Research and fact-check: GPT-5.5 Spud, Inference economics: token efficiency, API pricing, and production latency/throughput tradeoffs.
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,...
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 h
Key takeaways
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 late
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
Research answer
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 i.j4i.i2
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].
Supporting visuals
OpenAI API Pricing October 2025: Complete Guide to GPT-5An infographic titled "OpenAI API Pricing October 2025" with a robot mascot pointing to different API cost categories, including GPT-5 access per 1K tokens, real-time API usage per second, and image generation API per image output, alongside a cost growth chart.GPT-5 API: $1.25 Pricing, 90% Cache Discount & 272KThe image features a bar chart that displays GPT-5 API performance metrics, highlighting rapid response times for tasks like text, chat, embeddings, images, fine-tuning, and streaming, with throughput rates, as well as additional information about latency, throughput capacity, and regional deployment.GPT-3.5 and GPT-4 response timesThe content displays multiple graphs comparing the response times of various AI models, including Azure GPT-3.5 Turbo, OpenAI GPT-3.5 Turbo, Azure GPT-4, and others, with each graph illustrating the relationship between response time (in seconds) and the number of completion tokens.ChatGPT Integration Costs 2025: What Startups Actually PayThe image provides a detailed comparison of ChatGPT API pricing for 2025, highlighting costs per 1,000 tokens for GPT-3.5 and offering an example of monthly expenses for different app sizes, with references to OpenAI and key figures like Sam Altman.nPrice Per Tokenog imagetongyongSomething big is coming from OpenAISomething big is coming from OpenAI. Internal signals ...Databricks logoLangDBAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagram
Research conversation
You
Research question
Research and fact-check: GPT-5.5 Spud, Inference economics: token efficiency, API pricing, and production latency/throughput tradeoffs.
Studio Global AI40 sources
Cited answer
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 i.j4i.i2
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].
People also ask
What is the short answer to "Research and fact-check: GPT-5.5 Spud, Inference economics: token efficiency, API pricing, and production latency/throughput tradeoffs."?
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 late
What are the key points to validate first?
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 late 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
Which related topic should I explore next?
Continue with "Research and fact-check: GPT-5.5 Spud, Agentic coding and tool orchestration, including tool calling, web search, and tool-heavy workflows." for another angle and extra citations.
Cross-check this answer against "Research and fact-check: GPT-5.5 Spud, Long-context reliability and instruction retention across extended workflows.".
Analysis of API providers for GPT-5 mini (medium) across performance metrics including latency (time to first token), output speed (output tokens per second), price and others. ## Time to First Answer Token: GPT-5 mini (medium) Providers. The providers with the lowest time to first token for GPT-5 mini (medium) are OpenAI (19.79s) and Azure (29.52s). The providers with the lowest input token pricing for GPT-5 mini (medium) are Azure ($0.25 per 1M input tokens) and OpenAI ($0.25 per 1M input tokens). ### Which provider has the lowest output price for GPT-5 mini (medium)? The providers with the…
GPT-5.5 Release Date: 70% Odds for April, Spud Pretraining Done. # GPT-5.5 Release Date: Spud Pretraining Done, What Developers Should Prepare For (2026). No official GPT-5.5 release date, no model card, no API pricing has been announced. Speculation | Extrapolated from GPT-5.4 pricing trends || Release before June 2026 | Likely | Based on typical post-training timeline |. Spud is OpenAI's next-generation model following the GPT-5.4 release. TokenMix.ai has been tracking OpenAI's release cadence: five GPT-5.x models shipped in under seven months. GPT-5.4 pricing (confirmed):. | GP…
OpenAI API costs range from $0.20 to $30 per million input tokens. OpenAI API cost depends on the model you choose and the number of tokens you process. The key takeaway on OpenAI model pricing: GPT-5.4 Mini at $0.75 input is 4x cheaper than Claude Sonnet 4.6 on input, while GPT-5.4 Nano at $0.20 is the cheapest proprietary model available — undercutting Gemini 3.1 Flash-Lite ($0.25) and sitting just below DeepSeek V3.2 ($0.28). CloudZero’s OpenAI integration ingests both cost and usage dat…
