Claude Sonnet 4.6 vs Gemini 3.1 Flash-Lite
Compare Claude Sonnet 4.6 and Gemini 3.1 Flash-Lite side-by-side. See how these vision models stack up in Image Captioning, Classification, Open Prompt, Object Detection, and OCR.
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Claude Sonnet 4.6 vs Gemini 3.1 Flash-Lite: Overview
Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.
Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.
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.
Claude Sonnet 4.6 vs Gemini 3.1 Flash-Lite Comparison Table
| Property | Claude Sonnet 4.6 | Gemini 3.1 Flash-Lite |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Mar 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.250 |
| Output $/1M | $15.00 | $1.50 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 70.15% | 68.66% |
| Avg Response Time | 4.24s | 1.86s |
| Median input tokensincl. image tokens | 2.2K | 1.1K |
| Median output tokens | 105 | 6 |
| Est. cost / taskon this benchmark | $0.0080 | $0.0003 |
| Defect Detection | 80%(12/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 77.8%(7/9) |
| Object Counting | 30%(3/10) | 30%(3/10) |
| Object Understanding | 71.4%(10/14) | 64.3%(9/14) |
| Spatial Understanding | 78.9%(15/19) | 84.2%(16/19) |
| OCR | ||
| Overall Score | 81.66% | 89.96% |
| Avg Response Time | 3.42s | 1.32s |
| Median input tokensincl. image tokens | 736 | 1.1K |
| Median output tokens | 85 | 10 |
| Est. cost / taskon this benchmark | $0.0035 | $0.0003 |
| Focused Scene OCR | 85.9%(85/99) | 91.9%(91/99) |
| Handwritten Math | 50%(5/10) | 80%(8/10) |
| License Plate Recognition | 90%(27/30) | 100%(30/30) |
| Text Recognition | 86.7%(26/30) | 90%(27/30) |
| VQA & Extraction | 73.3%(44/60) | 83.3%(50/60) |
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