Grok 4.6 vs Claude Opus 5: Which Model Wins?

Published August 12, 2026

Grok 4.6 and Claude Opus 5 are both built for the work that exposes weaker models: repository-scale coding, long-running agents, research, documents and professional deliverables. Claude is the better high-stakes default. Grok is the more compelling value, delivering near-frontier scores at a fraction of Opus 5’s measured task cost.

Verdict

Overall: Claude Opus 5, narrowly, for its stronger measured capability, 1-million-token context and broader enterprise availability.
Coding: Claude Opus 5.
Agents: Claude Opus 5 for success rate; Grok 4.6 for cost-sensitive scale.
Research: Claude Opus 5.
Long context: Claude Opus 5.
Best value: Grok 4.6.
Enterprise: Claude Opus 5.
Qualification: Grok 4.6 launched today. Its early third-party results are strong, but the evidence base is much younger.

At a glance

What exactly are we comparing?

This is a comparison of the canonical API models grok-4.6 and claude-opus-5, not router labels or informal nicknames. xAI released Grok 4.6 on August 12, 2026 as its frontier model for coding, agents and knowledge work. The API has a 500,000-token window, image input and text output. Anthropic released Claude Opus 5 on July 24, 2026. Its API has a 1-million-token window, a 128,000-token synchronous output ceiling and adaptive thinking enabled by default. xAI model documentation, Anthropic model documentation.

Versioning matters. Anthropic says claude-opus-5 is a pinned snapshot even though its ID has no date. xAI’s model documentation describes bare model names as aliases that can automatically migrate to a later stable version, while dated names remain fixed. Buyers who need reproducibility should not assume the two IDs have equivalent immutability. Anthropic versioning guide, xAI model alias policy.

The effort settings in headline results are also different. Artificial Analysis tested Grok 4.6 at high and Claude Opus 5 at max. That is a fair reflection of each tested configuration, but it is not an equal-compute contest. Anthropic’s own system-card summary normally uses adaptive thinking at max effort and five trials; xAI’s launch table reports Grok at high without publishing the same level of trial detail. Those scores can guide a buying decision, but they should not be mistaken for a controlled Kingy.ai head-to-head.

Benchmark results: what survives an audit

The cleanest comparison is Artificial Analysis Intelligence Index v4.1.1 because one independent organization ran both models through the same nine-evaluation composite. Grok 4.6 scored 61 at high effort; Claude Opus 5 scored 63 at max. The index includes GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1, SciCode, Humanity’s Last Exam, GPQA Diamond, CritPt, AA-Omniscience and AA-LCR. The two-point lead favors Claude, but the effort mismatch and composite construction make it directional rather than absolute. Artificial Analysis’s July 24 Opus launch article originally reported 61; its current Opus model page and August 12 Grok report show 63, so this article uses the current value at the research cutoff. Grok 4.6 analysis, current Claude Opus 5 model page.

The category results tell a more useful story. On GDPval-AA v2, which uses real occupational tasks and the open-source Stirrup agent harness, Opus 5 scored 1861 Elo and Grok 4.6 scored 1753. On AA-Briefcase, a private long-horizon knowledge-work benchmark combining rubric success, analytical quality and presentation quality, Opus scored 1720 and Grok 1577. Those are meaningful Claude wins for professional work. Artificial Analysis also found that Grok completed Briefcase tasks in roughly 53 turns and about 0.5 billion input tokens on average, while Opus 5 max used roughly 103 turns and 2.0 billion. Claude produced the stronger result; Grok got much closer than its cost would suggest. Artificial Analysis Grok report.

Terminal-Bench v2.1 is nearly a tie: 88.4% for Grok 4.6 and 89% for Opus 5 in Artificial Analysis testing. This is strong evidence that Grok is not merely a cheap chat model. It can compete on terminal-based software tasks. It does not prove equal performance on large repositories, code review or multi-day engineering work.

