Anthropic vs Anthropic

Claude 4.6 Sonnet vs Claude 4.6 Opus

Compare Claude 4.6 Sonnet and Claude 4.6 Opus across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.

Overview Comparison

Structured side-by-side differences for the highest-signal model metadata.

Claude 4.6 Sonnet
Claude 4.6 Opus

Provider

The entity that currently provides this model.

Claude 4.6 Sonnet Anthropic
Claude 4.6 Opus Anthropic

Model ID

The routed model identifier exposed by upstream providers.

Claude 4.6 Sonnet anthropic/claude-sonnet-4.6
Claude 4.6 Opus anthropic/claude-opus-4.6

Input Context Window

The number of tokens supported by the input context window.

Claude 4.6 Sonnet 1M tokens
Claude 4.6 Opus 1M tokens

Maximum Output Tokens

The number of tokens that can be generated by the model in a single request.

Claude 4.6 Sonnet 128,000 tokens tokens
Claude 4.6 Opus 128,000 tokens tokens

Open Source

Whether the model's code is available for public use.

Claude 4.6 Sonnet No
Claude 4.6 Opus No

Release Date

When the model was first released.

Claude 4.6 Sonnet Feb 17, 2026
Claude 4.6 Opus Feb 04, 2026

Knowledge Cut-off Date

When the model's knowledge was last updated.

Claude 4.6 Sonnet February 2026
Claude 4.6 Opus February 2026

API Providers

The providers that currently expose the model through an API.

Claude 4.6 Sonnet
OpenRouter
Claude 4.6 Opus
OpenRouter

Modalities

Types of data each model can process or return.

Claude 4.6 Sonnet
Text Image File Code
Claude 4.6 Opus
Text Image File

Pricing Comparison

Compare current token pricing before you choose the cheaper or more scalable API option.

Claude 4.6 Sonnet Anthropic
Input price $3.00 Per 1M tokens
Output price $15.00 Per 1M tokens
Claude 4.6 Opus Anthropic
Input price $5.00 Per 1M tokens
Output price $25.00 Per 1M tokens

Capabilities Comparison

See where each model overlaps, where they differ, and which one supports more of the features you care about.

Capability
Claude 4.6 Sonnet
Claude 4.6 Opus
1M Token Context Accepts up to 1 million tokens in a single request (beta), enabling reasoning across entire codebases, lengthy contracts, or dozens of documents at once.
Claude 4.6 Sonnet Supported
Claude 4.6 Opus
Adaptive Thinking Automatically adjusts the amount of reasoning effort applied based on task complexity, allocating deeper computation to harder problems and less to simpler ones.
Claude 4.6 Sonnet
Claude 4.6 Opus Supported
Advanced Coding Supports the full software development lifecycle including planning, implementation, debugging, and large-scale refactors across multiple files.
Claude 4.6 Sonnet Supported
Claude 4.6 Opus
Agentic Coding Handles long-horizon software development tasks including architecture, implementation, and deployment, with benchmark results on Terminal-Bench 2.0 cited in the model overview.
Claude 4.6 Sonnet
Claude 4.6 Opus Supported
Agentic Web Search Performs deep, multi-step web research to locate hard-to-find information, with BrowseComp cited as a benchmark for this capability in the model overview.
Claude 4.6 Sonnet
Claude 4.6 Opus Supported
Agentic Workflows Handles long-running, multi-step autonomous tasks with improved instruction following, tool selection, and error correction over extended sessions.
Claude 4.6 Sonnet Supported
Claude 4.6 Opus
Complex Reasoning Applies multi-step reasoning across rigorous multidisciplinary tasks, with performance on Humanity's Last Exam cited as a benchmark reference in the model overview.
Claude 4.6 Sonnet
Claude 4.6 Opus Supported
Computer Use Controls browsers and desktop software to navigate complex spreadsheets, fill multi-step web forms, and automate workflows that previously required human intervention.
Claude 4.6 Sonnet Supported
Claude 4.6 Opus
File
Claude 4.6 Sonnet Supported
Claude 4.6 Opus Supported
Image
Claude 4.6 Sonnet Supported
Claude 4.6 Opus Supported
Large Context Window Processes up to 1 million tokens in a single session (currently in beta), enabling analysis of entire codebases, lengthy documents, or large data sets without truncation.
Claude 4.6 Sonnet
Claude 4.6 Opus Supported
MCP Integration Compatible with Model Context Protocol (MCP) servers, enabling connection to external data sources and services through a standardized interface.
Claude 4.6 Sonnet Supported
Claude 4.6 Opus
MCP Server Support Connects to Model Context Protocol servers, allowing the model to interact with external data sources and services through a standardized interface.
Claude 4.6 Sonnet
Claude 4.6 Opus Supported
Professional Knowledge Work Handles economically valuable tasks in domains such as finance and legal analysis, with GDPval-AA cited as a benchmark evaluation in the model overview.
Claude 4.6 Sonnet
Claude 4.6 Opus Supported
Reasoning Applies multi-step reasoning to complex professional tasks including financial analysis, research synthesis, and frontend code generation.
Claude 4.6 Sonnet Supported
Claude 4.6 Opus Supported
Safety Guardrails Includes Anthropic's safety evaluations with documented resistance to prompt injection attacks, rated as safe as or safer than other recent Claude models.
Claude 4.6 Sonnet Supported
Claude 4.6 Opus
Structured Output
Claude 4.6 Sonnet Supported
Claude 4.6 Opus Supported
Subagent Orchestration Can coordinate and manage teams of subagents, parallelizing work across tools to complete complex, multi-stage tasks with minimal human intervention.
Claude 4.6 Sonnet
Claude 4.6 Opus Supported
Text
Claude 4.6 Sonnet Supported
Claude 4.6 Opus Supported
Tool Use Supports structured tool calling, allowing the model to invoke external functions and APIs as part of a reasoning or task-completion workflow.
Claude 4.6 Sonnet Supported
Claude 4.6 Opus Supported
Tools
Claude 4.6 Sonnet Supported
Claude 4.6 Opus Supported

