Claude Computer Use Review: Hands-On Testing (2026)
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Claude Opus 4.6 Review: Anthropic Just Raised the Bar Again
I need to come clean about something. Opus 4.5 ruined every other AI model for me.
For the past several months, I’ve used Opus 4.5 as my daily driver for anything requiring real thought. Complex debugging, multi-document analysis, strategic planning. It wasn’t the fastest. It wasn’t the cheapest. But it was the first AI that genuinely thought through problems rather than pattern-matching to an answer. Best thing since sliced bread, and I said that publicly more than once.
So when Anthropic dropped Opus 4.6 on February 5th, I cleared my calendar and went in headfirst. I’ve been running it hard since launch, and I’m going to share what I’ve found so far. Spoiler: they somehow made sliced bread even better.
Quick Verdict
Aspect Rating Overall Score ★★★★★ (4.7/5) Best For Complex reasoning, large codebases, multi-agent workflows Pricing $5/$25 per 1M tokens (API) or Claude Pro at $20/mo Adaptive Thinking ★★★★★ Context Window 1M tokens (up from 200K in Opus 4.5) Speed ★★★☆☆ (improved over 4.5) Cost Efficiency ★★★☆☆ Bottom line: The smartest AI model I’ve ever used, now with a massive context window and faster response times. If Opus 4.5 was already your go-to for hard problems, 4.6 makes the gap between this and everything else even wider.
Coming From Opus 4.5: What Actually Changed
I’m not going to pretend I’m being objective here. I was already sold on Opus 4.5. But 4.6 adds three things that matter:
1M token context window. This is the headline. Opus 4.5 maxed out at 200K tokens. That was generous, but I still had to slice up large codebases or trim down document collections before feeding them in. With 1M tokens (roughly 750,000 words), I’ve been uploading entire project repositories without filtering. The model sees everything, and its analysis reflects that.
Adaptive thinking. Opus 4.5’s extended thinking was brilliant but rigid. It thought hard about everything, including questions that didn’t need it. Opus 4.6 reads the room. Simple questions get fast answers. Complex problems get the deep reasoning treatment. This alone makes it 2-3x faster for everyday use compared to 4.5.
Agent teams in Claude Code. You can now spin up multiple agents that coordinate autonomously on a task. I tested this with a large refactoring project. Three agents worked in parallel: one handling the database layer, one on the API, one on the frontend. They communicated and stayed consistent without me playing traffic cop. Early days, but this is a glimpse of where AI development is heading.
Benchmarks That Actually Mean Something
I normally glaze over benchmark numbers, but Opus 4.6’s results are hard to ignore because of where it jumped.
ARC AGI 2: 68.8%. This is the one that grabbed me. Opus 4.5 scored 37.6%. That’s not an incremental improvement. That’s nearly doubling the score on a benchmark designed to test genuine reasoning ability. For comparison, GPT-5.2 hits 54.2% and Gemini 3 Pro scores 45.1%.
Terminal-Bench 2.0: 65.4%. Up from 59.8% on Opus 4.5. This measures real-world coding ability, and Opus 4.6 leads all frontier models.
OSWorld (computer use): 72.7%. Humans score around 72% on this benchmark. Opus 4.6 is functionally matching human-level performance on agentic computer use tasks. Opus 4.5 scored 66.3%.
Humanity’s Last Exam: #1 among all models. This is the multidisciplinary reasoning test that’s supposed to be brutally hard. Opus 4.6 leads every frontier model.
The ARC AGI 2 jump is the number I keep coming back to. That’s not a model getting better at memorized patterns. That’s a measurable improvement in the ability to reason about novel problems.
Coding: Already Better Than My Opus 4.5 Workflow
I coded with Opus 4.5 constantly. It was already the best debugging partner I’d found. Opus 4.6 moves the needle further in two specific ways.
It plans before it acts. Opus 4.5 would sometimes rush into a solution, especially for medium-complexity tasks. Opus 4.6 consistently outlines its approach first, catches its own mistakes during execution, and course-corrects. I watched it start down the wrong path on a TypeScript refactor, recognize the issue mid-stream, and pivot. That’s new behavior.