Vendor-published coding results point in the same direction but deserve a weaker label. xAI reports 65.9% for Grok 4.6 high on DeepSWE v1.1; Anthropic reports 68.8% for Opus 5 max averaged over five trials. On FrontierCode v1.1 Extended, xAI reports 61.3% for Grok high while Anthropic reports Opus 5’s best score of 63.6% at medium effort, from an evaluation run and scored by Cognition. Claude leads both, but different effort levels and disclosure depth prevent a clean winner row. xAI launch evaluation table, Claude Opus 5 system card.

Do not turn those percentages into a homemade intelligence average. They represent different task sets, harnesses, token budgets and grading rules.

What the benchmark gap actually means

A 63–61 result does not mean Claude is “3.3% smarter.” The Intelligence Index normalizes nine evaluations with its own weights, and a two-point difference can hide larger wins and losses underneath. In this case the more actionable gap appears in agentic professional work: Opus leads by 108 Elo on GDPval-AA and 143 Elo on AA-Briefcase. Grok’s near-tie on Terminal-Bench tells us its relative strength is different. It is especially competitive when the task is expressed through a terminal and success can be checked by execution.

The index also rewards the chosen operating point. Opus 5 max spent more tokens to reach 63. Artificial Analysis recorded 100 million output tokens across its index evaluation, while Grok high produced 72 million. A buyer who deploys Opus at medium or high rather than max may get lower scores and lower cost; a buyer who raises Grok from high to xhigh may see a different balance. Neither vendor has supplied a common effort-to-compute conversion, so the labels themselves are not comparable units.

Confidence intervals deserve more attention than leaderboard rank. Artificial Analysis says Grok’s GDPval-AA confidence interval overlaps those of Fable 5 and Qwen3.8 Max, but describes Opus 5 as the leader. A ranking without intervals can make a small, noisy difference look categorical. The article therefore treats repeated, same-owner patterns as evidence and single-point vendor leads as signals.

There is another launch-day trap: benchmark selection. xAI’s announcement foregrounds evaluations where Grok 4.6 performs well, just as Anthropic’s launch foregrounds Opus strengths. That is normal product marketing, not proof of misconduct. The defense is to use an independent suite that contains both models, then inspect several workload-specific evaluations rather than adopting either vendor’s preferred scoreboard.

Coding and software engineering

Claude Opus 5 is the safer pick for teams delegating difficult repository work. Its audited lead is not huge, but it is consistent across DeepSWE, FrontierCode and broader agentic knowledge-work testing. Anthropic also reports 79.2% on SWE-bench Pro and 96.0% on SWE-bench Verified, both averaged over five trials. There is no equivalently documented Grok 4.6 score in the launch material, so those figures establish Claude’s capability but cannot serve as a direct win over Grok. Claude Opus 5 system card.

The interesting caveat is overwork. Anthropic says Opus 5’s FrontierCode result declines above high effort because the model sometimes changes more than the task requires. It reached its best Main and Extended results at medium effort. That is a practical warning for code agents: more reasoning budget can create broader diffs, not just better ones. Explicit scope constraints and repository tests still matter.

Grok’s case is efficiency. It is almost level on Terminal-Bench v2.1, close on the two vendor-published coding comparisons, and far cheaper per token. For CI repair bots, codebase triage and high-volume implementation tasks where engineers review every patch, that trade can be excellent. For a sensitive migration or a difficult root-cause investigation where one missed edge case dominates cost, Opus 5’s higher success ceiling is worth the premium.

Coding winner: Claude Opus 5. Value-coding winner: Grok 4.6.

Agents and tool use

Both models support the core ingredients of modern agents: reasoning controls, function calling, structured output and server-side tools. xAI documents web search, X search, code execution, collections search and remote MCP. Anthropic offers web search and fetch, code execution, computer-use tooling and managed-agent infrastructure. Feature availability varies by endpoint and cloud, so “supports tools” does not mean every surface exposes the same tool set.

