample).

Claude Sonnet 5.5Gemini 3.7 Flash

Claude Sonnet 5.5 vs Gemini 3.7 Flash Comparison Table

Evals updated September 28, 2026Pricing updated September 28, 2026

PropertyClaude Sonnet 5.5Gemini 3.7 Flash
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Aug 2026
Context Window1.0M1.0M
ParametersUndisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750
Output $/1M$3.75
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
83.8%
85.2%
Avg cost / sample$0.0065$0.0031
Avg speed / sample10.78s16.50s
By task
Object Detection (low)
74.3%
±0.9, Mean of 3 runs, range 73.5 to 75.3
$0.0098
70.5%
±1.1, Mean of 3 runs, range 69.4 to 71.5
$0.0047
Object Detection (high)
76.8%
±0.4, Mean of 3 runs, range 76.5 to 77.3
$0.014
74.3%
±0.8, Mean of 3 runs, range 73.3 to 75.0
$0.0089
Counting (low)
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0042
78.4%
±1.4, Mean of 3 runs, range 77.0 to 79.7
$0.0025
Counting (high)
82.9%
±1.4, Mean of 3 runs, range 81.1 to 83.8
$0.0053
79.3%
±2.0, Mean of 3 runs, range 77.0 to 81.1
$0.0056
Identification (low)
91.7%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0029
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
Identification (high)
90.6%
±0.0, Mean of 3 runs, range 90.6 to 90.6
$0.0033
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0021
OCR (low)
90.6%
±0.9, Mean of 3 runs, range 90.0 to 91.7
$0.0079
88.2%
±1.6, Mean of 3 runs, range 86.9 to 90.0
$0.0027
OCR (high)
90.9%
±1.5, Mean of 3 runs, range 89.2 to 92.3
$0.011
89.0%
±0.8, Mean of 3 runs, range 88.3 to 89.9
$0.0093
Data Extraction (low)
90.7%
±1.5, Mean of 3 runs, range 89.7 to 92.8
$0.0033
96.2%
±0.5, Mean of 3 runs, range 95.9 to 96.9
$0.0014
Data Extraction (high)
93.1%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.0036
95.9%
±0.0, Mean of 3 runs, range 95.9 to 95.9
$0.0023
Reasoning (low)
76.4%
±0.7, Mean of 3 runs, range 75.5 to 76.8
$0.0049
80.8%
±2.0, Mean of 3 runs, range 78.8 to 82.8
$0.0022
Reasoning (high)
83.9%
±1.7, Mean of 3 runs, range 82.1 to 85.4
$0.0061
81.9%
±1.3, Mean of 3 runs, range 80.1 to 82.8
$0.0050

Claude Sonnet 5.5 vs Gemini 3.7 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.

On visual and agentic evaluations reported at launch, Sonnet 5.5 scores 61.6% on Chartography, a chart recognition test, compared with 15.6% for Claude Sonnet 5, and 80.1% on OSWorld 2.1, a computer use benchmark measuring screenshot driven control of a desktop environment, compared with 57.0% for Sonnet 5. It reports 70.6% on Terminal-Bench 4.0 for agentic coding. 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.

Gemini 3.7 Flash

Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.

Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.7 Flash performed slightly better overall. The two split the six vision tasks 3 to 3, but Gemini 3.7 Flash averages 85.2% (#4 of 60) against 83.8% (#7 of 60) for Claude Sonnet 5.5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Data Extraction benchmark at low effort, Gemini 3.7 Flash leads with 96.2% against 90.7%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.7 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0031 per sample against $0.0065. Actual costs depend on your image sizes, prompts, and output length.

Claude Sonnet 5.5 is faster. Across Roboflow's Vision Evals it averaged 10.8s per inference against 16.5s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.