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Claude Haiku 5.5 vs Gemini 3.1 Pro

Compare Claude Haiku 5.5 and Gemini 3.1 Pro side-by-side.

Compare Claude Haiku 5.5 vs Gemini 3.1 Pro 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 Haiku 5.5 vs Gemini 3.1 Pro on Vision Evals

Gemini 3.1 Pro scores higher on all six Vision Evals tasks.

The widest gap is Identification, where Gemini 3.1 Pro leads 100.0% to 83.3%.

Overall, Claude Haiku 5.5 averages 77.2% (#20 of 61) against 83.3% (#9 of 61) for Gemini 3.1 Pro.

Claude Haiku 5.5 is cheaper ($0.0005 vs $0.0093 per sample), while Gemini 3.1 Pro is faster (7.8s vs 13.2s per sample).

Claude Haiku 5.5Gemini 3.1 Pro

Claude Haiku 5.5 vs Gemini 3.1 Pro Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyClaude Haiku 5.5Gemini 3.1 Pro
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2026Feb 2026
Context Window1.0M1.0M
ParametersundisclosedUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100$2.00
Output $/1M$0.500$12.00
Vision Tasks
CaptioningSupportedDemo
Chart Question AnsweringSupportedSupported
ClassificationSupportedDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionSupportedDemo
OCRSupportedDemo
Vision LanguageSupportedSupported
Visual Question AnsweringSupportedDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
77.2%
83.3%
Avg cost / sample$0.0005$0.0093
Avg speed / sample13.23s7.81s
By task
Object Detection (low)
65.8%
±0.6, Mean of 3 runs, range 65.1 to 66.2
$0.0006
67.4%
$0.010
Object Detection (high)
68.2%
±1.0, Mean of 3 runs, range 67.2 to 69.2
$0.0010
–
Counting (low)
68.9%
±4.7, Mean of 3 runs, range 64.9 to 74.3
$0.0003
71.6%
$0.0071
Counting (high)
73.0%
±1.3, Mean of 3 runs, range 71.6 to 74.3
$0.0004
–
Identification (low)
83.3%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0002
100.0%
$0.0070
Identification (high)
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0003
–
OCR (low)
90.1%
±1.1, Mean of 3 runs, range 88.8 to 91.0
$0.0006
92.6%
$0.0066
OCR (high)
88.0%
±1.3, Mean of 3 runs, range 87.0 to 89.6
$0.0009
–
Data Extraction (low)
85.9%
±1.5, Mean of 3 runs, range 84.5 to 87.6
$0.0002
95.9%
$0.0063
Data Extraction (high)
87.3%
±0.5, Mean of 3 runs, range 86.6 to 87.6
$0.0002
–
Reasoning (low)
68.9%
±2.0, Mean of 3 runs, range 66.9 to 70.9
$0.0004
72.2%
$0.012
Reasoning (high)
74.8%
±2.6, Mean of 3 runs, range 72.2 to 77.5
$0.0006
74.8%
$0.021

Claude Haiku 5.5 vs Gemini 3.1 Pro: Overview

Claude Haiku 5.5

Claude Haiku 5.5 is a proprietary multimodal language model from Anthropic and the smallest member of the Claude 5.5 family, released on October 7, 2026 after Claude Opus 5.5 and Claude Sonnet 5.5. It accepts text and image input and returns text, with a 1M token context window and up to 128K output tokens per request. It is the first Haiku-class model with an adjustable effort parameter: adaptive thinking is on by default and the model decides how much to reason, steered by effort levels from low to max with medium as the default. Its training data cutoff is June 2026. Pricing starts at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100K tokens, which Anthropic reports is about 90% lower than Claude Haiku 4.5 for requests in that range.

Anthropic positions Haiku 5.5 for high-volume, latency-sensitive work such as classification, extraction, routing, summarization, and subagent tasks, and describes it as its fastest model to date at standard speed. On visual and agentic evaluations reported at launch, it scores 46.4% on Chartography, a chart reading benchmark, compared with 6.4% for Haiku 4.5 and 61.6% for Sonnet 5.5, and 72.4% on the offline subset of OSWorld 2.1, a screenshot driven computer use benchmark, compared with 15.7% for Haiku 4.5. It uses the same tokenizer as Claude Opus 4.7 and later models, so the same text counts as roughly 30% more tokens than on Haiku 4.5.

Gemini 3.1 Pro

Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.

The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.1 Pro performed better. It scores higher on all six vision tasks and averages 83.3% (#9 of 61) against 77.2% (#20 of 61) for Claude Haiku 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 Identification benchmark at low effort, Gemini 3.1 Pro leads with 100.0% against 83.3%. This is the widest gap between the two models across the benchmark's tasks.

Claude Haiku 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0005 per sample against $0.0093. Claude Haiku 5.5 is priced at $0.10 per 1M input tokens and $0.50 per 1M output; Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 13.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.