Grok 4.6 vs Claude Opus 5: Same 61, Two Different Economies

Artificial Analysis runs every frontier model through the same nine evaluations and publishes the invoice. On its Intelligence Index, Grok 4.6 and Claude Opus 5 land on the same number — 61 — which makes this the rare head-to-head where the composite cannot tell you which to use. The thing that can is a less famous figure from the same source: on the AA-Briefcase knowledge-work suite, Grok 4.6 finished a task in roughly 53 turns and about half a billion input tokens, while Claude Opus 5 at max effort needed roughly 103 turns and two billion tokens for the same work. That is a four-to-one gap in token consumption between two models whose headline score is identical, and it shows up only when you stop comparing model cards and start comparing bills.

Both models are current flagship launches, which keeps this a clean comparison rather than a generation mismatch. Grok 4.6 shipped on August 12, 2026 from SpaceXAI as a refinement of the Grok 4.5 foundation — the same 1.5-trillion-parameter mixture-of-experts base, retrained with longer supervision and reinforcement-learning runs aimed at long-running agents. Claude Opus 5 shipped on July 24, 2026 from Anthropic as a fixed-datestamp flagship with adaptive thinking, a 1-million-token context window, and image and file input. They sit at the same point on the independent scoreboard and opposite points on the price sheet: $2 / $6 per million tokens for Grok 4.6 against $5 / $25 for Claude Opus 5. The interesting work is figuring out which half of that sentence decides your production choice.

The 61 that hides a four-to-one efficiency gap

The Intelligence Index is a composite of nine evaluations, which is its strength and its blind spot. A single number averaging reasoning, math, coding, and knowledge-work scores hides how a model gets there — and these two get there oppositely. Artificial Analysis's own briefcase-style professional knowledge-work suite is the clearest window into that: it measures real long-horizon tasks, and it logged Grok 4.6 at about 53 turns and 0.5B input tokens per task versus Claude Opus 5 Max at roughly 103 turns and 2B input tokens. On per-task economics that is the difference between a model that finishes a research-and-produce job in a quarter of the tokens and one that burns through them.

The cause is a design choice, not a bug. Grok 4.6 was trained specifically to verify its own work as it goes and to stop when a subtask is genuinely complete — xAI describes long task trajectories with self-testing as the training objective. Claude Opus 5 is trained for depth of reasoning and breadth of knowledge, and its default behavior is to think harder and consult more than is strictly necessary. Both are "reasoning models"; they allocate effort differently, and the allocation is the spec that matters for agent workloads.

The gap matters most for agents that call a model in a loop — research agents, code-agents that run dozens of turns, document pipelines that process batches overnight. Every turn is billed, and every input token is context that must be re-sent on the next call. A model that finishes in 53 turns at 0.5B tokens is not just cheaper per token; it is cheaper per task by roughly an order of magnitude than one that takes 103 turns at 2B tokens, before you even multiply by the list price.

The spec sheet, both sides

Where they differ on paper, on one line each:

• Intelligence Index — Grok 4.6 scores 61, ranked #6 of 184; Claude Opus 5 scores 61, ranked alongside at the top of the board. A tie, per Artificial Analysis.

• List price per 1M tokens — Grok 4.6 at $2 in / $6 out (doubling to $4 / $12 for prompts at or above 200K input tokens); Claude Opus 5 at $5 in / $25 out, with a 50% batch discount to $2.50 / $12.50.

• Context window — 500K tokens for Grok 4.6 versus 1,000,000 for Claude Opus 5, the single clearest spec advantage either model holds.

• Max output — Claude Opus 5 allows up to 128K output tokens (300K via the batch API); Grok 4.6's output ceiling is lower, in the range of a typical long answer.

• Inputs — both accept text and image; Claude Opus 5 also accepts files, and Anthropic's version of multimodal input reaches into documents more directly.