Benchmark Comparison

Shared benchmark rows make it easier to compare performance where both models have published scores.

Benchmark Claude 4.6 Sonnet Claude 4.6 Opus
ARC-AGI-2
Novel abstract reasoning and pattern recognition
Claude 4.6 Sonnet 58.3%
Claude 4.6 Opus 68.8%
BigLaw Bench
Legal reasoning and analysis tasks
Claude 4.6 Sonnet N/A
Claude 4.6 Opus 90.2%
Finance Agent
Financial analysis and decision-making tasks
Claude 4.6 Sonnet 63.3%
Claude 4.6 Opus N/A
GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
Claude 4.6 Sonnet 79.9%
Claude 4.6 Opus 84.0%
HLE
Questions that challenge frontier models across many domains
Claude 4.6 Sonnet 13.2%
Claude 4.6 Opus 18.6%
IFBench
Instruction following accuracy
Claude 4.6 Sonnet 41.2%
Claude 4.6 Opus N/A
Long Context Reasoning
Reasoning across long documents and contexts
Claude 4.6 Sonnet 57.7%
Claude 4.6 Opus N/A
MATH-500
Undergraduate and competition-level math problems
Claude 4.6 Sonnet 97.8%
Claude 4.6 Opus N/A
MCP-Atlas Tool Use
Structured tool use via Model Context Protocol
Claude 4.6 Sonnet 61.3%
Claude 4.6 Opus N/A
MMLU-Pro
Expert knowledge across 14 academic disciplines
Claude 4.6 Sonnet 79.1%
Claude 4.6 Opus N/A
MMMB
Multilingual and multimodal understanding
Claude 4.6 Sonnet 76.1%
Claude 4.6 Opus N/A
OSWorld-Verified
Autonomous computer use and desktop tasks
Claude 4.6 Sonnet 72.5%
Claude 4.6 Opus N/A
SciCode
Scientific research coding and numerical methods
Claude 4.6 Sonnet 46.9%
Claude 4.6 Opus 45.7%
SWE-bench Verified
Real GitHub issues requiring multi-file code fixes
Claude 4.6 Sonnet 79.6%
Claude 4.6 Opus 80.8%
Terminal-Bench 2.0
Agentic coding and terminal command tasks
Claude 4.6 Sonnet 59.1%
Claude 4.6 Opus 65.4%
TerminalBench Hard
Agentic coding and terminal command tasks
Claude 4.6 Sonnet 46.2%
Claude 4.6 Opus N/A
τ²-Bench
Agentic tool use in realistic scenarios
Claude 4.6 Sonnet 79.5%
Claude 4.6 Opus N/A
τ²-bench Retail
Agentic tool use in retail scenarios
Claude 4.6 Sonnet 91.7%
Claude 4.6 Opus N/A
τ²-bench Telecom
Agentic tool use in telecom scenarios
Claude 4.6 Sonnet 97.9%
Claude 4.6 Opus N/A
Community discussion

