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.
Provider
The entity that currently provides this model.
Model ID
The routed model identifier exposed by upstream providers.
Input Context Window
The number of tokens supported by the input context window.
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
Open Source
Whether the model's code is available for public use.
Release Date
When the model was first released.
Knowledge Cut-off Date
When the model's knowledge was last updated.
API Providers
The providers that currently expose the model through an API.
Modalities
Types of data each model can process or return.
Pricing Comparison
Compare current token pricing before you choose the cheaper or more scalable API option.
Capabilities Comparison
See where each model overlaps, where they differ, and which one supports more of the features you care about.
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 |
|---|---|---|
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ARC-AGI-2
Novel abstract reasoning and pattern recognition
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BigLaw Bench
Legal reasoning and analysis tasks
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Finance Agent
Financial analysis and decision-making tasks
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
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HLE
Questions that challenge frontier models across many domains
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IFBench
Instruction following accuracy
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Long Context Reasoning
Reasoning across long documents and contexts
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MATH-500
Undergraduate and competition-level math problems
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MCP-Atlas Tool Use
Structured tool use via Model Context Protocol
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MMLU-Pro
Expert knowledge across 14 academic disciplines
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MMMB
Multilingual and multimodal understanding
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OSWorld-Verified
Autonomous computer use and desktop tasks
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SciCode
Scientific research coding and numerical methods
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SWE-bench Verified
Real GitHub issues requiring multi-file code fixes
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Terminal-Bench 2.0
Agentic coding and terminal command tasks
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TerminalBench Hard
Agentic coding and terminal command tasks
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τ²-Bench
Agentic tool use in realistic scenarios
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τ²-bench Retail
Agentic tool use in retail scenarios
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τ²-bench Telecom
Agentic tool use in telecom scenarios
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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.
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.
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
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)
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.
[https://openrouter.ai/anthropic/claude-sonnet-4.6](https://openrouter.ai/anthropic/claude-sonnet-4.6)
Same price as Sonnet 4.5
Which model should you choose?
Use the summary below to decide which model better fits your workflow, budget, and feature requirements.
Claude 4.6 Sonnet
Claude 4.6 Sonnet is a stronger fit for long-context workloads, reasoning-heavy tasks, tool-augmented workflows.
Claude 4.6 Opus
Claude 4.6 Opus is a stronger fit for long-context workloads, reasoning-heavy tasks, tool-augmented workflows.
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.
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.