Claude Opus 4.6 Review: Agentic Coding Champion or Overhyped?
Anthropic dropped Opus 4.6 on February 5, 2026, and the benchmarks immediately triggered a wave of superlatives across tech Twitter. Terminal-Bench 2.0 record. SWE-Bench over 80%. Agent Teams that coordinate multiple coding sessions simultaneously. After three weeks of daily use across API integrations, Claude Code sessions, and head-to-head comparisons with Gemini 3.1 Pro, here's what the numbers don't tell you — and what actually matters for your workflow and your budget.
TL;DR — Key Takeaways:
- • Agentic coding king — 65.4% on Terminal-Bench 2.0 and 80.8% on SWE-Bench Verified are genuine records, not marketing spin
- • 67% cheaper than Opus 4.1 — $5/$25 per MTok (input/output) makes it accessible for the first time, but still roughly 7x pricier than Gemini 3.1 Pro on input
- • 128K output tokens — doubled from 64K, genuinely useful for large code generation and long-form technical writing
- • Adaptive Thinking is a double-edged sword — auto-adjusts reasoning depth brilliantly, but token consumption becomes unpredictable and sometimes excessive
- • Creative writing regression — some users (including us) notice Opus 4.6 writes slightly less naturally than 4.5 for prose and storytelling tasks
- • Not a universal upgrade — for cost-sensitive or latency-sensitive workloads, Sonnet 4.5 or Gemini 3.1 Pro remain better choices
What Is Claude Opus 4.6?
Opus 4.6 is Anthropic's flagship model, released February 5, 2026. It sits at the peak of the Claude model family — above Sonnet 4.5 (the workhorse) and Haiku 4.5 (the budget option). Where Sonnet is optimized for speed-to-cost ratio, Opus is built to push capability boundaries regardless of efficiency.
The "4.6" designation follows Anthropic's pattern of incremental numbering. This isn't a ground-up architecture change from Opus 4.5 — it's a significant capability upgrade built on the same foundation, with three headline additions: Adaptive Thinking, Context Compression, and Agent Teams.
The context window holds at 200K tokens standard with a 1M token beta available through the API. Max output doubled from 64K to 128K tokens. For developers who've hit output ceilings trying to generate large files or comprehensive codebases, that's a meaningful jump.
At a Glance
Genuinely Impressive:
- • Record-setting agentic coding benchmarks
- • 128K output — generates entire modules in one pass
- • Agent Teams for parallel multi-session coordination
- • 2x better on computational biology and organic chemistry
- • 67% API price reduction from Opus 4.1
Where It Falls Short:
- • Still roughly 7x pricier than Gemini 3.1 Pro on input
- • Adaptive Thinking inflates token usage unpredictably
- • Standard mode noticeably slower than Sonnet 4.5
- • Creative writing quality dipped compared to Opus 4.5
- • ARC-AGI-2 still behind Gemini (68.8% vs 77.1%)
How We Tested
This review is based on three weeks of hands-on use starting February 6, 2026 — the day after launch. We ran Opus 4.6 through a structured evaluation across five categories, logging token consumption, completion times, and output quality for each.
Agentic Coding Tasks (12 sessions)
Multi-file refactoring, bug diagnosis from error logs, test suite generation, and full feature implementation across Python, TypeScript, and Rust codebases using Claude Code. Tasks ranged from 30-minute fixes to 4-hour feature builds.
API Integration Testing (8 projects)
Direct API calls measuring token consumption, latency, and output quality across different reasoning_effort levels. Compared costs against equivalent Gemini 3.1 Pro and GPT-5 API calls for identical prompts.
Creative Writing and General Tasks (6 sessions)
Blog drafts, marketing copy, email writing, and narrative fiction to test whether the coding-focused improvements came at the cost of general language quality. Compared outputs blindly against Opus 4.5 and Claude Sonnet 4.5.
