# Grok 4.6 Took 53 Turns. Claude Opus 5 Took 103. **Summary:** Grok 4.6's strongest benchmark signal is its reported efficiency, not its tied composite score; I would shortlist it for repeated workload tests before making it the default. - Canonical: https://markhuang.ai/news/grok-4-6-53-turns-vs-103 - Language: en - Author: [Mark Huang](https://markhuang.ai/about) - Published: 2026-08-12 - Section: News - Tags: AI, Grok, Model Evaluation, AI Agents, AI Cost - Source: [Artificial Analysis](https://artificialanalysis.ai/articles/grok-4-6-benchmarks-and-analysis) - License: https://creativecommons.org/licenses/by-nc/4.0/ --- ![A compact AI engine moves work artifacts through a long evaluation course toward a final inspection gate](https://cdn.markhuang.ai/news/grok-4-6-53-turns-vs-103/hero.webp) *Grok 4.6 looks efficient on a demanding benchmark course. I still want the inspection gate at the end.* [Artificial Analysis reports](https://artificialanalysis.ai/articles/grok-4-6-benchmarks-and-analysis) that Grok 4.6 scored 61 on its Intelligence Index, level with GPT-5.6 Sol at its maximum setting. Fine. The number that made me stop was $0.84 per task. The same analysis says Grok 4.6 averaged about 53 turns and 0.5 billion input tokens on AA-Briefcase, versus roughly 103 turns and 2.0 billion input tokens for Claude Opus 5 at maximum effort. xAI also held standard API pricing at $2 per million input tokens and $6 per million output tokens. My read is that Grok 4.6 has earned a serious workload trial. It has not earned a blanket migration. xAI is pitching the model for long-running agents and ambitious technical work. If it reaches an acceptable result with fewer turns and fewer processed tokens, teams can afford to automate more. If the result still needs repair or fails unpredictably, the savings simply move into the review queue. ## The efficiency result is the useful news Leaderboard positions age quickly. Cost structure can change how a product is built. Artificial Analysis says Grok 4.6 gained five points over Grok 4.5 just over a month after that model's release. It reports an Elo of 1753 on GDPval-AA v2, 50.7% on the tool-using banking benchmark τ³-Banking, and 88.4% on Terminal-Bench v2.1. Those results put the model near the leaders across several agent-style workloads, rather than showing one isolated coding win. [xAI's launch post](https://x.ai/news/grok-4-6) makes the product case more directly. The company says Grok 4.6 is available in Cursor, Grok Build, Grok Bot, and its API. It describes supplemental training with curated model-generated reasoning and engineering data, followed by supervised fine-tuning and reinforcement learning on knowledge work and coding tasks, including web development and computer-aided design. I cannot independently verify the training recipe, and xAI does not disclose enough in that post to reproduce it. Still, the external benchmark pattern lines up with the stated goal: this release is aimed at agents that must keep working through a chain of steps. > **Info:** > > I would put Grok 4.6 into a controlled routing test now. I would measure accepted tasks per dollar, not promote it because two composite scores happen to match. ## Fifty-three turns can mean two different things The AA-Briefcase comparison is striking because long agent loops compound cost. More turns can mean more context carried forward, more tool activity, and more chances to wander. Reaching the finish in about half as many turns sounds like exactly the improvement agent products need. But turn count is not quality. A shorter run may be decisive, or it may stop early. A longer run may be wasteful, or it may catch its own mistake. Artificial Analysis gives Grok 4.6 an AA-Briefcase Elo of 1577, behind the Claude Opus 5 family, so the efficiency comparison should not be read as equal deliverables at one-quarter of the input. AA-Briefcase is also a private benchmark. Its [published description](https://artificialanalysis.ai/articles/aa-briefcase/) is useful: models work on realistic, long-horizon projects with thousands of source files and produce spreadsheets, presentations, memos, and other artifacts. Private tasks reduce the chance that a model has memorized the test. They also prevent a buyer from replaying the exact workload and inspecting every failure. So I see $0.84 per task as a reason to test, not a verdict. The model may sit at an unusually attractive point on the cost-capability curve. I still do not know what it will do with my repository, tool permissions, and acceptance rubric. ## The benchmark measures a contract, not my product Artificial Analysis documents its [Intelligence Index methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking) as a composite of evaluations across agents, coding, science, and general capability. That breadth is useful, but aggregation hides the workload shape. A model can match another at 61 while being better for terminal tasks and worse for a document workflow I care about. I go deeper into that measurement problem in [my guide to what AI benchmarks measure](/blog/what-ai-benchmarks-actually-measure). There is a second gap between benchmark success and production reliability. [ReliabilityBench](https://arxiv.org/abs/2601.06112) argues that single-run success misses consistency under repeated execution, robustness to equivalent prompt changes, and tolerance of tool or API failures. An [IBM study of production agents](https://research.ibm.com/publications/measuring-agents-in-production) likewise reports that consistent correct behavior remains the top development challenge and is usually addressed through systems design around the model. None of this invalidates Grok's result. It tells me what to do next. I would repeat each representative task, change the wording, inject a tool failure, and count silent damage as a failure even when the final artifact looks polished. This is also where the early public reaction is useful. In a [Cursor discussion](https://www.reddit.com/r/cursor/comments/1vmibtf/grok_46_amazing/), some commenters welcomed the apparent efficiency gains, while another questioned why benchmark charts drive model discussion when Grok 4.5 did not feel comparable to the leaders in use. That thread is anecdotal and too early to settle anything. It does surface the right objection: a chart can nominate a candidate, but repeated work decides whether the candidate stays. ## How I would run the trial I would not begin with a broad model swap. I would choose 20 to 50 tasks from one expensive workflow, including cases that usually require intervention. Every candidate would receive the same tools, context, time limit, and acceptance checks. I would run each task more than once. The ledger would include total input and output cost, wall-clock time, turns, retries, tool errors, reviewer minutes, and accepted results. I would also record severe failures separately. One corrupted file or confident bad action can matter more than several cheap successes. Grok 4.6 has a 500,000-token context window, according to Artificial Analysis, but I would not reward it for filling that window. I would reward it for using only the context needed to finish correctly. The reported 53-turn result is promising because it suggests less churn. A local eval should determine whether that economy survives outside AA-Briefcase. Price also needs the whole path. xAI says the fast variant costs twice the standard rate, and Artificial Analysis notes that cache hits cost $0.50 per million tokens, up from $0.30 for Grok 4.5. Depending on latency needs and cache behavior, a team's bill may not look like the headline $2/$6 rate. ## What would make me switch I would switch a workload when Grok 4.6 produces more accepted results per dollar without increasing severe failures or reviewer time. It does not need to win every prompt. It needs a stable lane where the cheaper run stays cheaper after verification. That standard is tougher than scoring 61, but it gives Grok 4.6 room to prove its apparent strength. A fair trial should let its efficiency compound across real tasks, then charge it for every retry and repair. For now, I would move Grok 4.6 onto the shortlist and resist moving it to the default. The benchmark has done its job: it found a candidate worth spending evaluation time on. The next score belongs to the buyer.