Opus 5 leads the best comparable professional-agent measures. Its 1861 GDPval-AA score and 1720 AA-Briefcase score are the strongest evidence in this matchup. Anthropic additionally reports 70.6% on OSWorld 2.0, averaged over five runs, and 26.0% on AutomationBench. Grok’s launch materials do not provide directly comparable results for those exact configurations, so they strengthen the Opus case without creating a valid head-to-head row.

Grok’s counterargument is operating economics. Artificial Analysis measured $0.84 per average Intelligence Index task for Grok versus $2.03 for Opus. On Briefcase, Grok used about half as many turns and one-quarter the input tokens. An agent that is slightly weaker but dramatically cheaper may complete more useful work under a fixed budget, especially when tasks are retryable and outputs are reviewed.

The buying rule is simple. Use Opus when the agent will make consequential decisions, coordinate long work or run with limited oversight. Use Grok when you can supervise, retry and optimize for throughput.

Agent winner: Claude Opus 5 on completion quality; Grok 4.6 on cost-adjusted scale.

Reasoning, research and factual reliability

Claude’s lead on GDPval-AA and AA-Briefcase makes it the stronger recommendation for research teams, analysts and professional knowledge workers. Its system card also reports 64.7% on Humanity’s Last Exam with tools and 56.3% without, both at max effort in the summary table. Those runs used web search, fetch, programmatic tool calling and code execution, with contamination controls for the tool-enabled variant. Grok’s same-index performance remains close, but its launch-day documentation does not expose an equivalent, audited HLE configuration.

That does not make either model a source of truth. Artificial Analysis found Opus 5 max improved factual accuracy over Opus 4.8 but also answered more often when uncertain, producing a 50% hallucination rate on AA-Omniscience. That benchmark has a particular abstention-sensitive design, so 50% is not a universal real-world hallucination rate. It is still a useful warning: higher reasoning scores do not remove the need for citations and source checks. Artificial Analysis Opus 5 report.

Grok’s native access to X search can be useful for live social and news discovery, while both platforms can search the web when the relevant server-side tool is enabled. Search access is not the same as factual reliability. Research workflows should log queries, preserve links and verify claims against primary sources regardless of model.

Research and complex-reasoning winner: Claude Opus 5.

Instruction following and professional knowledge work

Instruction following is easy to underestimate because both models can produce polished text. The hard version is maintaining a schema, respecting exclusions, distinguishing evidence from inference and recovering after a tool fails. Neither launch package provides a clean, same-prompt IFBench or IFEval comparison for these exact configurations. The Artificial Analysis index includes evaluations that indirectly punish failures to complete a task, but that is not a substitute for a dedicated stress test.

The available evidence still favors Opus for professional work. GDPval-AA covers tasks across 44 occupations, while AA-Briefcase grades analytical quality, presentation and rubric compliance over long-horizon deliverables. Opus leads both. Anthropic’s AutomationBench, spreadsheet, document and agent evidence points in the same direction, although much of it is first-party and cannot independently prove performance in a buyer’s templates or office stack.

Grok’s efficiency changes how teams should pilot it. Its lower turn count on AA-Briefcase may indicate more direct execution, but fewer turns are only beneficial when the final artifact is correct. A serious procurement test should score omissions, unauthorized actions, invented inputs and repair behavior, not just whether the final document looks finished. It should also run multiple trials: a model that passes four times out of five still creates a review obligation on every run.

For regulated or approval-heavy work, configure both models to make assumptions explicit and stop before external actions. Structured output support helps enforce a response shape, but it does not guarantee truthful fields. Validation belongs outside the model: schemas, deterministic checks, source allowlists and human approval gates remain part of the system.

Professional knowledge-work winner: Claude Opus 5. Dedicated instruction-following head-to-head: inconclusive.

Long context and multimodal work

Claude has the straightforward specification win: 1 million tokens versus Grok’s 500,000. Anthropic charges the same base per-token rate across its full window. xAI doubles all Grok 4.6 token rates when a prompt reaches 200,000 tokens: input rises from $2 to $4 per million, cached input from $0.50 to $1, and output from $6 to $12. Anthropic pricing, xAI pricing.

