Claude Opus 4.6 vs Sonnet 4.6: Which Anthropic Model Actually Wins?

Here’s a question I’ve been getting constantly since Anthropic dropped two major models in the span of two weeks: Do you actually need Opus 4.6, or is Sonnet 4.6 good enough?

The honest answer? It depends on what you’re building. But the more interesting answer – the one nobody’s talking about – is that Sonnet 4.6 is quietly eating Opus 4.6’s lunch on several benchmarks that actually matter for production workloads. And that’s a big deal for anyone paying $5 per million input tokens when $3 gets you 90% of the way there.

Let’s break this down properly. No hype, no press release regurgitation. Just the numbers, the tradeoffs, and a clear decision framework for developers and teams trying to pick the right model.

The Benchmark Reality: Where Each Model Actually Wins

Start with the headline numbers, because they tell a story Anthropic’s marketing doesn’t fully emphasize.

On SWE-bench Verified – the gold standard for real-world software engineering tasks – Claude Opus 4.6 scored 80.8% while Sonnet 4.6 hit 79.6%. That’s a 1.2 percentage point gap. For context, Sonnet 4.5 scored 77.2%, so Sonnet 4.6 made a bigger leap than the Opus-to-Opus improvement. The gap between the two models is essentially noise for most codebases.

Now here’s where it gets interesting. On OSWorld-Verified – the benchmark that measures actual computer use, navigating real software interfaces – Sonnet 4.6 scored 72.5% versus Opus 4.6’s 72.7%. Two-tenths of a percentage point. For all practical purposes, they’re identical at computer use.

But flip to knowledge work and the story inverts completely. On GDPval-AA (economically valuable office work), Sonnet 4.6 achieved an Elo score of 1633 versus Opus 4.6’s 1606. Sonnet wins. On agentic financial analysis, Sonnet 4.6 leads with 63.3% versus Opus 4.6’s 60.1%. Sonnet wins again.

Where does Opus genuinely pull ahead? On Terminal-Bench 2.0 – the benchmark that tests agentic coding agents on shell commands, multi-file debugging, and complex planning – Opus 4.6 scored 65.4% versus Sonnet 4.6’s score that trails by roughly 5-8 points.

And on ARC-AGI 2, which tests novel abstract reasoning on unseen problems, Opus 4.6 scored 68.8% – nearly doubling its predecessor Opus 4.5’s 37.6% and beating GPT-5.2 Pro’s 54.2%.

The pattern is clear: Opus 4.6 wins on deep reasoning, abstract problem-solving, and complex agentic terminal tasks. Sonnet 4.6 wins on knowledge work, financial analysis, and everyday productivity. And on coding? They’re basically tied.

Pricing and the Real Cost of Intelligence

This is where the decision gets practical. Fast.

Claude Opus 4.6 costs $5 per million input tokens and $25 per million output tokens at the standard 200K context window. Push beyond 200K into the 1M token beta? That jumps to $10/$37.50. And if you need Opus 4.6 in Fast Mode (research preview), you’re looking at $30/$150 per million tokens – a number that will make your finance team physically ill.

Claude Sonnet 4.6 runs at $3 per million input tokens and $15 per million output tokens. That’s a 40% discount on input and a 40% discount on output versus standard Opus 4.6 pricing.

Think about what that means at scale. If you’re running 100 million tokens per day through an agentic pipeline – which is not unusual for production coding agents – the difference is $200/day versus $300/day. That’s $36,500 per year in savings, just by switching to Sonnet for tasks where the benchmarks show it’s equally capable.

The math gets even more compelling when you factor in Anthropic’s cost reduction options: up to 90% savings with prompt caching and 50% savings with batch processing. Both models support these features, but the base price differential compounds.

I’ve been tracking Anthropic’s pricing strategy for months, and this is clearly intentional. They want Sonnet to be the default workhorse – it’s now the default model for Free and Pro users on claude.ai and Claude Cowork – while Opus serves as the premium tier for genuinely hard problems.

