GPT-6.1 Sol vs Grok 4.8: One of These Models Exists, and It Isn't the One in the Title

There is no Grok 4.8 to compare against GPT-6.1 Sol. As of 5 November 2026, SpaceXAI — the company formerly known as xAI — has published no model identifier, no rate card, no context window and no benchmark card for Grok 4.8, and its developer documentation lists nothing under that name. What exists is a set of statements from Elon Musk on X, a slipped training deadline, and a single-source rumour about model-picker strings. GPT-6.1 Sol, by contrast, has been generally available since 29 September 2026 and has been measured on the same independent board as everything else. So the useful version of this comparison is not Sol versus a model that does not exist; it is Sol versus what you would run today instead, plus an honest estimate of what 4.8 would have to do to change the answer.

What is actually confirmed about Grok 4.8

The record is short enough to state in full, and every item traces to Musk rather than to SpaceXAI's documentation.

• 13–14 September 2026 — Musk said Grok 4.8 was roughly 2.5 trillion parameters, trained on a from-scratch C++ software stack, and would finish pre-training "this week" before reinforcement learning.

• Around 20 September — that milestone passed with no confirmation.

• 21 September — SpaceXAI shipped Grok 4.7 instead.

• 30 September — a single unnamed tracker reported grok-4.8-high, grok-4.8-high-fast and grok-4.8-xhigh-fast strings appearing in SpaceXAI's model picker, with 500K and 256K router tests. No commit hash, no file path, no second source. That is internal wiring, not a launch.

• 30 October — Musk said development had taken longer than expected, that Grok 4.8 "will not be a disappointment", and that the team is not optimising for benchmarks.

That last sentence is the important one, and it cuts both ways. A lab that does not optimise for benchmarks can still ship a better product — benchmark scores are a proxy, and Grok 4.7 is a genuinely good coding model that scores badly on the index. But it also means that when Grok 4.8 arrives, the first thing you will not have is a number that lets you compare it to anything.

The stand-in, and the size of the gap it has to close

Grok 4.7 is the model you can call today. It shipped on 21 September 2026 at $2.00 per million input tokens and $6.00 per million output, with cached input at $0.50 and every rate doubling above 200,000 input tokens. Here is how it stands against GPT-6.1 Sol on Artificial Analysis Intelligence Index v5.0.1, both at maximum published effort.

• Intelligence Index v5.0.1 — GPT-6.1 Sol 55 vs Grok 4.7 35, a 20-point gap

• Cost per completed index task — Sol $1.27 vs Grok $7.77

• Output tokens on the index run — Sol 63.7 million vs Grok 148 million

• Output speed — Sol 56 tokens/sec vs Grok 72 tokens/sec

• Context window — Sol 1,050,000 vs Grok 500,000

• Maximum output in one response — Sol 128,000 vs Grok 450,000 claimed

• Input / output price per 1M — Sol $2.00 / $10.00 vs Grok $2.00 / $6.00

• Cached input — Sol $0.10 vs Grok $0.50

• Long-context step — Sol reprices the whole request above 272,000 input tokens at 2× input and 1.5× output; Grok doubles every rate above 200,000

• Weights — both closed

The pricing inversion is the trap this pair sets. Grok 4.7's output rate is 40% cheaper and its cache line is five times more expensive, so a rate-card comparison can be made to point either way depending on which row you lead with. The measurement resolves it: Grok 4.7 generated 2.3 times Sol's output tokens to complete the same index and cost 6.1 times as much per task. A cheaper token that you buy more of is not a cheaper run.

The one place Grok 4.7 genuinely wins

Speed, and the output ceiling. At 72 tokens per second against Sol's 56, Grok 4.7 is about 29% faster in steady-state generation, and its 450,000-token maximum response is three and a half times Sol's 128,000. If your workload is "take a 400,000-token transcript and produce a long structured artefact in one call", Grok 4.7 can do it and Sol will truncate. That is a real capability difference, not a benchmark artefact, and it is the one line in the list above where the model 20 points behind is the only one that can complete the job.

For everything else — tool calling, document QA, multi-step support flows — the 20-point gap and the 6.1× cost gap describe a model that is not in the same tier. Grok 4.7 is a fast, cheap-per-token model whose reasoning burns an unusual number of tokens. That is a defensible product for latency-sensitive work with short outputs, and a poor one for agentic loops that reason at length.

What Grok 4.8 would have to do

Start with the arithmetic, because it is unforgiving. To match GPT-6.1 Sol on the current index, Grok 4.8 needs 20 points over Grok 4.7 — a larger jump than any single SpaceXAI generation has produced on this board. To match Sol's cost per task while doing so, it needs to reason in roughly 40% of Grok 4.7's tokens, because a 20-point capability gain bought with more thinking is a bill increase, not a saving. The 2.5-trillion-parameter claim is not evidence for either. Parameter count predicts training cost, not token efficiency.

Then subtract the things Musk has ruled out. "Not optimising for benchmarks" means the numbers, when they land, may be published late, partially, or not at all, and that the first credible comparison may come from a third party weeks after launch. It also means the model may be tuned for something the index does not measure — which is a legitimate strategy and a difficult one to plan a migration around.

The realistic scenario is that Grok 4.8 lands somewhere in the 40s on this index, faster than Sol, priced aggressively, and better than its score suggests on coding. That would make it a serious alternative for interactive work and still leave Sol ahead on cost per completed task. The unrealistic scenario — 4.8 clears 55 and undercuts $1.27 — is not impossible, but nothing published so far supports planning for it.

The cost of waiting, measured in the thing you would actually do

Suppose you have an agent that handles 50,000 requests a month at 15,000 input tokens and 3,000 output tokens each. On GPT-6.1 Sol's list price that is $0.03 of input and $0.03 of output per request, or about $3,000 a month. On Grok 4.7 at $2.00 and $6.00 it is $0.03 and $0.018, or about $2,400. The cheaper rate card saves you $600 a month — and that is before the token counts diverge, which on the index they do by a factor of 2.3.

Now price the wait. If Grok 4.8 arrives in December and you deferred the project for it, you have paid nothing and shipped nothing. If it arrives in March, you have lost a quarter. The expected value of waiting on an unannounced model with a slipped deadline and no published date is negative for any team with a roadmap, which is why the correct posture is to build against what exists and keep the switch cheap.

That is the practical argument for a routing layer rather than a hard-coded model. GPT-6.1 Sol and Grok 4.7 both sit behind one key here at provider list price with no markup, so trying Grok 4.7 on your own traffic is a config change and a comparison, not a procurement. If Grok 4.8 lands and turns out to be everything Musk says, moving a percentage of traffic to it is a routing rule; automatic failover means an unproven model can sit behind a proven one without becoming a single point of failure on day one. The expensive mistake in this pair is not picking the wrong model — it is building something that makes picking again expensive.

So: no, there is nothing to compare yet. Grok 4.8 is a date that keeps moving, attached to a parameter count and a promise not to chase benchmarks. GPT-6.1 Sol is 55 on the index at $1.27 a task and has been callable for five weeks. The decision in front of you is whether Grok 4.7's speed and 450K output ceiling are worth a 20-point capability gap, and that one you can answer this afternoon.