What is Claude Opus 4.6?
Claude Opus 4.6 is Anthropic's February 2026 Opus model for coding, reasoning, research, long-context documents, and vision input. It sits between Opus 4.5 and the later Opus 4.7 and 4.8 releases, and runs on Unifically as anthropic/claude-opus-4-6. The important change is not a new style of chat. It is a bigger, more useful work model: 1M tokens of context, up to 128k output tokens, better long-context retrieval, stronger agentic coding, and more control over reasoning effort. Use it when the task has many moving parts: a large codebase, dense source documents, financial analysis, multi-step research, or visual inputs that need careful reasoning.
Key features of Claude Opus 4.6
1M token context window
Very large prompts and document sets fit in one call. Recall is strong deep into the window: it scores 76.0% on the 1M-token, 8-needle MRCR v2 retrieval test.
128k max output
Long reports, code changes, and structured analysis come back in fewer turns instead of being stitched together across requests.
Adaptive thinking
The model decides when deeper reasoning is useful, rather than forcing the same reasoning depth on every turn.
Effort controls
Four levels (low, medium, high, max) trade speed for more careful reasoning inside the same model.
Stronger coding and debugging
It scores 65.4% on Terminal-Bench 2.0, a benchmark of real terminal tasks, with gains in planning, code review, debugging, and larger codebase work.
Top document and search rankings
On Arena, the thinking variant ranks #1 on the Document board at 1505±7 and the search variant ranks #1 on the Search board at 1253±5.
Best for
Large codebase review
Migration planning and bug diagnosis across a whole repository.
Long document analysis
Contracts, filings, research papers, or policy packs compared in one context.
Multi-step research
Source synthesis with careful citations and hard-to-find facts.
Professional knowledge work
Finance and legal first drafts built from filings and spreadsheets.
Vision reasoning
Charts, diagrams, screenshots, and scientific figures as input.
Long-session agents
Goals held across long traces without losing the thread.
Use cases
Build a code review agent that reads a whole repository, finds risky changes, and writes a focused review. Build a document analyst that compares long contracts, extracts obligations, and flags conflicts. Build a research workflow that searches, reads, and summarizes many sources into a traceable brief. Or turn filings and spreadsheets into a first-pass finance memo for human review. Opus 4.6 is strongest when the job needs context, planning, and judgment more than raw response speed.
Limitations
Opus 4.6 can spend more reasoning than needed on simple prompts. Use lower effort for routine extraction, classification, or short answers.
Large context is not a free pass to paste everything. Recall can degrade as context grows, so curated inputs still matter.
Safety testing also flags over-eager behavior in some agentic GUI and computer-use settings. Give it clear permission boundaries when actions can change files, accounts, data, or external systems.
High-stakes legal, financial, medical, and security work still needs human review. The model is good at first-pass analysis, not final accountability.
Claude Opus 4.6 vs Claude Opus 4.5
Claude Opus 4.6 is the better choice for long-context and long-running work. It adds a 1M-token context window, 128k output, adaptive thinking, and the max effort level. Opus 4.5 has a smaller 200k context window and 64k max output.
The benchmark picture is also clear: Opus 4.6 improves over Opus 4.5 on Terminal-Bench 2.0, ARC-AGI-2, OpenRCA, OSWorld-Verified, long-context retrieval, and BrowseComp. SWE-bench Verified is roughly flat, so the upgrade is less about one more coding point and more about deeper context and longer work.
When to use Claude Opus 4.6
Use Claude Opus 4.6 when the task is too large or too ambiguous for a faster general model. It is a good fit for careful plans, deep code understanding, dense document work, and research where missing one detail matters. If the task is short, repetitive, or latency-sensitive, a smaller model is usually enough.
API examples
Call Claude Opus 4.6 from any language by POSTing to /v1/chat/completions, the OpenAI-compatible endpoint shared by every language model on the platform. Full parameter docs live at docs.unifically.com/models/llm/anthropic/claude-opus-4-6.
curl -X POST https://api.unifically.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "anthropic/claude-opus-4-6",
"messages": [
{ "role": "user", "content": "Compare these two contract drafts and list every obligation that changed." }
]
}'
The response comes back synchronously with the completion. Set "stream": true to receive tokens as they generate.
FAQs
People also ask
anthropic/claude-opus-4-6, called through the OpenAI-compatible POST /v1/chat/completions endpoint with your Unifically API key.
Text and image input, with text output. It is a reasoning and analysis model, not an image, audio, or video generation model.
1M tokens. That covers large document sets, long agent traces, and codebases with many related files.
Up to 128k output tokens in a single response. Use it for long reports, large structured outputs, and multi-part technical writeups.
It lets the model decide when deeper reasoning is useful. It pairs with effort levels, so you can ask for lighter or deeper reasoning without changing models.
low, medium, high, and max. Use max for the hardest reasoning and planning work; use lower settings for simple or repeated tasks.
For long-context work, agentic coding, document analysis, and research, yes. It adds a 1M context window (up from 200k), 128k output (up from 64k), adaptive thinking, and stronger results across many evaluations.
For simple, high-volume tasks where a smaller model answers well. Also be careful with autonomous actions - give clear rules, require review for risky steps, and keep humans in control of high-impact decisions.
