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Claude Opus 4.8 vs Gemini 3.7 Flash

Compare Claude Opus 4.8 and Gemini 3.7 Flash side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.

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AnthropicClaude Opus 4.8
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GoogleGemini 3.7 Flash
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Models in this comparison

Claude Opus 4.8 vs Gemini 3.7 Flash on Vision Evals

Gemini 3.7 Flash scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where Gemini 3.7 Flash leads 70.5% to 38.6%.

Overall, Claude Opus 4.8 averages 58.3% (#38 of 63) against 80.1% (#6 of 63) for Gemini 3.7 Flash.

Gemini 3.7 Flash is cheaper ($0.0033 vs $0.021 per sample), while Claude Opus 4.8 is faster (7.7s vs 11.0s per sample).

Claude Opus 4.8Gemini 3.7 Flash

Claude Opus 4.8 vs Gemini 3.7 Flash Comparison Table

Evals updated October 13, 2026Pricing updated October 16, 2026

PropertyClaude Opus 4.8Gemini 3.7 Flash
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Aug 2026
Context Window1.0M1.0M
ParametersUnknownUndisclosed
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$5.00$0.750
Output $/1M$25.00$3.75
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
58.3%
80.1%
Avg cost / sample$0.021$0.0033
Avg speed / sample7.66s11.04s
By task
Object Detection (low)
38.6%
$0.026
70.5%
±1.1, Mean of 3 runs, range 69.4 to 71.5
$0.0047
Object Detection (high)–
74.3%
±0.8, Mean of 3 runs, range 73.3 to 75.0
$0.0089
Counting (low)
54.0%
$0.0076
78.4%
±1.4, Mean of 3 runs, range 77.0 to 79.7
$0.0025
Counting (high)–
79.3%
±2.0, Mean of 3 runs, range 77.0 to 81.1
$0.0056
Identification (low)
84.4%
$0.0067
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
Identification (high)–
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0021
OCR (low)
61.5%
$0.025
73.9%
$0.0032
by category
Single value
53.0%
Transcription
70.8%
Structured JSON
82.4%
Text localization
24.2%
Single value
74.8%
Transcription
91.1%
Structured JSON
86.7%
Text localization
29.4%
OCR (high)
62.1%
$0.038
78.8%
$0.010
by category
Single value
54.8%
Transcription
67.0%
Structured JSON
83.5%
Text localization
23.6%
Single value
71.7%
Transcription
91.9%
Structured JSON
90.6%
Text localization
59.2%
Reasoning (low)
53.0%
$0.0078
80.8%
±2.0, Mean of 3 runs, range 78.8 to 82.8
$0.0022
Reasoning (high)
52.3%
$0.0078
81.9%
±1.3, Mean of 3 runs, range 80.1 to 82.8
$0.0050

Claude Opus 4.8 vs Gemini 3.7 Flash: Overview

Claude Opus 4.8

Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.

Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.

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 better. It scores higher on all five vision tasks and averages 80.1% (#6 of 63) against 58.3% (#38 of 63) for Claude Opus 4.8. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark at low effort, Gemini 3.7 Flash leads with 70.5% against 38.6%. 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 $0.0033 per sample against $0.021. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; Gemini 3.7 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 Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 7.7s per inference against 11.0s. 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.