Based on the provided evidence, I cannot verify a public OpenAI model, price card, or performance profile called “GPT-5.5 Spud.” The only explicitly documented model family in this evidence is GPT-5.4 / GPT-5.4-mini, so any claim about GPT-5.5 Spud’s token efficiency, API pricing, or production latency is unsupported here ^[2][6]. What the evidence does support is a narrower picture of OpenAI inference economics: model selection is framed as an accuracy-latency-cost tradeoff, recent models have automatic prompt caching, Priority processing can be enabled for certain endpoints, and a Batch API exists for batch workloads ^[5][8][7][1]. Key findings - Fact-check result: Insufficient evidence for “GPT-5.5 Spud.” The supplied model documentation names GPT-5.4 and describes it as a frontier model for complex professional work, and the supplied pricing evidence lists GPT-5.4 and GPT-5.4-mini, not GPT-5.5 Spud ^[2][6]. - OpenAI’s own model-selection guidance says choosing a model requires balancing accuracy, latency, and cost, and recommends maintaining accuracy with the cheapest, fastest model possible after establishing the required quality bar ^[5]. That means the right production model is not necessarily the most capable one; it is the least expensive and lowest-latency model that still clears your acceptance criteria ^[5]. - The pricing snippet shows materially lower listed rates for GPT-5.4-mini than GPT-5.4 in the visible columns: GPT-5.4 appears with $2.50 / $0.25 / $15.00 in one visible set of values, while GPT-5.4-mini appears with $0.75 / $0.075 / $4.50 in the corresponding visible set ^[6]. The same snippet also shows another GPT-5.4 entry with $1.25 / $0.13 / $7.50 and additional values of $2.50 / $0.25 / $11.25 ^[6]. Because the snippet does not include the table headers, those figures should be reported only as listed prices, not mapped with certainty to specific token classes or endpoints from this evidence alone ^[6]. - Prompt caching is one of the clearest documented levers for token efficiency in the supplied evidence: it works automatically, requires no code changes, has no additional fees, and is enabled for recent models from gpt-4o onward ^[8]. Inference: for workloads with repeated prompt prefixes or shared system prompts, caching can improve effective input-token economics, and the lower secondary price points visible in the pricing snippet are consistent with that interpretation ^[6][8]. - Priority processing is an explicit latency-oriented control in the API: requests to the Responses or Completions endpoints can opt in with service_tier=priority, or the setting can be enabled at the Project level ^[7]. However, the supplied evidence does not quantify the latency gain, throughput effect, or price premium, so stronger claims about production performance are not supported ^[7]. - A Batch API is available, which confirms OpenAI supports a distinct batch-processing path ^[1]. But the supplied snippet does not state batch discounts, turnaround times, or throughput guarantees, so I cannot verify economic or latency advantages for batch workloads from this evidence alone ^[1]. - The strongest model-specific statement supported here is about GPT-5.4, not GPT-5.5: GPT-5.4 is positioned for complex professional work ^[2]. Combined with the lower listed prices for GPT-5.4-mini and the model-selection guidance, the supported inference is that GPT-5.4 is the capability-first option in this evidence set, while GPT-5.4-mini is the more cost-sensitive option; the exact accuracy and latency gap is not quantified here ^[2][5][6]. Evidence notes - Directly supported facts: GPT-5.4 exists and is described as a frontier model for complex professional work ^[2]; pricing entries for GPT-5.4 and GPT-5.4-mini appear on the pricing page ^[6]; prompt caching is automatic and free on recent models ^[8]; Priority processing can be enabled on Responses and Completions ^[7]; a Batch API exists ^[1]; model choice is explicitly framed as an accuracy-latency-cost tradeoff ^[5]. - Inference, not directly measured in the supplied evidence: repeated shared prompts likely improve effective token economics under caching ^[6][8]; GPT-5.4-mini is the stronger candidate for high-volume cost control, while GPT-5.4 is the stronger candidate for maximum capability ^[2][5][6]. Limitations / uncertainty - Insufficient evidence to verify that “GPT-5.5 Spud” is a public OpenAI model at all, or to state its pricing, token efficiency, latency, throughput, or benchmark performance ^[2][6]. - The supplied pricing snippet lacks column headers, so any precise mapping of the visible numbers to input, cached input, output, audio, or other billing categories would overclaim the evidence ^[6]. - The supplied evidence does not include benchmark scores, tokens-per-second measurements, end-to-end latency percentiles, RPM/TPM limits, or production throughput data, so any numerical performance comparison would be speculative ^[5][6][7]. Summary The defensible conclusion is narrow: this evidence does not substantiate “GPT-5.5 Spud,” so claims about its economics or production behavior should be treated as unverified ^[2][6]. What is supported is a general OpenAI inference-economics framework built around accuracy-latency-cost tradeoffs ^[5], automatic prompt caching on recent models ^[8], opt-in Priority processing ^[7], a Batch API for batch workloads ^[1], and visibly lower listed price points for GPT-5.4-mini than GPT-5.4 in the supplied pricing snippet ^[6]. For any stronger claim about GPT-5.5 Spud specifically, the correct conclusion is: Insufficient evidence ^[2][6].