GPT-5.2 Pro vs Grok 4.3

Pricing math on a real workload

Take a 10M input / 2M output token job — a realistic batch or long-running agent session. On OpenKey, GPT-5.2 Pro list price is $21/M input and $168/M output, which becomes $21.63/M and $173.04/M after the 3% fee ($21 × 1.03, $168 × 1.03). That workload costs **$546.00**. Grok 4.3 lists at $1.25/M input and $2.50/M output, or $1.2875/M and $2.575/M on OpenKey — the same workload costs **$17.50**. That's a 31x difference in total cost for the same token volume, driven mostly by a 16.8x gap on input pricing alone. Grok 4.3 also supports cache reads at $0.20/M provider-side, which GPT-5.2 Pro doesn't offer at all — so repeated-context workloads widen the gap further.

Context window and long documents

Grok 4.3 handles 1,000,000 tokens of context; GPT-5.2 Pro caps at 400,000 — a context ratio of 0.4, meaning GPT-5.2 Pro holds less than half of what Grok 4.3 can. If your workload involves ingesting large codebases, long transcripts, or multi-document RAG contexts that approach or exceed 400K tokens, GPT-5.2 Pro will truncate or fail where Grok 4.3 still has headroom. GPT-5.2 Pro does cap max output at 128,000 tokens; Grok 4.3's max completion isn't specified in its catalog record, so don't assume it's unlimited — check before you build a workflow that depends on very large single-response output.

Reasoning control and agentic performance

GPT-5.2 Pro makes reasoning mandatory — you choose between medium, high, or xhigh effort, but you can't turn it off, so every call carries reasoning-token cost. Grok 4.3 makes reasoning optional (high, medium, low, or none), with low as the default and reasoning enabled by default — you can dial it down or off for latency- or cost-sensitive calls. On Design Arena's agent benchmarks, Grok 4.3 posts elo scores from 1033–1194 across categories like webapps (1194, rank 14), mobileapps (1146, rank 26), and fullstack (1072, rank 26), with win rates in the 27–47% range. GPT-5.2 Pro has no benchmark data available in this comparison, so any coding-quality claim for it here would be a guess — go by the workload fit and pricing instead.

Parameter and modality differences

Both models accept text, image, and file input and return text only — no modality advantage either way. Grok 4.3 supports a longer parameter list: frequency_penalty, presence_penalty, logprobs, top_logprobs, temperature, top_p, and stop, in addition to the reasoning and tool-calling params both models share. GPT-5.2 Pro's supported parameters are limited to reasoning, tool use, structured outputs, and basic generation controls — no sampling-parameter fine-tuning. If your pipeline depends on temperature or penalty tuning for output diversity, Grok 4.3 gives you that control; GPT-5.2 Pro doesn't expose it.

Questions

How much cheaper is Grok 4.3 than GPT-5.2 Pro?

On a 10M input / 2M output workload, Grok 4.3 costs $17.50 on OpenKey versus $546.00 for GPT-5.2 Pro — roughly 31x cheaper. The input price alone shows a 16.8x ratio ($1.2875/M vs $21.63/M on OpenKey after the 3% fee).

Which model has a bigger context window?

Grok 4.3 supports 1,000,000 tokens of context; GPT-5.2 Pro supports 400,000. That's a context ratio of 0.4 — GPT-5.2 Pro holds less than half of what Grok 4.3 can process in a single call.

Can I turn off reasoning to save cost?

On Grok 4.3, yes — supported efforts include high, medium, low, and none, with low as default. On GPT-5.2 Pro, reasoning is mandatory; you can only choose between medium, high, or xhigh effort, so you always pay for some reasoning overhead.

Does GPT-5.2 Pro have published benchmark scores?

No benchmark data is available for GPT-5.2 Pro in this comparison. Grok 4.3 has Design Arena scores (e.g., webapps elo 1194, rank 14) and artificial_analysis metrics (intelligence index 37.6, coding index 42.2), but there's nothing to compare it against directly for GPT-5.2 Pro yet.