As we noted in our Anthropic vs OpenAI monetization analysis, Anthropic’s API-first revenue model depends on high-volume enterprise adoption, and Sonnet is the vehicle for that.

The Feature Divide: Adaptive Thinking vs Context Compaction

Both models got new capabilities in their respective launches. But they’re different capabilities, and that’s telling.

Opus 4.6’s Adaptive Thinking is an evolution of extended thinking. Instead of a fixed reasoning budget, the model dynamically adjusts how hard it thinks based on task complexity. Four levels: low, medium, high, and max.

For a simple code completion, it uses low effort. For a multi-step legal analysis across 50 documents, it cranks to max. Developers get explicit effort controls, which is genuinely useful for cost management in production.

Sonnet 4.6’s Context Compaction (beta) is a different kind of intelligence. As a conversation approaches its token limit, the model automatically summarizes and compresses older context, extending the effective working memory without losing critical information.

Think of it like a smart filing system – instead of dumping old papers when the desk gets full, it creates compressed summaries and keeps the key facts accessible.

This matters enormously for long-running agentic tasks. The Vending-Bench Arena results illustrate this: Sonnet 4.6 demonstrated sophisticated multi-step business strategy in a simulated competitive environment, adopting an initial capacity investment phase before pivoting to profitability – almost matching Opus 4.6’s performance. Context compaction is what makes those extended autonomous sessions possible without hitting walls.

Both models also support interleaved thinking – the ability to pause and reason between tool calls rather than just at the start of a response. This is a significant upgrade for agentic workflows where the model needs to reassess its strategy mid-execution based on tool outputs. As we covered in our Claude Sonnet 4.6 article, this capability pushed the tool use benchmark from 43.8% to 61.3%.

The 1M token context window is available in beta for both models, but it’s gated behind Usage Tier 4 or custom rate limits on the Anthropic Developer Platform. Standard users get 200K.

Who Should Use What: A Practical Decision Framework

Here’s the framework I’d give any developer or team making this decision today.

Choose Opus 4.6 if:

• You’re building systems that require deep abstract reasoning (legal analysis, complex financial modeling, research synthesis)
  
• Your agentic coding tasks involve multi-file refactors, architectural planning, or debugging across large codebases
  
• You need the best possible performance on Terminal-Bench-style tasks where the model must plan and execute shell commands autonomously
  
• Cost is secondary to maximum capability (enterprise contracts, high-stakes deployments)
  
• You’re integrating with GitHub Copilot – Opus 4.6 is now available there
  

Choose Sonnet 4.6 if:

• You’re running high-volume coding pipelines where the 1.2% SWE-bench gap doesn’t justify 67% higher cost
  
• Your primary use case is knowledge work, office automation, or financial analysis
  
• You need computer use capabilities (OSWorld scores are essentially identical)
  
• You’re building consumer-facing products where cost efficiency drives unit economics
  
• You want context compaction for long-running autonomous agents
  

The hybrid approach – which the developer community on r/ClaudeAI has converged on – is to use Opus for planning and architectural decisions, then hand off implementation to Sonnet. Think of Opus as your senior architect and Sonnet as your senior engineer. The architect designs the system; the engineer builds it. You don’t need the architect writing boilerplate.

This connects to a broader pattern we’ve been tracking: the MiniMax M2.5 vs GLM 4.7 comparison showed the same dynamic in Chinese AI models – specialized models for planning versus execution. The industry is converging on tiered model architectures for cost-efficient agentic systems.

The Constraint Nobody’s Talking About

Here’s the thing about both models that the benchmark tables don’t capture: token economics are the real constraint, not raw capability.

Opus 4.6 supports up to 128,000 output tokens, enabling longer thinking budgets and more comprehensive responses. That sounds great until you realize that a single complex agentic task at max thinking effort can cost $5-15 in API calls. Reports from Antigravity IDE users showed $50 credits evaporating in a single complex task when Opus 4.6 was running at full effort.