Advertised capacity is not effective retrieval. Anthropic provides one useful but vendor-run stress test: on ProgramBench, where agents rebuild programs from binaries and documentation across five episodes of up to 1 million tokens each, Opus 5 rose from an 83% hidden-test pass rate after one episode to 93% after five. Grok has no equivalent published 4.6 result in the materials reviewed. This supports Claude for large document bundles and codebases, but independent long-context replication would improve confidence.

Both accept images and return text. Claude’s system card includes broader chart, document, GUI and vision-agent evaluations; xAI’s launch emphasizes visual and interactive application building but provides less directly comparable multimodal evidence. Claude therefore gets a low-confidence practical edge, not a definitive visual-intelligence win.

Long-context winner: Claude Opus 5. Multimodal winner: Claude Opus 5, low confidence.

Speed, pricing and value

Grok is the price-performance winner. Below 200,000 prompt tokens, its published API price is $2 per million input tokens and $6 per million output tokens. Opus costs $5 and $25. Cached input is $0.50 for both, although Anthropic charges $6.25 per million for a five-minute cache write and $10 for a one-hour write. Grok’s public table lists the hit price but not an equivalent cache-write line item. Artificial Analysis measured 85.8 output tokens per second for Grok and 54.1 for Opus through first-party APIs. Grok model measurement, Opus model measurement.

Consider a monthly workload of 100 million uncached input tokens and 20 million output tokens, all below Grok’s long-context threshold. Grok costs $320; Opus costs $1,000. With 80% of input served as cache hits and excluding cache-write charges, Grok costs $200 and Opus $640. If every prompt crosses 200,000 tokens, Grok rises to $640 while Opus remains $1,000. These examples assume standard processing, no tool charges, no regional premium and identical token consumption; real agents rarely consume identical tokens.

Batch pricing changes the picture. Anthropic discounts Opus batch input and output by 50%, to $2.50 and $12.50 per million. xAI’s current batch table does not list Grok 4.6 for a discount, so it receives none under the published policy. On the same uncached monthly workload, batch Opus would cost $500 versus Grok’s $320. Grok still wins, but not by three to one. xAI priority processing doubles token prices; Anthropic Fast mode doubles Opus pricing to $10 input and $50 output per million and is a research preview on the first-party API only.

Artificial Analysis’s measured task economics are even more persuasive: 61 index points at $0.84 per average task for Grok, versus 63 at $2.03 for Opus. That is not “cost per successful task” because the index mixes differently scored evaluations. It is the best available normalized cost signal, and it strongly favors Grok.

Value winner: Grok 4.6.

Everyday interactive use

API charts do not settle which chat product feels better. Consumer surfaces add system prompts, memory, connectors, search, file handling, usage caps and routing. They may also expose different latency and safety behavior from a raw API call. The exact same user prompt can therefore produce a different experience in Grok, Claude, Grok Build, Claude Code or a third-party editor even when the underlying model name is identical.

Claude is the better recommendation for users whose daily work involves long documents, careful drafting, code review and multi-step analysis. The 1M context window gives it more room, and Opus 5 is explicitly available on Pro, Max, Team and Enterprise. Anthropic describes it as the default model on Max and the strongest model on Pro. That does not mean every subscriber receives unlimited Opus use; plan caps should be checked at purchase time.

Grok is attractive for users who care about current social context, rapid iteration and the ability to move between Grok chat and Grok Build. Its X-search integration is distinctive, while the same independent measurement that found lower task cost also found faster output generation. Yet xAI’s launch page does not provide enough plan-level detail to compare message allowances or priority access with Claude. A consumer winner would require testing the products, not just the endpoints.

The practical answer is workload-specific. For a lawyer reviewing a large record, a researcher building a sourced brief or a developer handing over a difficult repository task, start with Opus. For a founder exploring current market chatter, an engineer running many supervised iterations or a user sensitive to response speed, start with Grok. Keep search results auditable in both.

