Grok 4.5 vs GPT-6 Astra: Six Times the Sticker Price, Three Times the Bill

Set Grok 4.5 and GPT-6 Astra side by side on their rate cards and the gap looks absurd: $2.00 against $10.00 per million input tokens, $6.00 against $50.00 per million on output. Run the same two through Artificial Analysis's Intelligence Index and the gap narrows to something a team can actually argue about — $1.04 per completed task against $3.26. The sticker multiple is 5x on input and 8.3x on output. The bill multiple, measured on real token counts, is a little over 3x. And the dimension where these two models differ most is neither of those. It is how long you wait for the first token, where the independent measurement is 10.94 seconds against 342.30.

That is the whole matchup in one paragraph, and it is why this page is not a coronation in either direction. GPT-6 Astra is the more intelligent model by a clear and reproducible margin. Grok 4.5 is the cheaper one by a margin that is real but considerably smaller than its price list suggests. Everything else — speed, token efficiency, context, the coding benchmarks — either splits, ties, or turns out to be measured on two different rulers.

Neither of these is a new model

Worth stating plainly, because a lot of comparison pages read as though both arrived last week.

xAI shipped Grok 4.5 on July 8, 2026. It is built on the 1.5-trillion-parameter V9 foundation and co-trained with Cursor on developer-session data rather than static code alone, which is where its agentic-coding reputation comes from. It carries a 500,000-token context window. Its own vendor model list still carries it today, alongside two successors — xAI has since shipped Grok 4.6 and Grok 4.7 — so treat Grok 4.5 as the $2/$6 tier of the Grok line rather than the top of it.

OpenAI announced GPT-6 Astra on September 3, 2026, with a staged rollout over the following days, and it remains OpenAI's flagship: a 1M-token context window (OrcaRouter's catalogue lists 1,050,000 input and 128,000 maximum output), a knowledge cutoff of April 30, 2026, and positioning squarely on long-horizon agentic work — computer use, research, scientific and cybersecurity tasks. On OpenAI's own account it is the first model to clear its "Critical" cybersecurity threshold, which is also why enterprise access is off by default until an administrator enables it.

Both are generally available on their vendors' APIs. Neither is launching. The only thing that moved this week is the family around one of them, and that comes at the end of this piece because it changes the recommendation rather than the comparison.

The rate cards, including the tier nobody quotes

• Input, short context — Grok 4.5 $2.00 per 1M vs GPT-6 Astra $10.00 per 1M

• Output, short context — Grok 4.5 $6.00 per 1M vs GPT-6 Astra $50.00 per 1M

• Cached input — Grok 4.5 $0.30 per 1M vs GPT-6 Astra $1.00 per 1M

• Long-context input — Grok 4.5 $4.00 per 1M vs GPT-6 Astra $20.00 per 1M

• Long-context output — Grok 4.5 $12.00 per 1M vs GPT-6 Astra $75.00 per 1M

• Context window — Grok 4.5 500,000 tokens vs GPT-6 Astra 1,000,000 tokens

The clause that matters is the one almost no comparison page prints. On Grok 4.5, once a request's prompt reaches 200,000 tokens, the whole request is repriced at the higher tier — xAI's documentation is explicit that it is "billed at the higher rate for all tokens in the request," not just the tokens past the line. So the 500,000-token window is real, but roughly the top 60% of it bills at double. OpenAI's pricing page for Astra prints the same short/long two-column structure without stating where its crossover sits, so treat the long-context column as the one that applies once you are working at scale and confirm the boundary against your own invoice.

Two service-level multipliers sit on top of Astra's numbers: Batch and Flex run at half the standard rate, and Fast mode runs at double — $20.00 input and $100.00 output at short context. Grok 4.5 has no batch tier on xAI's sheet; its levers are the cache discount and priority processing at 2x.

Our own position on all of this is deliberately boring: OrcaRouter passes provider list price through at 0% markup, so both rate cards above are live on our side the same day either vendor changes them, and cached tokens bill at the provider's cache rate rather than full price. There is no second number to reconcile.

