Claude Haiku 5.5 vs Gemini 3.5 Flash
Compare Claude Haiku 5.5 and Gemini 3.5 Flash side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.
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Models in this comparison
Claude Haiku 5.5 vs Gemini 3.5 Flash on Vision Evals
Gemini 3.5 Flash scores higher on all five Vision Evals tasks.
The widest gap is Identification, where Gemini 3.5 Flash leads 99.0% to 83.3%.
Overall, Claude Haiku 5.5 averages 69.6% (#22 of 63) against 81.8% (#4 of 63) for Gemini 3.5 Flash.
Claude Haiku 5.5 is both cheaper ($0.0006 vs $0.012 per sample) and faster (11.0s vs 11.5s per sample).
Claude Haiku 5.5 vs Gemini 3.5 Flash Comparison Table
Evals updated October 13, 2026Pricing updated October 19, 2026
| Property | Claude Haiku 5.5 | Gemini 3.5 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | May 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $1.50 |
| Output $/1M | $0.500 | $9.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 69.6% | 81.8% |
| Avg cost / sample | $0.0006 | $0.012 |
| Avg speed / sample | 11.01s | 11.52s |
| By task | ||
| Object Detection (low) | 65.8% ±0.6, Mean of 3 runs, range 65.1 to 66.2 | 70.6% ±2.0, Mean of 3 runs, range 68.7 to 72.6 |
| Object Detection (high) | 68.2% ±1.0, Mean of 3 runs, range 67.2 to 69.2 | 69.8% ±1.8, Mean of 3 runs, range 67.5 to 71.1 |
| Counting (low) | 68.9% ±4.7, Mean of 3 runs, range 64.9 to 74.3 | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 |
| Counting (high) | 73.0% ±1.3, Mean of 3 runs, range 71.6 to 74.3 | 82.4% ±0.0, Mean of 3 runs, range 82.4 to 82.4 |
| Identification (low) | 83.3% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| Identification (high) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| OCR (low) | 61.2% | 76.7% |
| by category |
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| OCR (high) | 65.3% | 75.5% |
| by category |
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| Reasoning (low) | 68.9% ±2.0, Mean of 3 runs, range 66.9 to 70.9 | 82.1% ±2.0, Mean of 3 runs, range 80.1 to 84.1 |
| Reasoning (high) | 74.8% ±2.6, Mean of 3 runs, range 72.2 to 77.5 | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 |
Claude Haiku 5.5 vs Gemini 3.5 Flash: Overview
Claude Haiku 5.5 is a proprietary multimodal language model from Anthropic and the smallest member of the Claude 5.5 family, released on October 7, 2026 after Claude Opus 5.5 and Claude Sonnet 5.5. It accepts text and image input and returns text, with a 1M token context window and up to 128K output tokens per request. It is the first Haiku-class model with an adjustable effort parameter: adaptive thinking is on by default and the model decides how much to reason, steered by effort levels from low to max with medium as the default. Its training data cutoff is June 2026. Pricing starts at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100K tokens, which Anthropic reports is about 90% lower than Claude Haiku 4.5 for requests in that range.
Anthropic positions Haiku 5.5 for high-volume, latency-sensitive work such as classification, extraction, routing, summarization, and subagent tasks, and describes it as its fastest model to date at standard speed. On visual and agentic evaluations reported at launch, it scores 46.4% on Chartography, a chart reading benchmark, compared with 6.4% for Haiku 4.5 and 61.6% for Sonnet 5.5, and 72.4% on the offline subset of OSWorld 2.1, a screenshot driven computer use benchmark, compared with 15.7% for Haiku 4.5. It uses the same tokenizer as Claude Opus 4.7 and later models, so the same text counts as roughly 30% more tokens than on Haiku 4.5.
Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.
Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.5 Flash performed better. It scores higher on all five vision tasks and averages 81.8% (#4 of 63) against 69.6% (#22 of 63) for Claude Haiku 5.5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Identification benchmark at low effort, Gemini 3.5 Flash leads with 99.0% against 83.3%. This is the widest gap between the two models across the benchmark's tasks.
Claude Haiku 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0006 per sample against $0.012. Claude Haiku 5.5 is priced at $0.10 per 1M input tokens and $0.50 per 1M output; Gemini 3.5 Flash is priced at $1.50 per 1M input tokens and $9.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Haiku 5.5 is faster. Across Roboflow's Vision Evals it averaged 11.0s per inference against 11.5s. 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 image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.