Scientific and Research Prompts (5 sessions)
Computational biology questions, organic chemistry synthesis routes, and multi-step mathematical reasoning. These target the areas Anthropic claims 2x improvement — we wanted to verify that claim independently.
Third-Party Benchmark Verification
Cross-referenced Anthropic's published benchmarks against independent evaluations from Artificial Analysis, LMSYS Chatbot Arena, and community reproductions on GitHub. Benchmark figures cited in this review match or closely approximate independent results.
All API testing was conducted at standard pricing with no sponsored access or credits from Anthropic. Token costs reported are actual billed amounts.
Key Features That Actually Matter
Opus 4.6's feature list is long, but three capabilities genuinely change how the model performs in practice. Everything else is incremental.
The Three Features Worth Paying Attention To
Adaptive Thinking
Instead of applying the same depth of chain-of-thought reasoning to every prompt, Opus 4.6 dynamically adjusts how much it "thinks" based on task complexity. A simple factual query gets a near-instant response. A complex code refactoring problem triggers extended reasoning that can consume thousands of thinking tokens.
In practice, this works remarkably well for coding tasks. The model correctly identifies when a problem requires deep analysis versus a straightforward pattern match. The downside: token bills become less predictable because you can't easily forecast how many thinking tokens a given prompt will consume.
Context Compression
For long sessions — especially in Claude Code — the model now auto-summarizes earlier context rather than dropping it entirely. Previous models would either truncate history or degrade in quality as context filled up. Opus 4.6 compresses older conversation turns into summaries, preserving key decisions and context while freeing token space.
This is particularly noticeable in multi-hour coding sessions. Around the 150K token mark where previous models started losing track of earlier files, Opus 4.6 maintains coherence. It's not perfect — nuanced details from early in a session can still get flattened — but the difference is significant.
Agent Teams
This is the headline feature for Claude Code users. You can now coordinate multiple Claude Code agents working on different parts of a codebase simultaneously — one agent writing tests, another implementing the feature, a third updating documentation. They share context and coordinate changes.
Agent Teams works well for clearly separable tasks (backend API + frontend components + test suite). It struggles when tasks have tight dependencies — agents sometimes produce conflicting code that requires manual reconciliation. Think of it as parallel junior developers, not a synchronized team.
Other Notable Upgrades
Benchmark Deep Dive
Benchmarks should always be taken with skepticism — models can be optimized for specific evaluations. That said, Opus 4.6's scores across multiple independent benchmarks paint a consistent picture: this model is genuinely strong at agentic coding tasks and meaningfully improved in scientific reasoning.
| Benchmark | Opus 4.6 | What It Measures |
|---|---|---|
| Terminal-Bench 2.0 | 65.4% | Agentic coding in real terminal environments. Multi-step tasks with file systems, package managers, and git. |
| SWE-Bench Verified | 80.8% | Resolving real GitHub issues from popular open-source repos. Tests practical software engineering ability. |
| OSWorld | 72.7% | Computer use / desktop automation. Navigating GUIs, clicking buttons, filling forms autonomously. |
| BrowseComp | 84.0% | Agentic web search and information retrieval. Multi-hop research across multiple sources. |
| Humanity's Last Exam | 53.1% | Expert-level questions across all academic fields (with tool use). A ceiling-test for frontier models. |
The Terminal-Bench 2.0 score deserves special attention. This benchmark tests models on real-world terminal tasks — not sanitized code completion, but full agentic workflows involving file manipulation, dependency management, debugging, and version control. 65.4% is the highest score ever recorded, and the gap between Opus 4.6 and the next best model is wider than most benchmark leaderboards show.
The Humanity's Last Exam score of 53.1% (with tools) is a useful reality check. These are genuinely hard problems that frontier models still can't solve half the time, even with access to tools and search. Impressive progress, but far from general intelligence.