What a workload actually costs

Sticker prices are a poor guide here, because the two models do not spend the same number of tokens to finish the same job. Take a coding-agent task with a 120,000-token repository context and a 25,000-token answer. On Grok 4.5 that is $0.24 of input and $0.15 of output, so $0.39. On GPT-6 Astra it is $1.20 and $1.25, so $2.45. A 6.3x gap.

Now push the prompt past the long-context line: 300,000 tokens in, 20,000 out. Grok 4.5 bills $1.20 plus $0.24, or $1.44. GPT-6 Astra bills $6.00 plus $1.50, or $7.50. The gap compresses to 5.2x, because both vendors double the input rate and Astra's output multiple narrows at the top tier.

Neither of those is what Artificial Analysis measures, and their number is the more useful one. Running the full Intelligence Index, the average cost per completed task is $1.04 for Grok 4.5 at high effort and $3.26 for GPT-6 Astra at max effort. The reason the multiple falls to 3.1x when the input price is 5x apart is token efficiency: across the index, Grok 4.5 generated 77M output tokens and Astra generated 60M. Astra is the more concise model, so it closes part of a gap it opened on price. If you have been quoting "five times cheaper" in a budget document, 3x is the number that will show up on the invoice.

Speed is a tie. Latency is not.

This is the finding that surprised us, and it runs against the intuition that the cheap model is the quick one.

Artificial Analysis measures Grok 4.5 at 55 output tokens per second and GPT-6 Astra at 58. That is a 5% difference on the same index revision, and AA's own summary describes both as slower than average — the comparison median is 74 tokens per second. If throughput is your selection criterion, these two models do not separate. Say it plainly rather than manufacturing a winner: on the one speed number measured the same way for both, they are level.

Latency separates them violently. AA puts Grok 4.5's time to first answer token at 10.94 seconds with 20.01 seconds end-to-end; GPT-6 Astra at 342.30 seconds with 350.91 seconds end-to-end. That is roughly 31x, and on a synchronous request it is the difference between a spinner and a coffee break.

Two honest caveats before anyone budgets on that. First, those are reasoning-inclusive figures — AA's "time to first answer token" deliberately counts the model's thinking time — so they describe a hard task, not a chat turn. Second, they are not matched-effort: Astra's published entry is at max effort, Grok 4.5's at high, and an effort setting is exactly the dial that moves this number. OrcaRouter's own listing for GPT-6 Astra shows a p50 time to first token of 8.31 seconds and a p95 of 10.00 seconds across live traffic, which is a completely different measurement point — first token on real production calls, not first answer token on a benchmark task. The two are not comparable and should not be put in the same sentence as a comparison.

Coding benchmarks: check the version before you cite it

Search for this matchup and you will find Grok 4.5's 83.3% on Terminal-Bench 2.1 set against GPT-6 Astra's 57.9% on Terminal-Bench 4.0. Those are different benchmarks wearing the same name. They cannot be compared, and the comparison pages that stack them are telling you nothing.

Most of both vendors' published coding numbers have this problem. xAI's sheet for Grok 4.5 reports SWE-bench Pro 64.7%, Terminal-Bench 2.1 83.3%, DeepSWE 1.0 62.0% and DeepSWE 1.1 53%. OpenAI's sheet for Astra reports Terminal-Bench 4.0 57.9%, OSWorld 2.0 72.6%, FrontierMath Tier 4 97.6% and ARC-AGI-3 99.9%. All vendor-reported, and almost none of them overlapping.

There is one place the two genuinely meet on the same harness: DeepSWE v1.1 from Datacurve, which runs every model through the same mini-swe-agent scaffold on 113 original, contamination-free tasks. There, GPT-6 Astra scores 74.1% at roughly $6.52 per task, while Grok 4.5 lands at 53%. That is the cleanest single result on this page, and it favours Astra decisively. One further caveat specific to Grok 4.5: xAI's CursorBench figure was excluded from public comparison after a prior codebase was found to have leaked into training, so treat any CursorBench number for this model as unusable.

Agentic behaviour and tool calling

The two take different routes to the same destination, and the difference is architectural rather than a score.