• GDPVal-AA v2 (knowledge work) — Grok 4.6 at 1,753 Elo versus Claude Opus 5 at 1,852 Elo. Opus 5 takes the knowledge-work quality crown on the measure where the briefcase efficiency favored Grok. [Artificial Analysis]

• CursorBench v3.2 — 69.9% for Grok 4.6 versus 70.0% for Claude Opus 5, effectively even on the coding-agent benchmark both were measured on. [Artificial Analysis]

• Reasoning control — Claude Opus 5 exposes an effort dial from low to max and adaptive thinking on by default; Grok 4.6 exposes reasoning effort but is tuned toward a default that favors long agent trajectories.

The point of laying these side by side is that neither model dominates. Claude Opus 5 is the better generalist on paper: more context, higher output ceiling, file input, higher knowledge-work quality. Grok 4.6 is the better value and the more efficient agent. The only question that remains is which of those describes the work you actually have.

Where Claude Opus 5 earns its premium

Anthropic's launch numbers — vendor-reported, not independently reproduced — put Claude Opus 5 at 96.0% on SWE-bench Verified and 79.2% on SWE-bench Pro, with a 70.6% OSWorld score for computer use. On the broad composite, its 61 matches Grok 4.6, and on GDPVal-AA it is measurably ahead. What that translates to in practice is a model that holds its own across the full span of a knowledge worker's day: drafting, analysis, long-document reasoning, image-plus-text tasks, and coding all in one place with a million tokens of runway.

The context window is the honest reason to reach for it. A 500K-token limit is large, but it is not a whole codebase plus a research corpus plus the intermediate results of a long agent session. Teams doing deep single-pass analysis over very large corpora — a full repository, a year of financial filings, a long PDF stack — will bump into Grok 4.6's ceiling and never hit Claude Opus 5's. For those workloads the extra context is not a luxury; it is the difference between fitting the job and orchestrating around the limit.

Where Grok 4.6 is the better buy

Flip the same facts and Grok 4.6 is the better buy for agent pipelines, and not by a small margin. It is 2.5× cheaper on input and 4.2× cheaper on output against Opus 5's list price, and its per-task token efficiency compounds that: the AA-Briefcase numbers suggest roughly 4× fewer tokens and 2× fewer turns for the same knowledge-work outcome. Multiply 4× by 2.5× and a task that costs $1.00 on Opus 5's economics costs on the order of $0.10 on Grok 4.6's — before batch discounts on the Opus side close some of the gap.

On agentic coding specifically, Grok 4.6's DeepSWE 1.1 result improved from 54% to 65.9% between 4.5 and 4.6, and its CursorBench score sits within half a point of Opus 5's while self-verifying its own work along the way. For a team running thousands of agentic calls a day where each call is a multi-turn loop, the per-task economics and the shorter trajectories are the difference between a viable product and a burn rate. That is the case xAI is making, and the independent efficiency data supports the direction of it.

The routing decision

This is the matchup where a router earns its keep, because the right answer is "both, with the split defined by workload shape." Grok 4.6 and Claude Opus 5 are both live on OrcaRouter at each vendor's own list price — $2 / $6 for grok/grok-4.6, $5 / $25 for anthropic/claude-opus-5 — with 0% markup, so the number in your cost model is the number that gets billed. The plumbing that makes the split practical: one API key for both models, automatic failover if one provider's endpoint degrades in the middle of a long agent run, and a routing DSL that can send short, high-volume turns to Grok 4.6 and escalate the long-horizon, context-hungry work to Claude Opus 5 within a single call. You do not need a second contract to run a two-tier architecture, and you can re-tune the split as real traffic data comes in instead of guessing from model cards.

The honest read

The tie on the composite index is real and it is a trap. Same-score models are not interchangeable; they are differentiated precisely where the score is blind. Claude Opus 5 is the safer default for broad, context-heavy, document-and-image knowledge work where a 1M-token window and deep reasoning matter more than cost. Grok 4.6 is the better default for high-volume agent loops where per-task token efficiency and a 2.5× price advantage compound into an order-of-magnitude bill difference. If your workload is the former, pay for Opus 5 and stop worrying about the price. If it is the latter, the 61-on-paper parity is the best reason you will ever have to test Grok 4.6 first — the benchmark says they are equal, and the invoice says they are not.