Pricing Breakdown
Anthropic's pricing strategy with Opus 4.6 is clearly designed to make the flagship model accessible to more developers. The 67% reduction from Opus 4.1 ($15/$75 per MTok) to $5/$25 per MTok is substantial. But "more accessible" doesn't mean "cheap" — Opus 4.6 remains a premium product at premium prices.
API Pricing
| Tier | Input / MTok | Output / MTok | Notes |
|---|---|---|---|
| Standard | $5.00 | $25.00 | 200K context, 128K output |
| Extended (1M beta) | $10.00 | $37.50 | 1M context window |
| Fast Mode | $30.00 | $150.00 | 2.5x speed, same quality |
| Batch API | $2.50 | $12.50 | 50% off, non-real-time |
The Batch API at $2.50/$12.50 per MTok is the hidden gem here. For workloads that don't need real-time responses — batch code analysis, test generation, documentation — the 50% discount makes Opus 4.6 competitive with Sonnet 4.5's standard pricing while delivering Opus-level quality.
The real cost question isn't the per-token price — it's the total token consumption. Adaptive Thinking means a complex coding prompt might consume 3,000–8,000 thinking tokens before generating a single output token. On simple queries that reasoning overhead is minimal, but on hard problems it can multiply your effective cost by 2–4x compared to a non-reasoning model.
In our testing, average API cost per coding task ranged from about $0.15 for simple completions to $2–4 for complex multi-file refactoring sessions. A Claude Code session running for an hour typically costs $8–15 at standard API rates.
Claude Opus 4.6 vs Gemini 3.1 Pro
This is the comparison most developers are actually asking about. Gemini 3.1 Pro launched within weeks of Opus 4.6, and the two models target overlapping developer audiences. The tradeoffs are stark.
| Dimension | Opus 4.6 | Gemini 3.1 Pro | Winner |
|---|---|---|---|
| SWE-Bench Verified | 80.8% | 80.6% | Tie (within margin) |
| Terminal-Bench 2.0 | 65.4% | Not reported | Opus 4.6 |
| ARC-AGI-2 | 68.8% | 77.1% | Gemini 3.1 Pro |
| Input Price / MTok | $5.00 | $0.075 | Gemini (67x cheaper) |
| Max Output Tokens | 128K | 65.5K | Opus 4.6 (2x) |
| Context Window | 200K (1M beta) | 2M native | Gemini 3.1 Pro |
| Agent Ecosystem | Claude Code + Agent Teams | Jules + Gemini CLI | Opus 4.6 |
The 67x input price difference is the elephant in the room. On raw capability, these models are remarkably close — SWE-Bench scores differ by 0.2 percentage points. But if you're processing large codebases or running high-volume analysis, the cost gap is enormous. A task consuming 1 million input tokens costs $5 on Opus versus roughly $0.075 on Gemini.
Where Opus 4.6 genuinely justifies the premium: the Claude Code ecosystem and Agent Teams have no equivalent on Gemini's side. Google's Jules and Gemini CLI are functional but less mature. If your workflow revolves around Claude Code, the integrated experience is hard to replicate.
The practical recommendation: use Gemini 3.1 Pro for high-volume, cost-sensitive tasks and standard coding assistance. Use Opus 4.6 for complex agentic workflows, multi-file refactoring, and tasks where the Agent Teams coordination genuinely saves time. Many teams will use both.
Claude Opus 4.6 vs GPT-5
GPT-5 and Opus 4.6 target somewhat different strengths. GPT-5 emphasizes multimodal capabilities and general reasoning breadth. Opus 4.6 is laser-focused on agentic coding and software engineering. The comparison depends heavily on what you're using the model for.