Everyday-use verdict: Claude for depth; Grok for live, fast iteration. Overall confidence is low without consumer-product testing.

Safety, privacy and enterprise considerations

Anthropic offers the broader documented deployment footprint: its first-party API, Amazon Bedrock, Claude Platform on AWS, Google Cloud and Microsoft Foundry. It also documents global, regional and US-only inference options; US-only inference adds 10% to token prices. Opus 5 is available in Claude Pro, Max, Team and Enterprise. That breadth reduces procurement friction for companies already standardized on a hyperscaler.

xAI confirms Grok 4.6 in its API’s US East and US West regions and names several launch partners, but does not confirm this exact model on Bedrock, Google Cloud or Microsoft Foundry in the sources reviewed. Do not infer 4.6 availability from documentation for another Grok model.

Both vendors say API customer data is not used for training by default without permission. Both describe a standard 30-day API retention window with exceptions and offer zero-data-retention arrangements or settings. xAI warns that ZDR disables stateful Responses, Files, Collections, Batch and other storage-dependent features. Anthropic’s commercial retention documentation similarly distinguishes standard retention, negotiated ZDR and longer-lived services. xAI API security FAQ, Anthropic retention policy.

On safety, Anthropic publishes a 190-plus-page system card with capability, alignment and safeguard testing. xAI’s launch says Grok 4.6 received its widest pre-deployment suite and improved safeguards, but the reviewed public launch materials provide less methodological detail. Anthropic’s stronger documentation should count in enterprise diligence, while neither vendor’s self-evaluation replaces a buyer’s red-team and data-flow review.

Enterprise winner: Claude Opus 5.

Which model should you choose?

Individual developers: Start with Grok 4.6 if you pay per token and review your own work. Choose Opus 5 for the hardest bugs, architecture changes and final review.

Coding-agent power users: Choose Opus 5 when you want the best chance of a clean, complete result with less supervision. Choose Grok when throughput and budget matter more than a small success-rate edge.

Startups: Grok is the pragmatic default for high-volume product features and internal agents. Route expensive or consequential tasks to Opus if your architecture supports model selection.

Research teams: Choose Opus 5. Its professional-work and agentic-search evidence is stronger, though every factual claim still needs source verification.

Content and knowledge workers: Choose Opus 5 for large document sets, polished professional artifacts and complex analysis. Choose Grok when live X discovery or lower API cost is central.

Enterprises: Choose Opus 5 when multi-cloud availability, regional routing and mature documentation drive procurement. Evaluate Grok where its economics justify a narrower platform footprint.

Cost-sensitive API workloads: Choose Grok 4.6 below 200,000 prompt tokens. Recalculate for very long prompts and batch workloads because Grok’s surcharge and Anthropic’s batch discount narrow the gap.

Consumer-chat users: Opus 5 is the strongest Opus on Claude Pro and the default on Max; Grok 4.6 is presented as available from Grok’s consumer launch path. Compare actual plan limits and surface tools, not API benchmark charts, before subscribing.

Weaknesses and deal-breakers

Grok 4.6

The biggest weakness is evidence age. The model launched on the day of this review. Artificial Analysis offers valuable same-day independent testing, but there has not been time for broad reproduction across agent frameworks and real codebases. Its 500k context window is half Claude’s, and its all-token price doubles once the prompt reaches 200k. Its exact hyperscaler availability, separate maximum output cap, fine-tuning support and consumer/API equivalence are not fully disclosed in the reviewed sources.

Claude Opus 5

The deal-breaker is price. Output costs $25 per million tokens, and max effort can be verbose: Artificial Analysis recorded 100 million output tokens across its index run, versus 72 million for Grok. Opus is also slower in that organization’s API measurement. Higher effort is not automatically better; Anthropic observed scope creep on FrontierCode above high effort. Finally, safety fallbacks can change which model answers a flagged request on some surfaces unless buyers understand and control the configuration.