Full codebase context changes everything. With 1M tokens, I’m feeding it entire repositories. Not cherry-picked files. Everything. The quality of its suggestions improved immediately because it sees the actual architecture, not a curated slice of it. Yesterday I dropped in a 400-file Next.js project and asked about performance bottlenecks. It identified an issue spanning three modules that I never would have connected manually because I wouldn’t have had all three files open at once.
For context, GPT-5.3-Codex launched the same day as Opus 4.6 and leads on SWE-Bench Pro. OpenAI’s model is also faster for pure code generation. But in my early testing, Opus 4.6 is better at understanding why code is broken, not just fixing what’s in front of it. That distinction matters when you’re debugging production systems at 2am.
The GPT-5.2 / 5.3 Comparison: Where Things Stand
The existing version of this review compared Opus against GPT-4o. That comparison is outdated. The real competition right now is GPT-5.2 (OpenAI’s best general-purpose model) and GPT-5.3-Codex (their new coding specialist, released the same day as Opus 4.6).
| Model | Reasoning | Coding | Speed | Context | Price |
|---|---|---|---|---|---|
| Opus 4.6 | ★★★★★ | ★★★★★ | ★★★☆☆ | 1M tokens | $5/$25 per 1M |
| GPT-5.2 | ★★★★☆ | ★★★★☆ | ★★★★☆ | 128K tokens | $$$ |
| GPT-5.3-Codex | ★★★★☆ | ★★★★★ | ★★★★☆ | 128K tokens | $$$$ |
| Gemini 3 Pro | ★★★★☆ | ★★★★☆ | ★★★★☆ | 2M tokens | $$$$ |
| Claude Sonnet 4.5 | ★★★★☆ | ★★★★☆ | ★★★★★ | 200K tokens | $$ |
Here’s how I’d break it down:
Opus 4.6 vs GPT-5.2: Opus wins on reasoning (ARC AGI 2: 68.8% vs 54.2%) and context window (1M vs 128K). GPT-5.2 wins on speed and has broader tool access including internet browsing. For deep analysis and complex debugging, Opus. For quick professional tasks with web access needs, GPT-5.2.
Opus 4.6 vs GPT-5.3-Codex: This one’s closer on coding. GPT-5.3-Codex leads on SWE-Bench Pro and is 25% faster. But Opus 4.6’s 1M context window means it can hold entire codebases in memory simultaneously. For isolated coding tasks, GPT-5.3-Codex is arguably better. For understanding and debugging complex systems across many files, Opus 4.6 has the edge. Also worth noting: OpenAI flagged GPT-5.3-Codex as “high-capability” for cybersecurity and is rolling it out with unusually tight controls.
Opus 4.6 vs Gemini 3 Pro: Gemini still has the largest context window (2M tokens) and better Google integration. But Opus 4.6 outscores it on every reasoning benchmark I’ve seen. If you need sheer document capacity, Gemini. If you need the smartest analysis of those documents, Opus.
What Opus 4.6 Does That No Other Model Matches
The 500 zero-day discovery. Anthropic reported that Opus 4.6, using just its out-of-the-box capabilities, found over 500 previously unknown zero-day vulnerabilities in open-source code. Each one validated by Anthropic’s team or external security researchers. That’s not a benchmark. That’s real-world impact that demonstrates a level of code understanding beyond any competitor I’m aware of.
Adaptive thinking actually saves time. With Opus 4.5, I’d sometimes wait 45 seconds for the model to ponder a straightforward question. Opus 4.6 gauges complexity and adjusts. Quick questions get quick answers. Hard problems get the extended reasoning. In practice, my average response time dropped noticeably while quality on complex tasks stayed the same or improved.
Agent teams are the real unlock. This is early-stage functionality in Claude Code, but the ability to assemble agents that divide work and coordinate autonomously is something no other provider offers at this level. I’ve only used it for a few projects so far, but the productivity gain when it works is significant.
Where Opus 4.6 Still Falls Short
I loved Opus 4.5 and I’m early into loving Opus 4.6, but I’m not going to pretend it’s perfect.
Still no internet access. Every other frontier model (GPT-5.2, GPT-5.3, Gemini) can browse the web. Opus cannot. For anything involving current events, live data, or recent documentation, I still switch to Perplexity or ChatGPT.
Rate limits on Claude Pro. The $20/month plan still caps Opus usage. During heavy work sessions, I hit limits and have to fall back to Sonnet. This is less painful now that Sonnet 4.5 is excellent, but it’s still frustrating when you’re mid-flow on a complex problem.