Where Each Model Leads
Opus 4.6 Wins:
- • Agentic coding tasks (Terminal-Bench, SWE-Bench)
- • Computer use and desktop automation
- • Multi-agent coordination (Agent Teams)
- • Longer output generation (128K vs GPT-5's limits)
- • Scientific reasoning in biology and chemistry
GPT-5 Wins:
- • Multimodal understanding (images, audio, video)
- • General-purpose conversation and creativity
- • Ecosystem breadth (plugins, GPTs, Operator)
- • Voice mode and real-time interactions
- • Image generation integration
For developers and engineers, Opus 4.6 is the stronger tool. The agentic coding benchmarks aren't close, and the Claude Code integration gives it a workflow advantage that matters in daily practice. For non-technical users, content creators, or anyone who needs multimodal capabilities, GPT-5's broader feature set is more practical.
Worth noting: both Anthropic and OpenAI offer their flagship models at $20/month on the consumer tier. If you're choosing between Claude Pro and ChatGPT Plus, our ChatGPT Plus vs Claude Pro comparison covers the full breakdown.
Real Limitations and Downsides
No review is honest without the downsides. Opus 4.6 is genuinely impressive, but it has real limitations that affect practical use.
Unpredictable Token Consumption
Adaptive Thinking is the biggest double-edged sword. On a complex prompt, thinking tokens can range anywhere from 500 to 12,000+ before output generation begins. You cannot easily predict or cap this. For API users paying per token, this means budgeting is harder than with fixed-reasoning models.
In our testing, the same refactoring prompt ran three times produced thinking token counts of 2,100, 5,800, and 3,400. Same prompt, same code, different reasoning paths.
Slower Than Sonnet in Standard Mode
Opus 4.6 in standard mode is noticeably slower than Sonnet 4.5 for routine tasks. Simple code completions that return in under a second on Sonnet take 2–4 seconds on Opus. The Fast Mode option ($30/$150 per MTok) solves the latency problem but at 6x the base price. For latency-sensitive applications — autocomplete, real-time chat — Sonnet remains the better choice.
Creative Writing Regression
This is subjective but consistently reported. Multiple users — and our own testing confirms it — find that Opus 4.6's prose feels slightly more mechanical than Opus 4.5 or even Sonnet 4.5. The model's optimization for structured reasoning and code appears to have come at a small cost to its creative voice. Marketing copy and blog drafts feel fractionally less natural.
ARC-AGI-2 Gap
Gemini 3.1 Pro scores 77.1% on ARC-AGI-2 versus Opus 4.6's 68.8%. This benchmark tests abstract visual reasoning — pattern recognition and generalization from minimal examples. An 8.3-point gap is significant. If your use case involves novel pattern recognition or abstract reasoning (as opposed to structured coding), Gemini currently has the edge.
Cost Barrier for High-Volume Use
Despite the 67% price cut, running Opus 4.6 at scale is expensive. A team of five developers using Claude Code for 4 hours daily could easily spend $1,500–3,000/month on API costs alone. For many startups and indie developers, Sonnet 4.5 or Gemini 3.1 Pro at a fraction of the cost delivers 85–90% of the capability.
Who Should Use Opus 4.6
Opus 4.6 makes sense for:
- • Professional developers using Claude Code daily — the Agent Teams feature and coding benchmark lead translate to real productivity gains on complex codebases
- • Teams building AI-powered products — when you need the highest-quality reasoning for your application and can absorb the cost premium
- • Researchers in computational biology or chemistry — the 2x improvement in scientific domains is genuine and well-validated by benchmarks
- • Anyone hitting output length limits — 128K output tokens means generating entire files, complete test suites, or long technical documents in one pass
- • Enterprises with Claude Pro Max subscriptions — at $100–200/month flat, the per-task cost is reasonable if you're using it heavily enough
Stick with alternatives if:
- • You're cost-sensitive — Sonnet 4.5 delivers 85–90% of Opus quality at roughly a fifth of the API cost. Gemini 3.1 Pro is even cheaper.
