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Gemini 3.1 Flash-Lite vs GPT-5.6 Sol

Compare Gemini 3.1 Flash-Lite and GPT-5.6 Sol side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, Open Prompt, and OCR.

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GoogleGemini 3.1 Flash-Lite
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Gemini 3.1 Flash-Lite vs GPT-5.6 Sol: Overview

Gemini 3.1 Flash-Lite

Gemini 3.1 Flash-Lite is a natively multimodal reasoning model from Google DeepMind in the Gemini 3 series, based on the Gemini 3 Pro architecture. It processes text, image, video, audio, and PDF inputs within a 1 million token context window and produces text output up to 64K tokens. The model targets high-volume, latency-sensitive workloads and supports visual question answering, image and document data extraction, content moderation, classification, translation, automated speech recognition, and agentic data pipelines. It exposes configurable thinking levels of minimal, low, medium, and high, which set the depth of internal reasoning applied per request and let developers balance response quality against cost and latency.

On benchmarks reported at launch, Gemini 3.1 Flash-Lite scores 86.9% on GPQA Diamond and 76.8% on the MMMU Pro multimodal benchmark, and reaches an Elo score of 1432 on the Arena.ai leaderboard. According to Artificial Analysis benchmarks, it produces a 2.5 times faster time to first answer token and a 45% increase in output speed relative to Gemini 2.5 Flash. It also shows improved instruction following, higher audio input quality for automated speech recognition tasks, and support for structured JSON output used in data extraction pipelines.

GPT-5.6 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.

GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.

Gemini 3.1 Flash-Lite vs GPT-5.6 Sol Comparison Table

PropertyGemini 3.1 Flash-LiteGPT-5.6 Sol
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Jul 2026
Context Window1.0M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.250$5.00
Output $/1M$1.50$30.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Document Question Answering
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Chart Question Answering
Image Tagging
Multi-Label Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
68.66%
76.12%
Avg Response Time1.86s8.41s
Median input tokensincl. image tokens1.1K1.4K
Median output tokens612
Est. cost / taskon this benchmark$0.0003$0.0073
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
30%(3/10)
30%(3/10)
Object Understanding
64.3%(9/14)
85.7%(12/14)
Spatial Understanding
84.2%(16/19)
89.5%(17/19)
OCR
Overall Score
89.96%
78.6%
Avg Response Time1.32s3.23s
Median input tokensincl. image tokens1.1K105
Median output tokens1022
Est. cost / taskon this benchmark$0.0003$0.0012
Focused Scene OCR
91.9%(91/99)
73.7%(73/99)
Handwritten Math
80%(8/10)
60%(6/10)
License Plate Recognition
100%(30/30)
90%(27/30)
Text Recognition
90%(27/30)
90%(27/30)
VQA & Extraction
83.3%(50/60)
78.3%(47/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology