GPT-6 vs Grok 4.7: The Output Column Decides It

GPT-6 Sol and Grok 4.7 charge the same thing for input tokens — $2.00 per million, both — which makes the input column useless for choosing between them. The decision lives one column to the right. GPT-6 Sol bills $10.00 per million output tokens; Grok 4.7 bills $6.00. And the two models disagree about how much output you are allowed to produce in a single call by a factor of three and a half: GPT-6 Sol caps at 128,000 tokens, while Grok 4.7 — SpaceXAI's flagship, released 21 September 2026 as the successor to Grok 4.6 — goes to 450,000.

Everything else in this matchup follows from those two facts, plus one more: GPT-6 Sol carries the larger context window, 1,050,000 tokens against Grok 4.7's 500,000. So this is not a story about one model being better. It is a story about two models with different shapes, and about which shape your workload has.

Get the shape of your request right first

The useful way to sort a GPT-6 versus Grok 4.7 decision is by which resource your job actually consumes. Three cases cover most production traffic.

Long input, short output. You are feeding a large document, a codebase, or a long agent transcript and getting back a summary, a diff, or a decision. Here the context window is the binding constraint, and GPT-6 Sol's 1,050,000 tokens is roughly double Grok 4.7's 500,000. But note the tier line on both cards: Grok 4.7's $2.00 rate applies up to 200,000 input tokens and steps to $4.00 beyond that, while GPT-6 Sol's $2.00 rate runs to 272,000 and steps to $4.00 after. At 200,001 tokens Grok has already repriced and GPT-6 Sol has not, so a 250,000-token document costs $4.00 per million on Grok 4.7 and $2.00 per million on GPT-6 Sol — double, before you count the output.

Short input, long output. You are generating: a long-form document, a large code file, a structured artifact, or a chain of tool calls whose results the model must write out. This is the case Grok 4.7 is built for. A 450,000-token output ceiling is more than an order of magnitude past what most models allow, and at $6.00 per million output versus $10.00 you are paying 40% less for each of those tokens. If your generation routinely runs past 128,000 output tokens, GPT-6 Sol cannot serve the request at all in one call, and Grok 4.7 can.

Balanced and heavy on both. Long input and long output together is the case where the two cards interact. A 400,000-token input with a 200,000-token output is priced as $4.00 in and $12.00 out on Grok 4.7 (both above its threshold) and $4.00 in and $15.00 out on GPT-6 Sol. That is the one configuration where GPT-6 Sol is the more expensive model per token, and it is worth checking your own averages against it before assuming the ChatGPT-era default is cheaper.

What OpenAI changed, and why it matters here

GPT-6 is a three-model family, and on 7 October 2026 OpenAI put the middle one in front of the public. GPT-6 in ChatGPT — the Intelligent UI rollout — is powered by GPT-6 Sol for Plus, Pro, Business and Enterprise, and by GPT-6 Luna for Free and Go, reaching the more than 1.2 billion people who use ChatGPT weekly. That is a distribution fact rather than a capability fact, and it changes what "GPT-6" means in a comparison: when someone says GPT-6 beat or lost to another model on some benchmark, they usually mean GPT-6 Sol specifically, or the flagship GPT-6 Astra at $10.00 / $50.00.

For this matchup, the ChatGPT rollout matters in one concrete way. Grok 4.7 was released on 21 September 2026, four days before GPT-6 Sol, and it is a pure API and app model with no equivalent billion-user consumer default. SpaceXAI's own description of it is narrow and specific: a flagship for long-running software engineering, agentic workflows with tool use and self-verification, and knowledge work. That is a statement about output-heavy work, which is exactly the shape where Grok's card is stronger.

The scoreboard, honestly labelled

Independent evaluation of these two comes from Artificial Analysis, and the summary is that they are close on the composite index and diverge on the individual tests in a way that matches the products.

• AA Intelligence Index: Grok 4.7 46.4 (rank 16 of the models it evaluates), GPT-6 Sol 47.6 — GPT-6 Sol ahead by about a point
• Humanity's Last Exam: Grok 4.7 43.1%, GPT-6 Sol 47.9%
• SciCode: Grok 4.7 57.4%, GPT-6 Sol 57.6% — effectively level
• Long-Context Recall: Grok 4.7 76.7%, GPT-6 Sol 83.7% — GPT-6 Sol ahead, consistent with the larger window
• Terminal-Bench (v4.0 on Grok's card): Grok 4.7 25.8%, GPT-6 Sol 43.9% on the same harness family
• Coding: Grok 4.7 lands in the top decile of the coding index, in the company of the strongest models on the board

Two caveats, and they are not decorative. The index values for the two models were evaluated on different dates, so this is not a same-day head-to-head and a point of difference is within the movement a revision produces. And every Grok 4.7 figure above is the provider's own published number as carried in the catalogue, not a score we re-ran — the honest reading is that Grok 4.7 is a top-decile coding model that sits a point or so behind GPT-6 Sol on a general reasoning composite, and that the terminal-bench gap is the largest single signal in the set.

Latency and reliability, which the spec sheet hides

Published rate cards and benchmark tables both omit the thing that decides production quality of service, so it is worth looking at the measured numbers rather than the marketing ones.

On OrcaRouter's own playground measurements over the first week of October 2026, Grok 4.7 returned a median first-token latency of 3,825 ms and produced output at roughly 147 tokens per second, with an error rate around 8%. GPT-6 Sol's measured median latency in the same window was higher — 6,363 ms — with a much higher error rate. These are playground observations on a shared route, not a vendor SLA, and a 39% error rate on one model's route is a routing problem rather than a model problem; it is exactly the kind of gap that failover exists to cover. The point for a comparison is that a Grok 4.7 route looked materially more responsive and more reliable than a GPT-6 Sol route in that particular window, and that neither vendor's published card would have told you so.

Running both without committing to either

The temptation in a matchup this close is to pick one in advance on price and then discover three months later that your traffic shape changed. That is the problem routing solves. On OrcaRouter both grok/grok-4.7 and GPT-6 Sol are live routes in the same catalogue behind one API, at the provider's list price with 0% markup — so when SpaceXAI or OpenAI moves a rate, the change is live the same day rather than at your next invoice reconciliation. Automatic failover across providers covers the case where one route degrades, which is directly relevant given the error-rate spread above. And the routing DSL lets you split traffic by request shape rather than by model preference: send long-input, short-output work to the model with the bigger window, send long-output generation to the one with the 450K ceiling and the cheaper output rate, and let a request-size predicate do the choosing instead of a human.

Choosing

If your work is long-document analysis, large-context reasoning, or anything that lives above 200,000 input tokens, GPT-6 Sol is the better card: double the context, a higher independent index, and a threshold that sits 72,000 tokens further out. If your work is generation — long code artifacts, long-form writing, long tool-calling chains where the model has to write out what it found — Grok 4.7 is the better card: a 450,000-token output ceiling GPT-6 Sol cannot reach, 40% cheaper output tokens, and a top-decile coding profile to match. If you genuinely do both, the answer is not a model. It is a route.