Roboflow
Anthropic

Anthropic: Claude Opus 5

Claude Opus 5 Overview

Claude Opus 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at a lower cost. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.

Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.

Claude Opus 5 Interactive Demo

Model settings

Extended thinking

Max output tokens

Default 65,536 · max 65,536

Sign in to adjust thinking and output length per run.

Results appear here. Add an image or pick an example to run Claude Opus 5.

Claude Opus 5 Details & Performance

Details

Resources

Tutorial

Vision Tasks

CaptioningChart Question AnsweringClassificationDocument Question AnsweringMulti-Label ClassificationOCRObject DetectionVision LanguageVisual Question Answering

Features

Foundation VisionMultimodal VisionLLMs with Vision Capabilities

Usage

Past 30 Days

Performance

Avg. Latency

Claude Opus 5 Vision Evals

Vision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across five vision tasks, and answers are scored against ground truth.

Evals updated October 13, 2026Pricing updated October 23, 2026

Overall score#20 of 63
70.9%
Avg cost / sample#57 of 63
$0.020
Avg speed / sample#16 of 63
8.21s
Avg tokens / sample
2.6K

Strengths and weaknesses

Claude Opus 5 averages 70.9% across the five Vision Evals tasks, ranking #20 of 63 models overall.

Its weakest relative showing is Object Detection, ranking #32 of 63 at 54.4%.

At $0.020 per sample it is the 57th cheapest of the 63 benchmarked models, and its average inference time of 8.2s per sample makes it the 16th fastest.

Performance profile

Field medianClaude Opus 5

Field medians: Object Detection 54.4%, Counting 63.5%, Identification 84.4%, OCR 63.6%, Reasoning 57.6%.

Results by task

TaskScoreField (0 to 100)RankCost / sampleSpeed
Object Detection
54.4%
#32 of 63$0.02711.20s
Counting
70.3%
#19 of 63$0.00964.44s
Identification
90.6%
#17 of 63$0.00723.71s
OCR (low)
68.0%
#17 of 47$0.0228.49s
Single value
56.1%
Transcription
92.7%
Structured JSON
85.2%
Text localization
40.5%
OCR (high)
65.8%
#20 of 47$0.04318.60s
Single value
47.8%
Transcription
85.2%
Structured JSON
88.7%
Text localization
42.6%
Reasoning (low)
71.5%
#18 of 63$0.0104.99s
Reasoning (high)
74.2%
#17 of 50$0.0188.59s
  • Thinking longer does not help: 2.1 points lower on ocr at high effort for 2x the cost and 2.2x the latency.
  • Thinking longer helps: 2.7 points higher on reasoning at high effort for 1.7x the cost and 1.7x the latency.

Price vs. performance

Score vs. cost

Overall benchmark score against estimated cost per sample, on a log scale. Upper-left is the sweet spot: high quality at low cost.

62 models on the current benchmark · scores and efficiency pooled across all five tasks at low effort · Claude Opus 5 highlighted

Claude Opus 5 scores are from a single run per task; a three-run re-run under the current protocol is pending · Methodology

View all Vision Evals →

Claude Opus 5 Pricing

Claude Opus 5 costs $5.00 per 1M input tokens and $25.00 per 1M output tokens.

Input$5.00 / 1M tokens
Output$25.00 / 1M tokens
Cached input$0.500 / 1M tokens

Pricing updated Oct 23, 2026

Alternatives to Claude Opus 5

Other models worth comparing for similar use cases.

OpenAI
GPT-6.1 Sol
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
Google
Gemini 4 Argon
Gemini 4 Argon is Google DeepMind's frontier Gemini 4 model, built to sustain extended reasoning across long-horizon workflows in software engineering, enterprise knowledge work such as legal and finance, scientific analysis, and defensive cybersecurity. It accepts text, image, video, and document input and returns text, operating over a one million token context window. The model raises the maximum output length to one million tokens, compared with a 64K ceiling on earlier Gemini releases, so a single reasoning trajectory can run for hundreds of thousands of tokens before terminating.On the visual side, Argon targets multimodal document comprehension, professional chart analysis, and retrieval of specific details from long recordings, reporting 91.7% on LVBench for long-video understanding and 71.6% on Chartography for chart reasoning. Google also reports 77.9% on DeepSWE v1.1, 51.3% on AutomationBench, 68% on CWE-bench v1, and first place on the Vals Index, which aggregates finance, coding, legal, and tax tasks. Following Gemini API conventions, it localizes 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 alongside captioning, document parsing, and visual question answering. Google describes adversarial training and automated red teaming for indirect prompt injection resistance, plus chain-of-thought and action monitors that halt execution when behavior drifts outside user intent.
Anthropic
Claude Fable 5.1
Claude Fable 5.1 is a proprietary multimodal model from Anthropic in the Mythos-class tier of the Claude 5 family, positioned above Claude Opus for demanding reasoning and long-horizon agentic work. It accepts text and images as input and returns text, with a one million token context window and a maximum output of 128 thousand tokens. Adaptive thinking is always on, and an effort parameter controls how much reasoning the model applies to a given request. Anthropic reports a reliable knowledge and training data cutoff of June 2026. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model; the difference between them is the set of safety classifiers applied to dual-use cybersecurity and biology requests.On the vision side, Anthropic documents improvements in reading dense charts, financial filings, and tables nested inside PDF documents, which extends the model toward document understanding, chart question answering, and spreadsheet and slide work. Reported evaluations cover agentic scientific research on Terminal-Bench-Science 0.1, agentic coding on Terminal-Bench 4.0, computer use on OSWorld 2.0, and multidisciplinary reasoning on Humanity's Last Exam. Model weights are not published.
MoonshotAI
Kimi K3
Kimi K3 is a sparse Mixture-of-Experts large language model developed by Moonshot AI, with 2.8 trillion total parameters and a 1-million-token context window. The model activates 16 out of 896 experts per token using the Stable LatentMoE framework, and is built on two architectural innovations: Kimi Delta Attention (KDA), a hybrid linear attention mechanism that enables up to 6.3x faster decoding in long-context settings, and Attention Residuals (AttnRes), which selectively retrieves representations across model depth and delivers roughly 25% higher training efficiency. Together with refined training and data recipes, these structural advances yield approximately 2.5x better overall scaling efficiency compared to its predecessor Kimi K2. The model applies quantization-aware training from the supervised fine-tuning stage onward, using MXFP4 weights with MXFP8 activations for hardware compatibility. Thinking mode is always enabled at launch, with reasoning effort configurable via the reasoning_effort field.Kimi K3 supports native visual understanding alongside text, accepting image inputs for tasks that combine software engineering and visual reasoning. It targets long-horizon coding, knowledge work, and agentic workflows, and ships in two variants: K3 Max for general chat and agent tasks, and K3 Swarm Max for large-scale parallel processing across many coordinated sub-agents. The model is compatible with the OpenAI SDK via an OpenAI-compatible API. Full model weights are scheduled for release by July 27, 2026 under a Modified MIT license, following the open-weight pattern established by the Kimi K2 model family. A technical report with full architecture, training, and evaluation details is expected to accompany the weights release.
Grok
Grok 4.7
Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.

