Grok 4.5 vs GPT-5.5: which finishes the task cheaper (vendor, independent & first-party data)

Almost every Grok 4.5 vs GPT-5.5 write-up on the web is copying the same table: Grok 4.5 resolves 64.7 percent of SWE-Bench Pro, GPT-5.5 resolves 58.6 percent, so Grok wins. That table is real, but it is xAI's own harness, not a neutral leaderboard — and resolve rate is not what you pay for. This comparison does three things the AI Overview does not: it separates the vendor numbers from the genuinely independent ones from our own executed run, it prices the models by what a finished task actually costs, and it gives you a decision rule for which one to route each job to. As of July 2026.

The internet has already decided this matchup: Grok 4.5 resolves more of SWE-Bench Pro than GPT-5.5, therefore Grok wins coding. That conclusion is built on a table that xAI published about its own model. It is not wrong, but it is not neutral either, and resolve rate is not the number that shows up on your invoice. This comparison keeps three kinds of evidence strictly separate — what the vendor reported, what an independent evaluator measured, and what we executed ourselves — and then reframes the whole question around the one axis that actually decides your bill.

The quick answer

If you want the short version: GPT-5.5 is the marginally stronger raw model, Grok 4.5 is dramatically cheaper to finish a task with, and for most agentic and coding workloads the cost gap is the deciding factor. On the independent Artificial Analysis Intelligence Index, GPT-5.5 ranks above Grok 4.5, which sits 4th overall at a score of 54. On the independent Coding Agent Index the two are roughly on par at 76 — but Grok gets there at about 2.49 dollars per task versus 5.07 dollars for GPT-5.5 in Codex. Our own executed run adds a blunt data point: GPT-5.5 was the priciest of the 13 models we ran, at 8.83 dollars per 1,000 tasks. Grok 4.5 was not in that run, so we never quote a first-party score for it.

Three layers of evidence

Before any numbers, here is the distinction almost every competing article blurs. There are three completely different sources feeding this comparison, and they do not carry equal weight:

1. Vendor-reported (xAI). The entire SWE-Bench Pro block — Grok 4.5 at 64.7 percent, GPT-5.5 at 58.6, Opus 4.8 at 69.2, Fable 5 at 80.4 — comes from xAI's own benchmark table on the Grok 4.5 launch page. A vendor benchmarking its own release against competitors is useful signal, but it is marketing-adjacent by construction. Read it directionally, not as a referee's scorecard.

2. Independent third-party (Artificial Analysis). The Intelligence Index (Grok 4.5 = 54, 4th) and the Coding Agent Index cost-per-task figures are run by a neutral evaluator against every model on the same rubric. This is the layer to trust for cross-vendor claims.

3. First-party executed (DataLLM Lab). Our LLM coding cost benchmark actually runs 13 models through 9 generate-then-run-hidden-tests tasks and prices real token usage. GPT-5.5 is in that run; Grok 4.5 is not (only its predecessor, Grok 4.3, was). We label every number below by which layer it came from, and we never manufacture a first-party Grok 4.5 score.

Specs and pricing side by side

Grok 4.5 shipped July 8 2026 from xAI — some coverage and Artificial Analysis brand the maker SpaceXAI after the SpaceX–xAI tie-up, but it is the same company, still primarily on the x.ai domain. It is a reasoning model with a 500k-token context, a Feb 1 2026 knowledge cutoff, and a reasoning_effort control (low / medium / high, high by default). GPT-5.5 is OpenAI's current frontier model and the higher scorer on the independent Intelligence Index. One pricing nuance to keep in mind for large-context agentic runs: Grok 4.5's headline 2 dollars / 6 dollars doubles above 200k of its 500k window, and cached input drops to 0.50 dollars.

Dashes mark values we could not source cleanly — GPT-5.5's exact published list price was not verified in our research, so we do not print a per-1M dollar figure for it. What we can say with sources is that its cost per task is roughly double Grok's on the independent index, and highest of all in our executed run.