Cookiebot 2Learn more about this providerCookieConsent[x2]Stores the user's cookie consent state for the current domainMaximum Storage Duration: 1 yearType: HTTP Cookie. * Microsoft 5[Learn more about this prov…
Join the conversation on AI models, pricing, and tools. Price Per TokenPrice Per Token. # GPT 5 API Pricing 2026. Compare pricing, benchmarks, and providers for GPT 5. Pricing starts at $1.25 per million input tokens and $10.00 per million output tokens. Compare GPT 5 with 0 similar models by price. ## Current Pricing (per 1M tokens). | O OpenAI | GPT-5 | $1.250 | $10.000 | 55.8 | 80.6 | 67.3 | 400,000 | Try |. * Some models use tiered pricing based on prompt length. Displayed prices are for prompts ≤ 200k tokens. GPT 5 is available from multiple providers with different pricing and…
For latency, Azure (54.46s), OpenAI (69.85s), Databricks (80.23s) offer the lowest time to first token. For pricing, Databricks (3.44), Azure (3.44), OpenAI (
Search the API docs. ### Realtime API. ### Model optimization. ### Specialized models. ### Legacy APIs. + Building frontend UIs with Codex and Figma. ### API. * Building frontend UIs with Codex and Figma. GPT-5.4 is our frontier model for complex professional work. Learn more in our latest model guide. For tool-specific models, like search and computer use, there’s a fee per tool call. For models with a 1.05M context window (GPT-5.4 and GPT-5.4 pro), prompts with >272K input tokens are priced at 2x input and 1.5x output for the full session for standard, batch, and flex. Tools supported by…
Prompt caching. Prompt Caching works automatically on all your API requests (no code changes required) and has no additional fees associated with it. Prompt Caching is enabled for all recent models, gpt-4o and newer. ## Prompt cache retention. Prompt Caching can either use in-memory or extended retention policies. ### In-memory prompt cache retention. In-memory prompt cache retention is available for all models that support Prompt Caching. ### Extended prompt cache retention. Extended prompt cache retention is available for the following models:. * Use the i.j4i.i2
Prompting. Learn how to create prompts. OpenAI has some strategies and API design decisions to help you construct strong prompts and get consistently good results from a model. ### Prompts in the API. Variables and prompts share a base prompt, so when you create a new version, you can use that for evals and determine whether a prompt performs better or worse. ### Prompting tools and techniques. ## Create a prompt. Log in and use the OpenAI dashboard to create, save, version, and share your prompts. 3. Use the prompt in your Responses API call. 4. Create a new prompt version. To crea…
See details in the pricing page. Text tokens. Per 1M tokens. ∙. Batch API price. Input. $1.10. Cached input. $0.55. Output. $4.40. Quick comparison. Input.
While reducing the number of input tokens does result in lower latency, this is not usually a significant factor – cutting 50% of your prompt may only result in
Prompt Caching can reduce time-to-first-token latency by up to 80% and input token costs by up to 90%. * In-memory prompt caching works automatically on all your API requests. Prompt Caching is enabled for all recent models, gpt-4o and newer. When you provide a i.j4i.i2
prompt_cache_key
, it is combined with that hash to increase routing stickiness - meaning requests with the same prefix are more likely to land on the same engine and reuse cached KV state. It’s effective - one of our coding customers saw an improved hit rate from 60% to 87% when they started using i.j4i.i2
Choosing the right model, whether GPT-4o or a smaller option like GPT-4o-mini, requires balancing accuracy, latency, and cost. * Optimize for cost and latency second: Then aim to maintain accuracy with the cheapest, fastest model possible. * Using the most powerful model to optimize: Start with the most capable model available to achieve your accuracy targets. Cost and latency are considered secondary because if the model can’t hit your accuracy target then these concerns are moot. * Select a smaller model: Use models that balance reduced costs and latency with maintai…
[{'message': {'role': 'user',[{'message': {'role': 'user', 'content': 'Summarize the conversation we had so far.'}, 'content': 'Summarize the conversation we had so far.'}, 'metadata': {'synthetic': True, 'metadata': {'synthetic': True, 'kind': 'history_summary_prompt', 'kind': 'history_summary_prompt', 'summary_for_turns': '< all before idx 6 >'}}, 'summary_for_turns': '< all before idx 6 >'}}, {'message': {'role': 'assistant', {'message': {'role': 'assistant', 'content': 'Product & Environment:\n- Device: Router\n- OS: Windows 10\n- Firmware: v1.0.3\n\nReported Issue:\n- Router fail…