Other Anthropic Opus models

Other versions in the same family as Claude Opus 5.

AnthropicClaude Opus 5.5AnthropicClaude Opus 4.8AnthropicClaude Opus 4.7AnthropicClaude Opus 4.6 AnthropicClaude Opus 4.5

Deploy Claude Opus 5 with an API

Claude Opus 5 runs as a hosted REST endpoint through Roboflow Workflows. Pick a task, then hand the prompt to your coding agent or copy the code. Deploying the workflow into a free Roboflow workspace replaces the your-workspace and YOUR_API_KEY placeholders with your own.

Connect your agent to Roboflow (once)

Add the Roboflow MCP server

claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp

Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.

Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).

Deploy this workflow to your Roboflow workspace to use it.

Integrate the Roboflow "Claude Opus 5" workflow into my app.

- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/claude-opus-5-captioning
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value } } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.

With the Roboflow MCP connected, call `workflows_get` on "claude-opus-5-captioning" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.

Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.

Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.
Installpip install inference-sdk

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workflow
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="YOUR_API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="your-workspace",
  workflow_id="claude-opus-5-captioning",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Deploy this workflow to your Roboflow workspace to use it.

// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/claude-opus-5-captioning', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    api_key: 'YOUR_API_KEY',
    inputs: {
      "image": {"type": "url", "value": "IMAGE_URL"}
    }
  })
});

const result = await response.json();
console.log(result);

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/claude-opus-5-captioning' \
--header 'Content-Type: application/json' \
--data '{
  "api_key": "YOUR_API_KEY",
  "inputs": {
    "image": {"type": "url", "value": "IMAGE_URL"}
  }
}'

Claude Opus 5 License

Proprietary

Claude Opus 5 is proprietary: the weights are not distributed, and the Claude Opus 5 license is the vendor's commercial terms of service that you accept when you call the API.

Commercial use
Permitted under the vendor terms, typically metered per token or per request, with the vendor usage policy applying to your inputs and outputs.
Modification
Not available. Claude Opus 5 weights are closed, so you can configure prompts and use vendor-hosted fine-tuning where it is offered, but you cannot modify the model itself.
Redistribution
Not permitted. You cannot self-host or resell the model; you build on the hosted API instead.

Vendor terms govern data retention, whether your inputs can be trained on, rate limits, and regional availability, and they can change with notice. Review them if you handle regulated or customer data.

Do I need a commercial license for Claude Opus 5?

Proprietary terms are set by the vendor rather than negotiated per project, and no open-source obligation attaches to your code. If you would rather deploy an open model instead of Claude Opus 5, Roboflow's licensing page lists the models whose commercial license is included when you deploy on Roboflow.

Do not hesitate to reach out with questions for your commercial project — our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.

Talk to sales

License information is provided as a guide and is not legal advice.

Frequently Asked Questions About Claude Opus 5 Vision

Yes. Claude Opus 5 accepts image input and handles OCR, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Identification at 90.6% (#17 of 63 at low effort). You can test it on your own image in the demo above.

Yes. it scores 68% (#17 of 47 at low effort) on Vision Evals OCR, which covers single readings, full transcriptions, structured JSON, and locating text with boxes. Within that, full transcriptions score 92.7%, and returning the fields of a label or form as JSON scores 85.2%.

Not its strength. On Vision Evals, Claude Opus 5 scores 54.4% mAP@50 on object detection (#32 of 63 at low effort) and 70.3% judge-graded accuracy on object counting. For production counting or precise localization, pairing it with a specialized detector like RF-DETR or your own trained model in a Roboflow Workflow is usually more reliable: detect the objects, then count the detections.

On our benchmark's task mix, Claude Opus 5 averages $0.02 per sample at $5.00 per 1M input and $25.00 per 1M output tokens (#57 of 63 on cost), with an average speed of 8.2s per sample across the benchmark. Actual cost depends on your images and prompts.

On the overall Vision Evals ranking, Claude Opus 5 sits #20 of 63 at 70.9%, just behind Claude Fable 5 (71.7%) and just ahead of GPT-6 Luna (69.7%). See the full side-by-side: Claude Opus 5 vs Claude Fable 5.