The benchmarks, labeled honestly

Start with the number everyone quotes, and label it correctly. On SWE-Bench Pro (vendor-reported by xAI), Grok 4.5 resolves 64.7 percent to GPT-5.5's 58.6 percent at xhigh — a genuine Grok lead, but sitting behind Opus 4.8 at 69.2 percent and Fable 5 at 80.4 percent on the same vendor table. So even on xAI's home turf, Grok 4.5 is a strong mid-frontier coder, not the capability leader.

Move to the independent layer and the coding story converges. On the Artificial Analysis Coding Agent Index, Grok 4.5 and GPT-5.5 both land at a score of 76 — effectively a tie on agentic coding capability. That is the honest capability verdict: on the metric run by a neutral evaluator, neither model clearly out-codes the other. Where they diverge is not quality; it is what that score costs to obtain. For the broader field of coders, our best coding LLM guide puts both in context, and the full head-to-head detail lives in the Grok 4.5 review and GPT-5.5 review.

Cost per completed task

This is the information the leaderboard hides. Two models can tie on a capability index and still bill you wildly differently, because the invoice is per-token price multiplied by tokens burned. Grok 4.5's defining trait is token efficiency. Independently, Artificial Analysis measured it at roughly 14k output tokens per Intelligence Index task — more than 60 percent below Opus 4.8 — and about 1.9M total tokens per Coding Agent Index task, against 6.2M for GPT-5.5 in Codex and 7.2M for Fable 5 in Claude Code. xAI's own figures tell the same story (15,954 output tokens per resolved SWE-Bench Pro task versus 67,020 for Opus 4.8), but the independent number is the one to lean on.

Fewer tokens at a lower price compounds. The result, on the independent Coding Agent Index, is a per-task cost of about 2.49 dollars for Grok 4.5 against 5.07 dollars for GPT-5.5 — the same capability score for roughly half the money. Note the harnesses differ per model (Grok Build vs Codex vs Claude Code), so these are eval costs in each model's native agent, not a single identical rig.

Our first-party run corroborates the direction from the other end. In the DataLLM Lab executed benchmark, 10 of 13 models scored a perfect 9/9, so raw correctness is table stakes — and GPT-5.5 scored 9/9 but at 8.83 dollars per 1,000 tasks, the most expensive model in the run (an 88x spread separated it from the cheapest). Grok 4.5 was not in that run; only Grok 4.3 was, at 8/9 and 1.75 dollars per 1k. So stacking the two independent-plus-first-party signals: the independent index already shows Grok at half GPT-5.5's per-task cost, and our executed run confirms GPT-5.5 sits at the pricey end of the market. The cost gap per finished task is wider than any raw-benchmark gap between them.

Which one to route each job to

The decision rule falls out of the layers above. Route to Grok 4.5 when the workload is agentic and token-heavy — long coding sessions, multi-step tool-calling, automation pipelines — because its capability is on par on the independent coding index while its cost per finished task is roughly half. Its token efficiency is the mechanism, and it compounds the most exactly where GPT-5.5's spend runs up on long runs. Route to GPT-5.5 when you need the extra edge on the hardest one-shot reasoning, where its higher Intelligence Index rank buys real headroom and the per-task premium is worth it on a small number of critical calls. And keep Opus 4.8 in mind as the raw-quality option above both on the vendor SWE-Bench table. If you are weighing the Anthropic and OpenAI stacks more broadly, Claude vs ChatGPT covers that ground, and you can inspect the GPT-5.5 model page for full specs.

The one-line synthesis: this is not a capability race Grok wins on a vendor table — on the neutral coding index it is a tie. It is a cost race, and there Grok 4.5 wins decisively. Pick GPT-5.5 for the marginal quality ceiling; pick Grok 4.5 for the cheapest path to a completed task, which for most production agents is the number that matters.

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