Cost at scale. At $5/$25 per million tokens with a 1M context window, a single max-context request costs over $5 for input alone. The pricing hasn’t changed from Opus 4.5, but the context window is 5x larger. It’s easy to burn through credits if you’re not disciplined about what you upload.
Overkill for simple work. This hasn’t changed from 4.5 despite adaptive thinking helping at the margins. If you’re writing basic emails or doing simple Q&A, Claude Sonnet 4.5 handles it at 1/5 the cost with virtually identical quality. I use Opus for maybe 15-20% of my AI interactions.
Pricing Breakdown
| Access Method | Cost | What You Get | Best For |
|---|---|---|---|
| Claude Pro | $20/month | Rate-limited Opus access, unlimited Sonnet | Individual professionals |
| API | $5/$25 per 1M tokens | No rate limits, pay per use | Developers, automation |
| API with caching | $0.50/$25 per 1M tokens | 90% input discount | Repeated analysis |
| API batch | $2.50/$12.50 per 1M tokens | 50% discount, 24hr delivery | Non-urgent processing |
What I actually spend: Through Claude Pro, most of my Opus usage is covered by the subscription. For API projects using the full 1M context, I’ve been spending roughly $15-25/day during my heavy testing period. That’ll drop as I settle into a routine. With caching enabled for repeated codebases, costs fall dramatically.
For most professionals, Claude Pro at $20/month gives you enough Opus access to handle complex work while defaulting to Sonnet for everything else.
My Early Hands-On Experience
I’ve only had Opus 4.6 for about a day at the time of writing. I’ll update this review as I accumulate more time with it. But here’s what I’ve found so far.
What’s Already Impressed Me
Full-codebase debugging. I uploaded an entire monorepo (roughly 600K tokens) and asked about an intermittent auth failure. Opus traced the issue across four services, identified a race condition in the token refresh logic, and explained exactly why it only appeared under load. This would have taken me hours to trace manually.
Research synthesis at scale. I fed it 15 articles, three whitepapers, and a competitor’s full documentation. Opus identified contradictions between the published research and the product’s actual capabilities. The kind of insight that usually requires reading everything twice.
Faster for easy stuff. Adaptive thinking is doing what Anthropic promised. Quick questions come back fast. I no longer feel like I’m wasting Opus’s time (and mine) on simple tasks.
What I’m Still Evaluating
Agent teams. I’ve run two multi-agent sessions so far. One went smoothly. The other had coordination issues where agents duplicated work. I need more time before I can say whether this feature is reliable enough for production workflows.
1M context quality. Does Opus maintain reasoning quality at the far end of its context window? My initial tests look good, but this needs more rigorous testing with varied content types.
Long-session reliability. Opus 4.5 sometimes lost coherence in very long conversations. Too early to tell if 4.6 improved on this.
Who Should Use Opus 4.6
Software architects and senior developers. The 1M context window plus the strongest code reasoning I’ve tested makes this the best coding partner available. If you’re working on large, complex codebases, the upgrade from Opus 4.5 is immediately noticeable.
Researchers and analysts who synthesize information across many sources. Upload everything. Let Opus find the patterns. The combination of massive context and superior reasoning catches things you’d miss.
Security professionals. The 500 zero-day finding isn’t a parlor trick. If Opus can find vulnerabilities that humans missed in popular open-source projects, it can audit your codebase too.
Anyone already paying for Claude Pro. You’re getting 4.6 automatically. The improvements over 4.5 are meaningful across the board. No action needed.
Who Should Wait
Casual AI users don’t need Opus. Claude Sonnet 4.5 handles 85% of tasks at 1/5 the cost. Start there.
Speed-critical applications still favor GPT-5.2 or Sonnet 4.5. Opus 4.6 is faster than 4.5, but it’s still not the fastest model in the room.
Budget-conscious teams should run the numbers. At max context, a single Opus request can cost $5+. For high-volume use cases, explore caching and batch pricing first, or evaluate whether Sonnet 4.5 gets close enough.
