Anthropic's most capable generally available model for agentic coding, long-horizon autonomy, and high-resolution document and vision work.
Output Speed
Intelligence Index
Context Window
Input price
Output price
Performance
Where Claude Opus 4.7 Pulls Ahead: Agentic Coding and Fault Recovery
Benchmarks
Independent and Reported Benchmarks: Coding, Tool Use, and Vision
Output Speed
Intelligence Index
MMLU
GPQA
HLE
LiveCodeBench
Technical Specifications
What the model supports
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"model": "anthropic/claude-opus-4.7",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer your_api_key",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)
const options = {
method: 'POST',
headers: {Authorization: 'Bearer your_api_key', 'Content-Type': 'application/json'},
body: JSON.stringify({
model: 'anthropic/claude-opus-4.7',
messages: [{role: 'user', content: 'Hello'}]
})
};
fetch('https://api.anyapi.ai/v1/chat/completions', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer your_api_key' \
--header 'Content-Type: application/json' \
--data '
{
"model": "anthropic/claude-opus-4.7",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
'
Comparison
Claude Opus 4.7 vs GPT-5.4: Which for Coding and Research Agents?
Both Claude Opus 4.7 and GPT-5.4 are frontier models teams evaluate for autonomous agents and complex engineering. They overlap heavily: strong reasoning, tool calling, vision input, and long context. The practical decision usually comes down to workload shape. Opus 4.7 is tuned for agentic coding and multi-step execution with fault recovery, while GPT-5.4 is stronger on browsing-heavy web research. Both are premium reasoning tiers, so cost sensitivity rarely separates them; capability fit on your specific task distribution does.
Choose Claude Opus 4.7 when your core workload is agentic software engineering, multi-tool orchestration, computer use, or high-resolution document and vision analysis—it leads SWE-bench Pro and Verified and recovers from mid-run tool failures. Choose GPT-5.4 when browsing-heavy research agents dominate, where it leads BrowseComp by roughly ten points and holds a narrow edge on Humanity's Last Exam with tools. For agentic coding inside Claude Code, Cursor, or custom pipelines, Opus 4.7 is the stronger default.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Autonomous coding agents
Opus 4.7 is the default choice for long-horizon coding agents in Claude Code, Cursor, and custom pipelines. It leads SWE-bench Verified (87.6%) and SWE-bench Pro (64.3%), improves fault localization and failing-test interpretation, and hallucinates fewer non-existent APIs. Critically, it continues through mid-run tool failures rather than halting, which matters for CI/CD automation and hours-long async workflows where cascading failures are costly. Expect higher latency and token use than Sonnet.
Large-codebase and multi-day project context
The 1M-token context window—carrying no long-context premium—lets Opus 4.7 reason over entire codebases and carry multi-day project state forward without frequent compaction. This suits repository-wide refactors, cross-file debugging, and agents that must hold accumulated context across a long session. Gemini 3.1 Pro offers a larger 2M window, so extreme single-prompt context needs may point elsewhere, but 1M covers most enterprise engineering workloads.
High-resolution document and vision analysis
Opus 4.7 processes images up to 2,576 pixels on the long edge (~3.75MP), more than three times prior Claude limits, and reports 82.1% on CharXiv visual reasoning. This targets enterprise document work: scanned contracts, technical drawings, financial statements, and dense charts where fine print and detail defeat lower-resolution vision. It reads and interprets these documents but returns text, so it fits analysis and extraction rather than visual generation.
Enterprise knowledge and financial work
Opus 4.7 scores 64.4% on Finance Agent v1.1 and self-verifies its output—re-reading generated models, spreadsheets, and redlined documents against the original request before delivering. Integrators report it correctly flags missing data instead of providing plausible-but-incorrect fallbacks and resists dissonant-data traps. This suits financial analysis, research, and document generation where quiet errors are expensive, though outputs still require review for high-stakes filings.