Roboflow

Claude 3.5 Haiku vs Gemma 4 31B

Gemma 4 31B: 57.7% average across four Vision Evals tasks (#41 of 65 models). Claude 3.5 Haiku is deprecated and was not run through Roboflow's Vision Evals, so there is no head-to-head score.

Compare Claude 3.5 Haiku vs Gemma 4 31B live

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

Extract and compare text from images across multiple models.

Open OCR in the full playground
AnthropicClaude 3.5 Haiku

Claude 3.5 Haiku is deprecated and can no longer be run. Details and evals are still available on its model page.

GoogleGemma 4 31B
Run to compare this model.

Claude 3.5 Haiku vs Gemma 4 31B Comparison Table

Evals updated October 26, 2026Pricing updated November 1, 2026

PropertyClaude 3.5 HaikuGemma 4 31B
OrganizationAnthropicGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateOct 2024Apr 2026
Context Window200K256K
ParametersUnknown31B
LicenseProprietaryApache 2.0
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1MNo published price$0.200
Output $/1MNo published price$0.400
Vision Tasks
CaptioningSupportedDemo
ClassificationSupportedDemo
Object DetectionSupportedDemo
OCRSupportedDemo
Visual Question AnsweringSupportedDemo
Both support 5 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks
OverallDeprecated
57.7%*
Quantizationsself-hosted
BF1657.7%FP857.7%QAT-W4A1657.6%hardware →
Avg cost / sample–$0.0015
Avg speed / sample–34.36s
By task
Object Detection–
47.5%
±0.6, Mean of 3 runs, range 46.8 to 48.0
$0
Counting–
51.4%
±2.7, Mean of 3 runs, range 48.6 to 54.0
$0
Identification–
79.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0
OCR––
by category
Reasoning–
52.8%
±1.3, Mean of 3 runs, range 51.7 to 54.3
$0

Claude 3.5 Haiku vs Gemma 4 31B: Overview

Claude 3.5 Haiku

Claude 3.5 Haiku, released by Anthropic in October 2024, is the fastest member of the Claude 3.5 family, optimized for low-latency, high-throughput applications. It is a multimodal model that handles both text and image inputs and supports a large ~200,000-token context window. Haiku is designed to balance efficiency with intelligence, outperforming even Claude 3 Opus on several reasoning benchmarks while maintaining its hallmark speed.

Typical applications include real-time chatbots, code completion, large-scale data extraction, and content moderation—scenarios where rapid response and scalability are essential.

Gemma 4 31B

Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.

For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.