SpaceXAI debuts Grok 4.6, overtaking Kimi K3's performance and matching GPT-5.6 Sol for world's third best on Artificial Analysis

Model

Input ($/1M)

Output ($/1M)

Total ($/1M)

Source

Muse Spark 1.2 Contributor

$0.10

$0.20

$0.30

deepseek-v4-flash

$0.14

$0.28

$0.42

GPT-5.6 Luna

$0.20

$1.20

$1.40

LongCat-2.0 — limited-time promo

$0.30

$1.20

$1.50

LongCat-2.0 — standard

$0.75

$2.95

$3.70

Muse Spark 1.1 / 1.2

$1.25

$4.25

$5.50

Grok 4.6 — <200K prompt tokens

$2.00

$6.00

$8.00

Qwen3.8-Max

$2.00

$6.00

$8.00

GPT-5.6 Terra

$2.00

$12.00

$14.00

GPT-5.4

$2.50

$15.00

$17.50

Claude Opus 5

$5.00

$25.00

$30.00

GPT-5.6 Sol — Standard mode

$5.00

$30.00

$35.00

GPT-5.6 Sol — Fast mode

$10.00

$60.00

$70.00

.

It's also available in SpaceX's recent acquisition of the AI coding startup Cursor, and from partners including OpenRouter, Vercel and Cloudflare.

SpaceXAI is providing twice the included usage for Grok 4.6 in Cursor and Grok Build during the first week.

The release arrives only weeks after Grok 4.5, which SpaceXAI launched in July as a model targeting coding, agentic tasks and knowledge work, and one day after the launch of (human preference of head-to-head model outputs, adapted from chess) of 1,753 on at a reported $0.84 per task — which actually makes it less of a bargain than its predecessor, Grok 4.5, and less economical than OpenAI's GPT-5.6 Luna, z.ai's GLM-5.2, and Meta's new Muse Spark 1.2, among other models.

Artificial Analysis also reports that Grok 4.6 completed its AA-Briefcase workloads in roughly 53 turns and about 0.5 billion input tokens on average, versus approximately 103 turns and 2 billion input tokens for Claude Opus 5 Max.

Those measurements do not prove that every production agent will use fewer tokens or finish twice as quickly. Agent costs depend heavily on harness design, prompts, tool calls, caching, retry behavior and the task itself. But they point toward an increasingly important enterprise metric: the cost of completing a workflow, rather than simply the cost of generating one million tokens. That distinction is central to SpaceXAI’s positioning.

The standard Grok 4.6 API starts at $2 per million input tokens and $6 per million output tokens, and SpaceXAI also offers a faster variant at twice the price.

The supplied API documentation adds an important caveat for long-context deployments. Grok 4.6 supports a 500,000-token context window, but prompts below 200,000 tokens are billed at $2 per million input tokens, $0.50 per million cached-input tokens and $6 per million output tokens. Once a prompt reaches 200,000 tokens, those rates rise to $4, $1 and $12 respectively, with the higher pricing applying to all tokens in that request.

That means enterprises should not extrapolate the $2/$6 headline pricing across the model’s entire context window when estimating total cost of ownership.

Artificial Analysis says the standard headline rates remain more than 60% below the competing frontier-model prices it cites for Claude Opus 5 and GPT-5.6 Sol. The practical savings will depend on how many tokens each model consumes to complete the same workload.

The Grok name carries considerable baggage and controversy, separate from the general AI skepticism

Performance and price may not be the only hurdles SpaceXAI faces in converting Grok 4.6's benchmark gains into enterprise adoption. The Grok brand arrives with an unusually visible history of safety and governance controversies — including extremist and antisemitic outputs, politically skewed responses, exaggerated praise of Elon Musk and, more recently, the use of Grok's image-generation capabilities to produce non-consensual sexualized imagery. For companies with strict compliance, brand-safety or responsible-AI requirements, that history could become a procurement consideration separate from the technical capabilities of Grok 4.6 itself.

The most notorious text-generation episode came in July 2025, when Grok produced antisemitic posts, praised Adolf Hitler and in some responses referred to itself as " to an alleged "white genocide" in South Africa into answers to unrelated questions. xAI said an unauthorized modification to Grok's response software had directed the system to produce a particular response on a political topic while bypassing its normal review process. The company said the change violated its policies and subsequently pledged to publish Grok's system prompts and establish round-the-clock monitoring for problematic responses. The South African government has rejected claims that a genocide against white South Africans is taking place.

Grok's objectivity came under scrutiny again in November of the same year after the chatbot repeatedly produced after reports that the Grok account was being used to create and distribute undressed images of people and sexualized images of children. Ofcom said the material under examination could amount to non-consensual intimate-image abuse, pornography and child sexual abuse material. X subsequently said it had implemented measures intended to stop the Grok account from being used to create intimate images of people, but Ofcom said its investigation remained open.

The scrutiny extends beyond Ofcom. , including the dissemination of manipulated sexually explicit material.

Those investigations concern X and the earlier xAI organization rather than establishing a finding that the newly released Grok 4.6 API violates those laws. Nevertheless, they are unlikely to help SpaceXAI sell Grok to businesses.

SpaceX acquired xAI in February 2026, and the AI operation now markets itself as SpaceXAI, meaning Grok's newest models sit under a different corporate structure but retain the same consumer-facing brand.

There is no evidence in the material examined here that Grok 4.6 itself repeats the specific "MechaHitler," "white genocide," sexual-image or Musk-flattery incidents associated with earlier Grok deployments. But enterprise procurement teams rarely evaluate a model in isolation from its vendor and product history. For SpaceXAI, that means Grok 4.6 may have to demonstrate not only that it is cheaper or more capable than competing frontier models, but that the controls around it are sufficiently predictable for organizations that cannot afford their AI supplier to become a brand-safety event.

That continuity creates a potential adoption problem that benchmark tables cannot measure. Developers choosing a model for an internal coding agent may care primarily about price, latency and task completion. A bank, government agency, healthcare provider or consumer brand deploying the same model into customer-facing or regulated workflows may also have to consider vendor governance, content-safety controls, auditability and reputational exposure.

A model designed to be deployed, not just chatted with

Grok 4.6 supports text and image inputs with text output, function calling, structured outputs and reasoning, according to the supplied API specifications. Those specifications also list rate limits of 150 requests per second and 50 million tokens per minute, with API availability in us-east-1 and us-west-2.

Cursor’s launch announcement similarly characterizes Grok 4.6 as designed for long-running agents and ambitious interactive and visual work, giving developers immediate access to the model inside an established coding-agent environment rather than requiring them to build a new harness around the API first.

For enterprise buyers, that distribution may matter almost as much as another leaderboard result. Models increasingly compete not just on reasoning scores but on whether developers can place them inside existing coding, research and operational workflows without destabilizing those workflows or dramatically increasing inference costs, as well as incurring any blowback from associating with a controversial brand.

Grok 4.6 does not establish an uncontested performance lead. Its launch instead presents a different proposition: frontier-level intelligence, large improvements over the previous generation, stronger long-running agent behavior and relatively aggressive token economics.

The next test will be whether the efficiency Artificial Analysis observes on controlled agentic workloads carries into production. If Grok 4.6 can consistently complete long-running coding and knowledge-work tasks with fewer turns and fewer tokens, the model’s most important benchmark may ultimately be the enterprise inference bill rather than the leaderboard.

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