Claude Opus 4.6 vs Gemini 3.1 Flash-Lite
Compare Claude Opus 4.6 and Gemini 3.1 Flash-Lite side-by-side. See how these vision models stack up in Open Prompt, OCR, Object Detection, Classification, and Image Captioning.
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Claude Opus 4.6 vs Gemini 3.1 Flash-Lite: Overview
Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.
As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.
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 Opus 4.6 vs Gemini 3.1 Flash-Lite Comparison Table
| Property | Claude Opus 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 | $5.00 | $0.250 |
| Output $/1M | $25.00 | $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 | 64.18% | 68.66% |
| Avg Response Time | 23.35s | 1.86s |
| Median input tokensincl. image tokens | 2.2K | 1.1K |
| Median output tokens | 130 | 6 |
| Est. cost / taskon this benchmark | $0.014 | $0.0003 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 77.8%(7/9) |
| Object Counting | 20%(2/10) | 30%(3/10) |
| Object Understanding | 71.4%(10/14) | 64.3%(9/14) |
| Spatial Understanding | 68.4%(13/19) | 84.2%(16/19) |
| OCR | ||
| Overall Score | 82.53% | 89.96% |
| Avg Response Time | 5.05s | 1.32s |
| Median input tokensincl. image tokens | 736 | 1.1K |
| Median output tokens | 99 | 10 |
| Est. cost / taskon this benchmark | $0.0062 | $0.0003 |
| Focused Scene OCR | 85.9%(85/99) | 91.9%(91/99) |
| Handwritten Math | 70%(7/10) | 80%(8/10) |
| License Plate Recognition | 90%(27/30) | 100%(30/30) |
| Text Recognition | 80%(24/30) | 90%(27/30) |
| VQA & Extraction | 76.7%(46/60) | 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