Claude Sonnet 4.5 vs Gemini 3.7 Flash
Gemini 3.7 Flash: 80.1% average across five Vision Evals tasks (#10 of 69 models). We have not run Claude Sonnet 4.5 through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Claude Sonnet 4.5 vs Gemini 3.7 Flash Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Claude Sonnet 4.5 | Gemini 3.7 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2025 | Aug 2026 |
| Context Window | 200K | 1.0M |
| Parameters | Unknown | Undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.750 |
| Output $/1M | $15.00 | $3.75 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 80.1% |
| Avg cost / sample | – | $0.0033 |
| Avg speed / sample | – | 11.04s |
| By task | ||
| Object Detection (low) | – | 70.5% ±1.1, Mean of 3 runs, range 69.4 to 71.5 |
| Object Detection (high) | – | 74.3% ±0.8, Mean of 3 runs, range 73.3 to 75.0 |
| Counting (low) | – | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 |
| Counting (high) | – | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 |
| Identification (low) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | – | 73.9% |
| by category |
| |
| OCR (high) | – | 78.8% |
| by category |
| |
| Reasoning (low) | – | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 |
| Reasoning (high) | – | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 |
Claude Sonnet 4.5 vs Gemini 3.7 Flash: Overview
Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.
It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.
Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.
Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.