From that day forward, they told the tale of Lily's journey, celebrating her as a heroine who embodied the power of love, kindness, and the belief that true treasure lies within oneself.\n\nAnd so, the story of Lily became an everlasting legend, inspiring generations to follow their dreams, be selfless, and find the true treasures that lie within their hearts.', role='assistant', function_call=None, tool_calls=None))], created=1701010806, model='gpt-3.5-turbo-0613', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=641, prompt_tokens=12, total_tokens=…
AI research involves various sub-fields focused on specific goals like reasoning, learning, and perception, using techniques from mathematics, logic, and other disciplines. The effectiveness of deep learning, which surged between 2012 and 2015, is attributed not to new theoretical advances but to increased computational power, including the use of GPUs, and the availability of large datasets like ImageNet. Generative Pre-trained Transformers (GPT) are large language models that learn from vast amounts of text to predict the next token in a sequence, thereby generating human-like text. This in…
1 2 3 4 5 6 7 8 curl https://api.openai.com/v1/batches \ curl https://api.openai.com/v1/batches \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Authorization: Bearer $OPENAI_API_KEY " \ -H "Content-Type: application/json" \ -H "Content-Type: application/json" \ -d '{ -d '{ "input_file_id": "file-abc123", "endpoint": "/v1/chat/completions", "completion_window": "24h" }'. Once the batch is complete, you can download the output by making a request against the Files API via the i.j4i.i2
output_file_id
field from the Batch object and writing it to a file on your machine, in this case `batch_output.jso…
Configuring Priority processing. Requests to the Responses or Completions endpoints can be configured to use Priority processing through either a request parameter, or a Project setting. To opt-in to Priority processing at the request level, include the i.j4i.i2
service_tier=priority
parameter for Completions or Responses. Create a response with priority processing. 1 2 3 4 5 curl https://api.openai.com/v1/responses -H "Authorization: Bearer $OPENAI_API_KEY" -H "Content-Type: application/json" -d '{ curl https://api.openai.com/v1/responses -H "Authorization: Bearer $OPENAI_API_KEY " -H "Content-T…
This track is designed for developers and technical learners who want to build production-ready AI applications with OpenAI’s models and tools. If you want to build robust AI applications, you need to make sure the model outputs are reliable. Now that we’ve covered how to build AI applications and incorporate some basic AI techniques in the development process, we’ll focus on testing and evaluation, learning how to integrate evals and guardrails to confidently ship AI applications that are safe, predictable, and production-ready. Now that you’ve learned how to incorporate evals into your work…
def optimize_matrix(def optimize_matrix( modified_embedding_length: int = 2048, # in my brief experimentation, bigger was better (2048 is length of babbage encoding) modified_embedding_length: int = 2048, # in my brief experimentation, bigger was better (2048 is length of babbage encoding) batch_size: int = 100, batch_size: int = 100, max_epochs: int = 100, max_epochs: int = 100, learning_rate: float = 100.0, # seemed to work best when similar to batch size - feel free to try a range of values learning_rate: float = 100.0, # seemed to work best when similar to batch size - feel free to try a…
OpenAI needs momentum. That is why GPT 5.5 Spud feels important. It looks less like a cosmetic update and more like a response to pressure from every direction. 15 hours ago
So GPT-5.5 "Spud" is coming. OpenAI finished pretraining around March 24. Altman called it "a very strong model that could really accelerate the ... Apr 5, 2026
Is OpenAI GPT-5.5 Spud Faster and More Efficient Than GPT-5.4? Codex App. OpenAI's GPT 5.5 Spud is the new base model. Pretraining wrapped ... 1 day ago
... OpenAI's GPT 5.5 'Spud' Leak Explained OpenAI just faced its massive leak yet with the reveal of 'Spud,' a model so powerful it might be ... 20 hours ago
... OpenAI's next major AI breakthrough. Here's everything you need to ... GPT-5.5 “Spud” Explained – The Truth Behind OpenAI's Next Big Model. 7 days ago
... GPT-5.5 "Spud" Tests Are Leaking OpenAI's shadow drop of GPT Pro updates, the front-end coding gains vs. Claude Opus 4.7, Polymarket ... 7 hours ago
ReportsPublic URLApr 21, 202651 sources
Research and fact-check: GPT-5.5 Spud, Inference economics: token efficiency, API pricing, and production latency/throughput tradeoffs.