How to Get Started
- If you have Claude Pro, you already have access. Open claude.ai and select Opus 4.6
- Test it on your hardest current problem. That’s where the difference shows
- Upload something large. A full codebase, a stack of documents. Let the 1M context work
- Try agent teams in Claude Code if you’re a developer. Start with a well-defined task
- Compare against your Opus 4.5 workflow. Note where adaptive thinking speeds things up
- Track your costs if using the API. The larger context window makes it easy to spend more than expected
- Keep Sonnet 4.5 as your default. Escalate to Opus for genuine complexity
The Bottom Line
Opus 4.5 was already the best reasoning model available. I said it was the best thing since sliced bread and I meant it. Opus 4.6 takes everything I liked about 4.5 and adds a massive context window, smarter pacing with adaptive thinking, and early but promising multi-agent capabilities.
The ARC AGI 2 score alone (68.8%, nearly double Opus 4.5’s 37.6%) tells a clear story: this isn’t a point release. It’s a significant reasoning upgrade wearing a modest version number.
Is it perfect? No. The lack of web access still hurts. Rate limits on Pro still frustrate. And it’s still overkill for most daily tasks. But for the 15-20% of my work that’s genuinely hard, Opus 4.6 is in a class by itself.
If you were already an Opus 4.5 user like me, you’ll feel the improvements within your first session. If you’ve been on the fence about paying the Opus premium, this is the version that justifies it.
Verdict: The best reasoning model available, now with a context window to match its intelligence. Not for everyday tasks, but for the work that actually matters, nothing else comes close.
Try Claude Pro with Opus → | View API Pricing →
Frequently Asked Questions
How is Opus 4.6 different from Opus 4.5?
Three major changes: the context window jumped from 200K to 1M tokens, adaptive thinking adjusts reasoning effort based on task complexity (so simple questions are faster), and agent teams in Claude Code let multiple AI agents coordinate on a project. The ARC AGI 2 benchmark score nearly doubled from 37.6% to 68.8%, indicating a substantial reasoning improvement.
Should I compare Opus 4.6 against GPT-4o or GPT-5.2?
GPT-4o is outdated for this comparison. The relevant competitors are GPT-5.2 (OpenAI’s current best general model) and GPT-5.3-Codex (their new coding specialist, released the same day as Opus 4.6). Opus 4.6 leads on reasoning benchmarks; GPT-5.2 is faster and has web access; GPT-5.3-Codex is competitive on pure coding tasks.
What about GPT-5.3-Codex that just came out?
GPT-5.3-Codex launched February 5th, the same day as Opus 4.6. It leads on SWE-Bench Pro and is 25% faster than GPT-5.2. For pure code generation and isolated coding tasks, it’s excellent. But it uses a 128K context window versus Opus 4.6’s 1M, which limits its ability to analyze large codebases holistically. OpenAI also flagged it as “high-capability” for cybersecurity and is delaying full developer access.
Is the 1M token context window real or marketing?
It’s real, though currently in beta for the full 1M. Claude Pro users get approximately 800K tokens per conversation. I’ve tested it with large codebases and document collections. The model maintains quality across the full window in my early testing, though I need more time to evaluate edge cases.
How much does Opus 4.6 cost in practice?
Through Claude Pro ($20/month), rate-limited but no per-query cost. Via API, a typical session (30K context, 3K output) costs about $0.23. A full 1M context request runs over $5 for input alone. Caching drops repeated-context costs by 90%. For most professionals, Claude Pro covers daily needs.
Is Opus 4.6 worth upgrading from Sonnet?
Only if you regularly hit problems that Sonnet can’t handle well. About 85% of my AI tasks work fine on Sonnet 4.5. But for complex debugging, multi-document analysis, strategic planning, and security review, Opus 4.6 operates at a different level. Start with Sonnet. Upgrade when you feel the ceiling.
What are agent teams and should I care?
Agent teams let you run multiple Claude agents in parallel within Claude Code. Each agent handles a piece of a larger task, and they coordinate autonomously. It’s early-stage and I’ve had mixed results so far, but when it works, it’s a glimpse of AI-assisted development that feels qualitatively different. Developers should experiment with it. Everyone else can wait.
Last updated: February 6, 2026. Based on hands-on testing since launch day with Claude Pro and API access. Features and pricing verified against Anthropic’s documentation. This review will be updated as I accumulate more time with the model.
Related reading: Claude vs ChatGPT vs Gemini | Best AI for Coding | Claude MCP Servers Tutorial
