Claude Sonnet 5 vs Gemini 3.8 Flash
Compare Claude Sonnet 5 and Gemini 3.8 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 3.8 Flash on Vision Evals
Gemini 3.8 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3.8 Flash leads 81.2% to 43.0%.
Overall, Claude Sonnet 5 averages 66.4% (#31 of 52) against 85.1% (#3 of 52) for Gemini 3.8 Flash.
Gemini 3.8 Flash is cheaper ($0.0033 vs $0.0064 per sample), while Claude Sonnet 5 is faster (4.8s vs 11.6s per sample).
Claude Sonnet 5 vs Gemini 3.8 Flash Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Claude Sonnet 5 | Gemini 3.8 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Sep 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 |
| 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% | 85.1% |
| Avg cost / sample | $0.0064 | $0.0033 |
| Avg speed / sample | 4.84s | 11.65s |
| By task | ||
| Object Detection (low) | 36.1% | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 |
| Object Detection (high) | – | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 |
| Counting (low) | 56.8% | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 |
| Counting (high) | – | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Identification (low) | 81.3% | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| Identification (high) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 91.7% | 87.3% ±0.8, Mean of 3 runs, range 86.5 to 88.2 |
| OCR (high) | – | 88.8% ±0.7, Mean of 3 runs, range 88.0 to 89.4 |
| Data Extraction (low) | 89.7% | 97.3% ±0.5, Mean of 3 runs, range 96.9 to 97.9 |
| Data Extraction (high) | – | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 |
| Reasoning (low) | 43.0% | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 |
| Reasoning (high) | 43.0% | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 |
Claude Sonnet 5 vs Gemini 3.8 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 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.8 Flash performed better. It scores higher on 5 of the six vision tasks and averages 85.1% (#3 of 52) against 66.4% (#31 of 52) 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 3.8 Flash leads with 81.2% against 43.0%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.0064. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Gemini 3.8 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.
Claude Sonnet 5 is faster. Across Roboflow's Vision Evals it averaged 4.8s per inference against 11.6s. 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.