GPT-6 Astra's developer-facing features assume you are building an orchestrator. It supports asynchronous tool calling, so waiting on a tool does not block the model from continuing other work; mid-task steering over WebSockets, so an in-flight run can be redirected without restarting it; and reasoning effort adjustable mid-conversation. In Codex it adds a context-preservation mechanism that keeps notes across context windows while earlier context stays searchable. OpenAI's own system card reportedly notes the model is harder to monitor than its predecessor, which is a reason to treat tool-call logs and system state — not the model's narration — as your audit evidence.

Grok 4.5's agentic story is less about new primitives and more about where it was trained. Co-training with Cursor on real multi-file editing and debugging sessions is the thing xAI credits for its token efficiency on software tasks, and it is why the model shows up as a default in coding surfaces rather than as a general-purpose flagship. Function calling, structured output, streaming and prompt caching are all supported; tool calling follows the OpenAI-compatible shape, which is why dropping it into an existing client is a base-URL change.

There is no shared independent agentic benchmark where both appear at matched effort, so we are not going to invent a winner here. What can be said is that Astra's tool-calling surface is the more expressive of the two for long-horizon work, and Grok 4.5's per-task token economy is the more attractive for high-volume work.

Pick Grok 4.5 if

• You are running high-volume or high-frequency work where the per-task bill dominates the decision, and a 39 on the AA Intelligence Index clears your quality bar.

• Your prompts sit under 200,000 tokens. That is where Grok 4.5's price advantage is at its widest and the long-context repricing never triggers.

• You want a cache discount doing real work. At $0.30 per million cached input tokens against Astra's $1.00, repeated-prefix workloads — long system prompts, shared repo context — get cheap fast.

• You need sub-minute first-token latency on hard tasks and cannot absorb a multi-minute wait.

Pick GPT-6 Astra if

• The task is the product and the bill is a rounding error next to a wrong answer. Fourteen index points at this end of the curve is a real capability difference, not a marketing one.

• You are working genuinely past 500,000 tokens. Astra's window is roughly twice Grok 4.5's, and it is the only one of the two that reaches it without a repricing clause.

• You need computer use, deep research, or the cybersecurity posture — including the enterprise-off-by-default controls that come with OpenAI's Critical threshold.

• You are writing orchestrator code that wants asynchronous tool calls and mid-run steering rather than a straight request-response call.

What moved this week, and why it changes the recommendation

On September 22, 2026, OpenAI shipped GPT-6 Sol and GPT-6 Luna at $2.00/$10.00 and $0.10/$0.50 per million tokens, describing the rates as permanent rather than promotional. Sol is the mid-tier model for coding and analysis at roughly a fifth of Astra's input price.

That matters for this exact decision. If the reason you were weighing Grok 4.5 against GPT-6 Astra was price, the honest comparison on OpenAI's side is no longer Astra — Astra is the flagship, and Sol now occupies the tier Grok 4.5 actually competes in. Grok 4.5 still undercuts Sol on output ($6.00 against $10.00) and matches it on input ($2.00 each), so it is not displaced; but a comparison written before this week was comparing a value model against a flagship and calling the result a price story.

The same is true on xAI's side: xAI's own model list carries grok-4.6 and grok-4.7 alongside grok-4.5, so Grok 4.5 is one option in a family rather than the current frontier.

Which is the actual argument for routing rather than picking. The two-model stack that keeps showing up in practitioner write-ups — send the bulk to the cheap model, escalate the hard tail to the expensive one — is a routing decision, and it is one you can express as configuration instead of a rewrite. OrcaRouter puts both models behind one API and one key, with provider list price passed through at 0% markup, automatic failover across providers, and a routing DSL that lets you send below-a-threshold work to grok/grok-4.5 and escalate to openai/gpt-6-astra when a task fails or crosses a size line. You can try the expensive path on a narrow slice of traffic without betting a production pipeline on it.

The short version

GPT-6 Astra is the better model. That is not close, and this page would be worthless if it pretended otherwise — 53 against 39 on the same index revision, 74.1% against 53% on the one coding benchmark both have taken.

Grok 4.5 is the cheaper model, but by 3.1x per completed task rather than the 5x-to-8.3x its price list implies, because Astra spends fewer tokens getting there. And on the one speed number measured identically for both, they are level.

The decision is not which model wins. It is whether a 14-point index gap is worth roughly triple the bill on your specific workload — and whether the answer is the same for every request you send.