What Reddit discussions say about Claude 4.6 Sonnet vs Claude 4.6 Opus

Claude 4.6 Sonnet and Claude 4.6 Opus are both surfacing live Reddit discussions, giving this comparison a community layer beyond specs and benchmarks.

The most visible threads right now are clustered in r/LocalLLaMA, r/cursor, r/DeepSeek.

Claude 4.6 Opus r/cursor 329 upvotes 83 comments April 9, 2026
How I use Cursor 10+ hours a day without torching my Claude Opus 4.6 limits

Anyone else here doing full-stack Next.js in Cursor and watching the Claude quota evaporate before lunch? I used to be in the same boat — massive context windows from all the components, pages, and DB logic would smoke the default limits fast.
Not anymore. I’ve been on this setup for weeks and basically never hit a wall while still getting top-tier answers. Here’s exactly what I do:

**1. .cursorrules is non-negotiable**
I keep one in the root of every project. The key line I added: “Never explain the code to me. Just output the code blocks.”
That single rule saves me thousands of output tokens a day. No more walls of “here’s what I changed and why” — just the goods.

**2. Stopped using Cursor’s built-in Claude quota**
I killed the default Cursor Pro subscription for the heavy stuff. Instead I use my own API keys and point Cursor’s “OpenAI Compatible” base URL at LLM Router Gateway.
Inside [llmrouter](https://llmrouter.app/) routing settings I set up simple tags routing like this:

* **UI & CSS tweaks**: gemini-3.1-flash → gpt-5.4-mini
* **Deep backend / complex logic**: claude-opus-4.6 → deepseek-v3.2
* **General / quick questions**: llama-4-scout

I sorted the fallback chains by speed vs intelligence. The router auto-detects the query type, so 90% of my UI polish and small fixes go to Gemini (basically free + huge context). I only actually hit Claude Opus 4.6 when I’m doing nasty database refactors or tricky architecture stuff. My Anthropic bill dropped \~70% overnight.

**3. Cmd+K for everything small**
Don’t open the full chat sidebar just to rename a variable or extract a component. Highlight the code, hit Cmd+K, let a fast model handle the inline edit. Saves a ton of tokens and feels way snappier.
That’s it. Super simple but it completely changed how much I can actually use Cursor in a day.

How are you all managing the limits? Using a Cursor Team? Or did you build your own router hacks too? Drop your setups — always looking to steal better ideas.

Open Reddit thread
Claude 4.6 Opus r/LocalLLaMA 272 upvotes 123 comments April 14, 2026
These "Claude-4.6-Opus" Fine Tunes of Local Models Are Usually A Downgrade

Time and time again I find posts about these fine tunes that promise increased intelligence and reasoning with base models, and I continuously try them, realize they're botched, and delete them shortly after. I sometimes do resort to a lower quant since they are bigger, in this case, a 40b variant of Qwen 3.5 27b, but they seem to always let me down. I've resorted to not downloading any model with "Claude Opus 4.6" in the name.

Kudos to everyone who tries to make the foundation models more intelligent, but imo, it never works.

Note that this example is anecdotal evidence on a single prompt, but it's overall always the case of decreased intelligence when using with a local agent setup + llama.cpp in WSL2. This is irrespective of the quant as well - I've tried many.

One thing to notice however, the reasoning/thinking is significantly less, perhaps that's part of the problem.

Have any you found these better than base, ever?