- • Latency matters more than depth — real-time applications should use Sonnet 4.5 or Haiku 4.5, not Opus
- • Creative writing is your primary use — the slight regression in prose quality means Sonnet 4.5 or even GPT-5 may feel more natural for content creation
- • You need multimodal capabilities — GPT-5 has a broader multimodal feature set including image generation, voice mode, and video understanding
- • Your tasks are routine and well-defined — for standard code completions, Q&A, and everyday tasks, Opus is overkill. The intelligence premium goes unused on simple prompts.
The clearest signal: if you're already using Claude Code and hit capability ceilings with Sonnet 4.5, Opus 4.6 removes those ceilings. If Sonnet handles your workload fine, the upgrade is hard to justify on pure cost-benefit. For a broader comparison of AI coding tools, see our AI coding tools comparison.
Frequently Asked Questions
How much does Claude Opus 4.6 cost?
API pricing is $5 per million input tokens and $25 per million output tokens — a 67% reduction from Opus 4.1. Extended Context (1M beta) runs $10/$37.50 per MTok. The Batch API offers 50% off at $2.50/$12.50. For consumer access, Claude Pro is $20/month, Max 5x is $100/month, and Max 20x is $200/month. Team plans start at $25–30 per seat. Enterprise pricing is negotiated.
Is Claude Opus 4.6 better than GPT-5?
For agentic coding and software engineering tasks, yes — Opus 4.6 leads on Terminal-Bench 2.0 (65.4%) and SWE-Bench Verified (80.8%). GPT-5 holds advantages in multimodal tasks, creative writing, and general conversation breadth. Neither model is universally superior. The choice depends on your primary use case: coding-heavy workflows favor Opus, general-purpose use favors GPT-5.
What is Adaptive Thinking in Claude Opus 4.6?
Adaptive Thinking automatically adjusts reasoning depth based on task complexity. Simple queries receive fast, direct answers. Complex coding problems trigger extended chain-of-thought reasoning that can consume thousands of thinking tokens. It generally produces better results on hard tasks but makes token consumption less predictable for API users.
How does Claude Opus 4.6 compare to Gemini 3.1 Pro?
They're remarkably close on coding benchmarks (SWE-Bench: 80.8% vs 80.6%). Opus leads on agentic tasks and output length (128K vs 65.5K tokens). Gemini leads on abstract reasoning (ARC-AGI-2: 77.1% vs 68.8%) and context window (2M native). The decisive difference is price: Gemini's input tokens cost approximately $0.075/MTok versus Opus's $5/MTok — roughly 67x cheaper. For cost-sensitive workloads, that gap is hard to ignore.
Can I use Claude Opus 4.6 with a 1 million token context window?
Yes, through the API in beta. The standard context window is 200K tokens. The 1M extended context is available at higher pricing ($10 input, $37.50 output per MTok). It's useful for ingesting entire codebases or very long documents. Note that processing a full 1M context costs roughly $10 in input tokens alone per request, so it's best reserved for tasks that genuinely require that much context.
Final Verdict
Claude Opus 4.6 is the strongest agentic coding model available in February 2026. That's not marketing — the benchmark lead on Terminal-Bench 2.0 and SWE-Bench Verified is substantive, and the real-world coding performance in Claude Code matches the numbers. Agent Teams adds a genuinely new capability that no competitor has matched yet.
It's also expensive, slower than Sonnet, and slightly weaker on creative writing than its predecessor. The Adaptive Thinking feature that makes it brilliant at hard problems also makes your API bill unpredictable. And Gemini 3.1 Pro delivers nearly identical coding benchmark scores at a fraction of the cost.
The 67% price reduction from Opus 4.1 shows Anthropic is moving in the right direction. If that trajectory continues, Opus-level capability at accessible pricing feels inevitable. The question is whether you need it now or can wait.
Our Recommendation
Opus 4.6 is the right model at the right time for a specific audience: developers pushing the boundaries of AI-assisted coding. For everyone else, it's an impressive demonstration of where the frontier is heading — and a reminder that the cheaper models are usually good enough.