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,...
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 h
Key takeaways
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 late
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
Research answer
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 i.j4i.i2
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].
Supporting visuals
OpenAI API Pricing October 2025: Complete Guide to GPT-5An infographic titled "OpenAI API Pricing October 2025" with a robot mascot pointing to different API cost categories, including GPT-5 access per 1K tokens, real-time API usage per second, and image generation API per image output, alongside a cost growth chart.GPT-5 API: $1.25 Pricing, 90% Cache Discount & 272KThe image features a bar chart that displays GPT-5 API performance metrics, highlighting rapid response times for tasks like text, chat, embeddings, images, fine-tuning, and streaming, with throughput rates, as well as additional information about latency, throughput capacity, and regional deployment.GPT-3.5 and GPT-4 response timesThe content displays multiple graphs comparing the response times of various AI models, including Azure GPT-3.5 Turbo, OpenAI GPT-3.5 Turbo, Azure GPT-4, and others, with each graph illustrating the relationship between response time (in seconds) and the number of completion tokens.ChatGPT Integration Costs 2025: What Startups Actually PayThe image provides a detailed comparison of ChatGPT API pricing for 2025, highlighting costs per 1,000 tokens for GPT-3.5 and offering an example of monthly expenses for different app sizes, with references to OpenAI and key figures like Sam Altman.nPrice Per Tokenog imagetongyongSomething big is coming from OpenAISomething big is coming from OpenAI. Internal signals ...Databricks logoLangDBAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagramAssistants object architecture diagram
Research conversation
You
Research question
Research and fact-check: GPT-5.5 Spud, Inference economics: token efficiency, API pricing, and production latency/throughput tradeoffs.
Studio Global AI40 sources
Cited answer
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 i.j4i.i2
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].
People also ask
What is the short answer to "Research and fact-check: GPT-5.5 Spud, Inference economics: token efficiency, API pricing, and production latency/throughput tradeoffs."?
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 late
What are the key points to validate first?
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 late 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
Which related topic should I explore next?
Continue with "Research and fact-check: GPT-5.5 Spud, Agentic coding and tool orchestration, including tool calling, web search, and tool-heavy workflows." for another angle and extra citations.
Cross-check this answer against "Research and fact-check: GPT-5.5 Spud, Long-context reliability and instruction retention across extended workflows.".
Analysis of API providers for GPT-5 mini (medium) across performance metrics including latency (time to first token), output speed (output tokens per second), price and others. ## Time to First Answer Token: GPT-5 mini (medium) Providers. The providers with the lowest time to first token for GPT-5 mini (medium) are OpenAI (19.79s) and Azure (29.52s). The providers with the lowest input token pricing for GPT-5 mini (medium) are Azure ($0.25 per 1M input tokens) and OpenAI ($0.25 per 1M input tokens). ### Which provider has the lowest output price for GPT-5 mini (medium)? The providers with the…
GPT-5.5 Release Date: 70% Odds for April, Spud Pretraining Done. # GPT-5.5 Release Date: Spud Pretraining Done, What Developers Should Prepare For (2026). No official GPT-5.5 release date, no model card, no API pricing has been announced. Speculation | Extrapolated from GPT-5.4 pricing trends || Release before June 2026 | Likely | Based on typical post-training timeline |. Spud is OpenAI's next-generation model following the GPT-5.4 release. TokenMix.ai has been tracking OpenAI's release cadence: five GPT-5.x models shipped in under seven months. GPT-5.4 pricing (confirmed):. | GP…
OpenAI API costs range from $0.20 to $30 per million input tokens. OpenAI API cost depends on the model you choose and the number of tokens you process. The key takeaway on OpenAI model pricing: GPT-5.4 Mini at $0.75 input is 4x cheaper than Claude Sonnet 4.6 on input, while GPT-5.4 Nano at $0.20 is the cheapest proprietary model available — undercutting Gemini 3.1 Flash-Lite ($0.25) and sitting just below DeepSeek V3.2 ($0.28). CloudZero’s OpenAI integration ingests both cost and usage dat…