The attached screenshots are:

./llama-server -hf mradermacher/Qwen3.5-27B-heretic-GGUF:Q4_K_S --temp 1.0 --top-p 0.8 --top-k 20 --min-p 0.00 --fit on --alias default --jinja --flash-attn on --ctx-size 262144 --ctx-checkpoints 256 --cache-ram -1 --cache-type-k q4_0 --cache-type-v q4_0 --threads 8 --threads-batch 16 --no-mmap

./llama-server -hf mradermacher/Qwen3.5-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-i1-GGUF:i1-Q3_K_S --temp 1.0 --top-p 0.8 --top-k 20 --min-p 0.00 --fit on --alias default --jinja --flash-attn on --ctx-size 131072 --ctx-checkpoints 256 --cache-ram -1 --cache-type-k q4_0 --cache-type-v q4_0 --threads 8 --threads-batch 16 --no-mmap

Open Reddit thread
Claude 4.6 Opus r/LocalLLaMA 207 upvotes 77 comments March 18, 2026
Let's GO ! Qwen3.5-Claude-4.6-Opus-Reasoning-Distilled-v2

Also waiting for 27B ? :D

[https://huggingface.co/collections/Jackrong/qwen35-claude-46-opus-reasoning-distilled-v2](https://huggingface.co/collections/Jackrong/qwen35-claude-46-opus-reasoning-distilled-v2)

UPDATE:
Well after some testing, for a small hobby project i found B27 Q6 very capable for local inference in opencode together with [https://github.com/code-yeongyu/oh-my-openagent](https://github.com/code-yeongyu/oh-my-openagent)

Open Reddit thread
Claude 4.6 Sonnet r/DeepSeek 113 upvotes 40 comments April 29, 2026
Deepseek V4 pro reminds me of Claude 4.6 sonnet

Honestly, it isn't a terrible model.

I would put it on par with maybe Claude 4.6 sonnet.

For creativity, as I usually need that for writing. Its pretty excellent. Just not the feeling of a model like opus would.

I don't really use any other model except Kimi K2.6, as that's the best one so far.

For coding, it's pretty good too, though I've only done some html stuff with it.

And the fact it's in preview, just only means there's a hole lot more this model can do! Once it gets better at roleplay (still a bit generic, better than deepseek V3.2 in some way imo). It would be my daily driver most definitely.

Open Reddit thread
Claude 4.6 Sonnet r/SillyTavernAI 91 upvotes 40 comments February 17, 2026
Claude Sonnet 4.6 is out

[https://openrouter.ai/anthropic/claude-sonnet-4.6](https://openrouter.ai/anthropic/claude-sonnet-4.6)

Same price as Sonnet 4.5

Open Reddit thread
View more discussions →

Which model should you choose?

Use the summary below to decide which model better fits your workflow, budget, and feature requirements.

Best fit for

Claude 4.6 Sonnet

Claude 4.6 Sonnet is a stronger fit for long-context workloads, reasoning-heavy tasks, tool-augmented workflows.

Best fit for

Claude 4.6 Opus

Claude 4.6 Opus is a stronger fit for long-context workloads, reasoning-heavy tasks, tool-augmented workflows.

Verdict

Choose Claude 4.6 Sonnet if you prioritize long-context workloads, reasoning-heavy tasks, tool-augmented workflows. Choose Claude 4.6 Opus if your workflow depends more on long-context workloads, reasoning-heavy tasks, tool-augmented workflows.

FAQ

Common questions about Claude 4.6 Sonnet vs Claude 4.6 Opus

What is the main difference between Claude 4.6 Sonnet and Claude 4.6 Opus?

Claude 4.6 Sonnet leans toward long-context workloads, reasoning-heavy tasks, tool-augmented workflows, while Claude 4.6 Opus is better suited to long-context workloads, reasoning-heavy tasks, tool-augmented workflows.

Which model is cheaper: Claude 4.6 Sonnet or Claude 4.6 Opus?

Claude 4.6 Sonnet starts lower on input pricing at $3.0000 per 1M input tokens, compared with $5.0000 for Claude 4.6 Opus.

Which model has the larger context window: Claude 4.6 Sonnet or Claude 4.6 Opus?

Claude 4.6 Sonnet is listed with a context window of 1M, while Claude 4.6 Opus is listed with 1M.

How should I evaluate Claude 4.6 Sonnet vs Claude 4.6 Opus for my use case?

This comparison currently includes 19 shared benchmark rows, helping you compare practical performance across overlapping evaluations.