Sonnet 4.6’s context compaction partially addresses this by extending effective context without burning tokens on repetition. But the fundamental constraint remains: these models are expensive to run at scale, and the performance gap between them is narrowing faster than the price gap.

My prediction: by mid-2026, Sonnet-class models will be the default choice for 80%+ of production agentic workloads, with Opus reserved for the genuinely hard 20%. We’re already seeing this with Sonnet 4.6 becoming the default on claude.ai. The trajectory is clear.

The Bottom Line

Claude Opus 4.6 vs Sonnet 4.6 isn’t really a competition – it’s a spectrum. Opus is the specialist you call for the hardest problems. Sonnet is the generalist who handles everything else with remarkable competence.

The benchmark data tells a nuanced story: Opus wins on abstract reasoning and complex terminal-based agentic tasks. Sonnet wins on knowledge work and financial analysis. On coding and computer use, they’re essentially tied. And Sonnet costs 40% less.

For most developers and teams, Sonnet 4.6 is the right default. Use Opus when you genuinely need it – for the architectural planning sessions, the complex multi-step reasoning chains, the tasks where that extra 1-5% on the benchmark actually translates to a meaningfully better outcome.

The real question isn’t which model is better. It’s whether the tasks you’re running actually require Opus-level reasoning, or whether you’ve been paying the premium out of habit. Run the benchmarks on your specific workload. The answer might surprise you.

FAQ

Is Claude Opus 4.6 worth the extra cost over Sonnet 4.6?

For most production workloads, no. The SWE-bench gap is 1.2 percentage points, OSWorld scores are virtually identical, and Sonnet 4.6 actually outperforms Opus on knowledge work and financial analysis. Opus is worth the premium for deep abstract reasoning, complex legal/financial analysis, and Terminal-Bench-style agentic coding tasks where planning complexity is high.

What is the context window for Claude Opus 4.6 vs Sonnet 4.6?

Both models support a 200,000 token standard context window, with a 1 million token context window available in beta for Usage Tier 4 users or those with custom rate limits on the Anthropic Developer Platform. The 1M context window costs $10/$37.50 per million input/output tokens for Opus 4.6 and proportionally less for Sonnet 4.6.

Which model is better for coding: Claude Opus 4.6 or Sonnet 4.6?

For everyday coding tasks, they’re essentially equivalent – Opus 4.6 scores 80.8% on SWE-bench Verified versus Sonnet 4.6’s 79.6%. For complex multi-file architectural work and agentic terminal tasks (Terminal-Bench 2.0), Opus 4.6 has a meaningful edge. For high-volume coding pipelines, Sonnet 4.6’s 40% lower cost makes it the practical choice.

What is Adaptive Thinking in Claude Opus 4.6?

Adaptive Thinking is Opus 4.6’s dynamic reasoning system that adjusts thinking effort based on task complexity. It offers four levels (low, medium, high, max) and replaces the fixed extended thinking budget from previous versions. This gives developers explicit control over the reasoning-cost tradeoff for production deployments.

What is context compaction in Claude Sonnet 4.6?

Context compaction is a beta feature in Sonnet 4.6 that automatically summarizes older conversation history as the context window approaches its limit. Instead of truncating or losing information, the model creates compressed summaries of past context, enabling longer autonomous agent sessions without hitting token walls. It’s particularly valuable for multi-step agentic workflows.

How does Claude Sonnet 4.6 compare to Claude Opus 4.6 on agentic tasks?

Sonnet 4.6 scored 72.5% on OSWorld-Verified (computer use) versus Opus 4.6’s 72.7% – essentially tied. On the Vending-Bench Arena (business simulation), Sonnet 4.6 nearly matched Opus 4.6 performance. On agentic financial analysis, Sonnet 4.6 leads at 63.3% versus Opus 4.6’s 60.1%. Opus maintains an edge on Terminal-Bench 2.0 (complex shell-based agentic coding).