Cookiebot 2Learn more about this providerCookieConsent[x2]Stores the user's cookie consent state for the current domainMaximum Storage Duration: 1 yearType: HTTP Cookie. * Microsoft 5[Learn more about this prov…
Join the conversation on AI models, pricing, and tools. Price Per TokenPrice Per Token. # GPT 5 API Pricing 2026. Compare pricing, benchmarks, and providers for GPT 5. Pricing starts at $1.25 per million input tokens and $10.00 per million output tokens. Compare GPT 5 with 0 similar models by price. ## Current Pricing (per 1M tokens). | O OpenAI | GPT-5 | $1.250 | $10.000 | 55.8 | 80.6 | 67.3 | 400,000 | Try |. * Some models use tiered pricing based on prompt length. Displayed prices are for prompts ≤ 200k tokens. GPT 5 is available from multiple providers with different pricing and…
For latency, Azure (54.46s), OpenAI (69.85s), Databricks (80.23s) offer the lowest time to first token. For pricing, Databricks (3.44), Azure (3.44), OpenAI (
Search the API docs. ### Realtime API. ### Model optimization. ### Specialized models. ### Legacy APIs. + Building frontend UIs with Codex and Figma. ### API. * Building frontend UIs with Codex and Figma. GPT-5.4 is our frontier model for complex professional work. Learn more in our latest model guide. For tool-specific models, like search and computer use, there’s a fee per tool call. For models with a 1.05M context window (GPT-5.4 and GPT-5.4 pro), prompts with >272K input tokens are priced at 2x input and 1.5x output for the full session for standard, batch, and flex. Tools supported by…
Prompt caching. Prompt Caching works automatically on all your API requests (no code changes required) and has no additional fees associated with it. Prompt Caching is enabled for all recent models, gpt-4o and newer. ## Prompt cache retention. Prompt Caching can either use in-memory or extended retention policies. ### In-memory prompt cache retention. In-memory prompt cache retention is available for all models that support Prompt Caching. ### Extended prompt cache retention. Extended prompt cache retention is available for the following models:. * Use the i.j4i.i2
Prompting. Learn how to create prompts. OpenAI has some strategies and API design decisions to help you construct strong prompts and get consistently good results from a model. ### Prompts in the API. Variables and prompts share a base prompt, so when you create a new version, you can use that for evals and determine whether a prompt performs better or worse. ### Prompting tools and techniques. ## Create a prompt. Log in and use the OpenAI dashboard to create, save, version, and share your prompts. 3. Use the prompt in your Responses API call. 4. Create a new prompt version. To crea…
See details in the pricing page. Text tokens. Per 1M tokens. ∙. Batch API price. Input. $1.10. Cached input. $0.55. Output. $4.40. Quick comparison. Input.
While reducing the number of input tokens does result in lower latency, this is not usually a significant factor – cutting 50% of your prompt may only result in
Prompt Caching can reduce time-to-first-token latency by up to 80% and input token costs by up to 90%. * In-memory prompt caching works automatically on all your API requests. Prompt Caching is enabled for all recent models, gpt-4o and newer. When you provide a i.j4i.i2
prompt_cache_key
, it is combined with that hash to increase routing stickiness - meaning requests with the same prefix are more likely to land on the same engine and reuse cached KV state. It’s effective - one of our coding customers saw an improved hit rate from 60% to 87% when they started using i.j4i.i2
Choosing the right model, whether GPT-4o or a smaller option like GPT-4o-mini, requires balancing accuracy, latency, and cost. * Optimize for cost and latency second: Then aim to maintain accuracy with the cheapest, fastest model possible. * Using the most powerful model to optimize: Start with the most capable model available to achieve your accuracy targets. Cost and latency are considered secondary because if the model can’t hit your accuracy target then these concerns are moot. * Select a smaller model: Use models that balance reduced costs and latency with maintai…
[{'message': {'role': 'user',[{'message': {'role': 'user', 'content': 'Summarize the conversation we had so far.'}, 'content': 'Summarize the conversation we had so far.'}, 'metadata': {'synthetic': True, 'metadata': {'synthetic': True, 'kind': 'history_summary_prompt', 'kind': 'history_summary_prompt', 'summary_for_turns': '< all before idx 6 >'}}, 'summary_for_turns': '< all before idx 6 >'}}, {'message': {'role': 'assistant', {'message': {'role': 'assistant', 'content': 'Product & Environment:\n- Device: Router\n- OS: Windows 10\n- Firmware: v1.0.3\n\nReported Issue:\n- Router fail…
