Claude Sonnet 5 vs Gemini 3.6 Flash
Compare Claude Sonnet 5 and Gemini 3.6 Flash side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.
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Models in this comparison
Claude Sonnet 5 vs Gemini 3.6 Flash on Vision Evals
Gemini 3.6 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3.6 Flash leads 80.1% to 43.0%.
Overall, Claude Sonnet 5 averages 66.4% (#21 of 30) against 83.1% (#5 of 30) for Gemini 3.6 Flash.
Gemini 3.6 Flash is both cheaper ($0.0032 vs $0.0064 per sample) and faster (4.7s vs 4.8s per sample).
Claude Sonnet 5 vs Gemini 3.6 Flash Comparison Table
Evals updated August 14, 2026Pricing updated August 18, 2026
| Property | Claude Sonnet 5 | Gemini 3.6 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.750 |
| Output $/1M | $10.00 | $3.75 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Video Classification | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 66.4% | 83.1% |
| Avg cost / sample | $0.0064 | $0.0032 |
| Avg speed / sample | 4.84s | 4.73s |
| By task | ||
| Object Detection | 36.1% $0.011 | 56.0% $0.0041 |
| Counting | 56.8% $0.0030 | 82.4% $0.0032 |
| Identification | 81.3% $0.0027 | 96.9% $0.0015 |
| OCR | 91.7% $0.0078 | 88.4% $0.0025 |
| Data Extraction | 89.7% $0.0030 | 94.8% $0.0015 |
| Reasoning (low) | 43.0% $0.0032 | 80.1% $0.0031 |
| Reasoning (high) | 43.0% $0.0043 | 80.1% $0.0085 |
Claude Sonnet 5 vs Gemini 3.6 Flash: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.6 Flash performed better. It scores higher on 5 of the six vision tasks and averages 83.1% (#5 of 30) against 66.4% (#21 of 30) for Claude Sonnet 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Gemini 3.6 Flash leads with 80.1% against 43.0%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0032 per sample against $0.0064. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Gemini 3.6 Flash is priced at $0.75 per 1M input tokens and $3.75 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.6 Flash is faster. Across Roboflow's Vision Evals it averaged 4.7s per inference against 4.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.