Final verdict

Choose Grok 4.6 when you need frontier-adjacent capability at scale and can tolerate a small measured quality gap. It is the better economic engine for supervised agents, interactive tools and API products. Its lower token prices, faster output and $0.84 measured average index-task cost are hard to ignore.

Choose Claude Opus 5 when the work is difficult enough that one failed task costs more than the token bill. It leads the strongest same-owner evaluations, has twice the context, stronger public safety documentation and a much broader enterprise deployment story. That makes it the overall winner, but not the universal default.

The evidence is inconclusive on a fully controlled multimodal head-to-head, consumer-chat experience and Kingy.ai’s own real-world task suite. The single development most likely to change this verdict would be reproducible, multi-trial testing showing Grok matching Opus on repository coding and professional agents while preserving its measured cost advantage.

Methodology and limitations

• Research was conducted from first-party xAI and Anthropic launch pages, API documentation, pricing pages, versioning documentation, privacy documentation and Anthropic’s Opus 5 system card; benchmark-owner evidence came primarily from Artificial Analysis.
• Research cutoff: August 12, 2026, 11:44:43 a.m. PDT (UTC−07:00).
• No authenticated calls to either exact model were run. No Kingy.ai hands-on claims or test-results table are included.
• Artificial Analysis results compare Grok high with Opus max; the organization’s shared harness improves comparability but does not equalize test-time compute.
• Vendor-published results were included only where decision-relevant and were labeled directionally comparable or not directly comparable.
• Consumer apps and APIs may have different system prompts, tools, routing, safety fallbacks, limits and latency.
• No affiliate relationship or conflict was disclosed for this analysis. Kingy.ai should add any site-level commercial relationship before publication.
• Not tested: fine-tuning, consumer-plan latency and caps, production rate-limit behavior, regional data paths, multimodal OCR under controlled conditions, or cost per successful Kingy.ai task.
• Volatile specifications, pricing, model status and benchmark pages were rechecked at the stated cutoff. Because Grok 4.6 launched that day, a 7–14 day post-launch update is editorially advisable.

Frequently asked questions

Is Grok 4.6 better than Claude Opus 5?

Not overall. Claude Opus 5 has the stronger measured capability, longer context window and broader enterprise availability. Grok 4.6 is the better value and is preferable for many cost-sensitive API workloads.

Which model is better for coding?

Claude Opus 5 is the safer choice for difficult repository work and low-supervision coding agents. Grok 4.6 is attractive for supervised, high-volume coding because its results are close and its API is cheaper.

Which model is cheaper through the API?

Below 200,000 prompt tokens, Grok 4.6 costs $2 per million input tokens and $6 per million output tokens, versus $5 and $25 for Claude Opus 5. Grok rates double for prompts of at least 200,000 tokens, while Claude retains standard rates across its full context window.

Which model has the larger context window?

Claude Opus 5 has a 1-million-token context window. Grok 4.6 has a 500,000-token context window.

Which is better for autonomous agents?

Claude Opus 5 leads the strongest shared professional-agent evaluations. Grok 4.6 is more cost-efficient, making it a strong choice when tasks can be supervised or retried.

Can Grok 4.6 and Claude Opus 5 search the live web?

Yes, when the relevant server-side tools are enabled. xAI offers web and X search; Anthropic offers web search and fetch. Tool availability and charges depend on the product surface and API configuration.

Are Grok 4.6 and Claude Opus 5 benchmark scores directly comparable?

Some Artificial Analysis results are directionally comparable because the same evaluator ran both models. Most vendor-reported figures use different effort settings, trial counts or harnesses and should not be treated as direct head-to-head wins.

Which model is better for business use?

Claude Opus 5 is the stronger enterprise default because of its professional-work results, 1-million-token context and availability across major cloud platforms. Grok 4.6 may be better when API economics dominate the decision.

Research cutoff: August 12, 2026, 11:44:43 a.m. PDT (UTC−07:00). Grok 4.6 launched on the research date; independent evidence may evolve quickly.