From that day forward, they told the tale of Lily's journey, celebrating her as a heroine who embodied the power of love, kindness, and the belief that true treasure lies within oneself.\n\nAnd so, the story of Lily became an everlasting legend, inspiring generations to follow their dreams, be selfless, and find the true treasures that lie within their hearts.', role='assistant', function_call=None, tool_calls=None))], created=1701010806, model='gpt-3.5-turbo-0613', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=641, prompt_tokens=12, total_tokens=…
AI research involves various sub-fields focused on specific goals like reasoning, learning, and perception, using techniques from mathematics, logic, and other disciplines. The effectiveness of deep learning, which surged between 2012 and 2015, is attributed not to new theoretical advances but to increased computational power, including the use of GPUs, and the availability of large datasets like ImageNet. Generative Pre-trained Transformers (GPT) are large language models that learn from vast amounts of text to predict the next token in a sequence, thereby generating human-like text. This in…
1 2 3 4 5 6 7 8 curl https://api.openai.com/v1/batches \ curl https://api.openai.com/v1/batches \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Authorization: Bearer $OPENAI_API_KEY " \ -H "Content-Type: application/json" \ -H "Content-Type: application/json" \ -d '{ -d '{ "input_file_id": "file-abc123", "endpoint": "/v1/chat/completions", "completion_window": "24h" }'. Once the batch is complete, you can download the output by making a request against the Files API via the i.j4i.i2
output_file_id
field from the Batch object and writing it to a file on your machine, in this case `batch_output.jso…
Configuring Priority processing. Requests to the Responses or Completions endpoints can be configured to use Priority processing through either a request parameter, or a Project setting. To opt-in to Priority processing at the request level, include the i.j4i.i2
service_tier=priority
parameter for Completions or Responses. Create a response with priority processing. 1 2 3 4 5 curl https://api.openai.com/v1/responses -H "Authorization: Bearer $OPENAI_API_KEY" -H "Content-Type: application/json" -d '{ curl https://api.openai.com/v1/responses -H "Authorization: Bearer $OPENAI_API_KEY " -H "Content-T…
This track is designed for developers and technical learners who want to build production-ready AI applications with OpenAI’s models and tools. If you want to build robust AI applications, you need to make sure the model outputs are reliable. Now that we’ve covered how to build AI applications and incorporate some basic AI techniques in the development process, we’ll focus on testing and evaluation, learning how to integrate evals and guardrails to confidently ship AI applications that are safe, predictable, and production-ready. Now that you’ve learned how to incorporate evals into your work…
def optimize_matrix(def optimize_matrix( modified_embedding_length: int = 2048, # in my brief experimentation, bigger was better (2048 is length of babbage encoding) modified_embedding_length: int = 2048, # in my brief experimentation, bigger was better (2048 is length of babbage encoding) batch_size: int = 100, batch_size: int = 100, max_epochs: int = 100, max_epochs: int = 100, learning_rate: float = 100.0, # seemed to work best when similar to batch size - feel free to try a range of values learning_rate: float = 100.0, # seemed to work best when similar to batch size - feel free to try a…
OpenAI needs momentum. That is why GPT 5.5 Spud feels important. It looks less like a cosmetic update and more like a response to pressure from every direction. 15 hours ago
So GPT-5.5 "Spud" is coming. OpenAI finished pretraining around March 24. Altman called it "a very strong model that could really accelerate the ... Apr 5, 2026
Is OpenAI GPT-5.5 Spud Faster and More Efficient Than GPT-5.4? Codex App. OpenAI's GPT 5.5 Spud is the new base model. Pretraining wrapped ... 1 day ago
... OpenAI's GPT 5.5 'Spud' Leak Explained OpenAI just faced its massive leak yet with the reveal of 'Spud,' a model so powerful it might be ... 20 hours ago
... OpenAI's next major AI breakthrough. Here's everything you need to ... GPT-5.5 “Spud” Explained – The Truth Behind OpenAI's Next Big Model. 7 days ago
... GPT-5.5 "Spud" Tests Are Leaking OpenAI's shadow drop of GPT Pro updates, the front-end coding gains vs. Claude Opus 4.7, Polymarket ... 7 hours ago
CookieConsent[x2]Stores the user's cookie consent state for the current domainMaximum Storage Duration: 1 yearType: HTTP Cookie. * Microsoft 5[Learn more about this prov…