{"content_id":"aillofw1zj","slug":"openai-gpt-5-6-price-performance-model-routing","locale":"en","schema_type":"TechArticle","category":"ai_data","category_name":"AI Data","title":"OpenAI GPT-5.6 Pricing and Speed Overhaul and Model Selection Strategy","summary":"Starting July 30, 2026, OpenAI cut API prices for GPT-5.6 Luna and Terra by 80% and 20%, respectively, and introduced Fast mode for Sol. The overhaul focuses on reducing cost per result by assigning models to the planning, execution, and verification stages rather than using the top-tier model for every task.","author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["The API price for GPT-5.6 Luna was reduced to $0.20 per 1 million input tokens and $1.20 per 1 million output tokens.","The API price for GPT-5.6 Terra was reduced to $2 per 1 million input tokens and $12 per 1 million output tokens.","At the time of the announcement, GPT-5.6 Sol's Fast mode was up to 2.5 times faster than Standard processing, cost twice as much, and maintained the model's intelligence level.","Companies can adopt a tiered configuration in which Sol handles complex decisions and planning, while Luna or Terra handles repetitive execution and verification.","OpenAI explains that joint optimization of the model, inference infrastructure, and agent harness enabled the price cuts and throughput improvements."],"content_markdown":"OpenAI has linked improvements in the execution efficiency of the GPT-5.6 family to lower API prices and faster processing. The key is not to apply the single most powerful model to every task, but to combine Sol, Terra, and Luna according to each task’s risk, complexity, latency, and verifiability to reduce the cost per result.\n\nPricing and performance figures are based on information announced by OpenAI on July 30, 2026. Customer benchmarks and efficiency improvement figures are measurements from OpenAI or companies cited in the announcement, so they should not be assumed to be reproducible in every environment.\n\n## July 30, 2026 API Pricing Changes\n\nThe revised API prices for GPT-5.6 Luna and Terra are as follows.\n\n| Model | Per 1 Million Input Tokens | Per 1 Million Output Tokens | Reduction | Primary Role |\n|---|---:|---:|---:|---|\n| GPT-5.6 Luna | $0.20 | $1.20 | 80% | High-volume processing, repetitive tasks, clearly defined execution work |\n| GPT-5.6 Terra | $2 | $12 | 20% | Everyday work requiring a balance of quality, cost, and speed |\n| GPT-5.6 Sol | No change in the announcement | No change in the announcement | None | Complex reasoning, planning, important decisions |\n\nPaid subscription prices and total allocation budgets for ChatGPT and Codex are not changing as a result of this announcement. However, fewer credits will be deducted when using Terra and Luna. OpenAI stated that pricing changes through AWS would be rolled out gradually beginning later on July 30.\n\n### API Cost Calculation Example\n\nToken costs can be calculated as follows.\n\n`Total cost = Number of input tokens ÷ 1,000,000 × input price + Number of output tokens ÷ 1,000,000 × output price`\n\nFor example, if 10 million input tokens and 2 million output tokens are processed, the cost based solely on token prices is as follows.\n\n| Model | Input Cost | Output Cost | Total |\n|---|---:|---:|---:|\n| Luna | $2 | $2.40 | $4.40 |\n| Terra | $20 | $24 | $44 |\n\nActual charges may be affected by service-specific conditions, including whether caching is applied, the selected processing method, and the tool-calling structure.\n\n## Roles Within the GPT-5.6 Family\n\nGPT-5.6 is not a single model but a tiered family with different cost and performance characteristics.\n\n### GPT-5.6 Sol\n\nSol handles the highest level of reasoning and complex problem-solving. It is suitable for ambiguous requirements, decisions with a high cost of failure, long-term planning, and reviews of important results.\n\n### GPT-5.6 Terra\n\nTerra aims to balance performance, cost, and response speed. It is suitable for tasks that require greater judgment than Luna but do not always require Sol, such as internal organizational Q\u0026A, bounded agent work, and general analysis and coding.\n\n### GPT-5.6 Luna\n\nLuna is the fastest and least expensive tier. Because it supports tool calls and multistep workflows in addition to generating simple, short text, it can serve as an execution model for repeating clearly defined tasks at scale.\n\nRepresentative applications include:\n\n- Classifying documents and customer inquiries at scale\n- Extracting structured data\n- Repetitive code modifications\n- Writing and running tests\n- Generating documents under clear rules\n- Reviewing content at scale\n- Background agent automation\n- Repetitive research assistance\n\nOpenAI claims that Luna delivers performance similar to that of models considered state-of-the-art a year ago at approximately 6% of the estimated cost per task and nearly 9 times the speed. Here, 6% is not a direct comparison of token prices, but a comparison of the estimated cost required to achieve the same task result.\n\n## Features of Sol Fast Mode\n\nFast mode for the API has been introduced for GPT-5.6 Sol. It replaces the existing Priority Processing and corresponds to Codex’s `/fast` feature.\n\n| Item | Fast mode |\n|---|---|\n| Speed | Up to 2.5 times faster than Standard processing |\n| Model intelligence | The same as Standard, according to OpenAI’s announcement |\n| Price | Twice the price of Standard processing |\n| Backward compatibility | Requests with the `priority` tag are automatically processed in Fast mode |\n\nThe phrase “up to 2.5 times faster” does not guarantee that latency for every request will decrease by exactly the same ratio. Actual perceived speed may vary depending on prompt length, output length, service load, and the number of tool calls.\n\n### When Fast Mode Is Suitable\n\n- Real-time services where users wait for responses\n- Development environments that rapidly iterate on code changes and verification\n- Tasks where Sol calls determine the latency of the entire agent\n- Situations where even a delay of a few minutes matters, such as incident response or outage analysis\n- Work where the cost caused by processing delays exceeds the additional API cost\n\nStandard processing may be more economical for tasks without urgent completion deadlines, such as background batches, overnight analysis, and asynchronous processing.\n\n## Outcome-Oriented Model Selection Criteria\n\nModel selection is not a matter of choosing the model at the top of a leaderboard. The following questions should be reviewed for each task.\n\n| Decision Factor | Question to Ask |\n|---|---|\n| Impact of failure | How would an error affect customers, revenue, security, or regulatory compliance? |\n| Error tolerance | Can a human reviewer or automated rule catch errors? |\n| Latency | Is a user waiting in real time, or can the task be processed asynchronously? |\n| Throughput | How many cases must be processed per day or month? |\n| Problem clarity | Are the input, rules, and expected output sufficiently defined? |\n| Value of reasoning | Does stronger reasoning meaningfully improve the quality of the actual result? |\n| Verifiability | Can the result be evaluated through tests, schemas, or cross-checking? |\n\nClear tasks that can be verified automatically are likely to be suitable for Luna. Terra can be considered when a consistent level of judgment is needed. Sol is suitable for resolving ambiguity, making high-risk judgments, or conducting final reviews of failure outcomes.\n\n## Planning With Sol and Executing With Luna\n\nDifferent models can be assigned to different stages even within a single agent task. For example, a coding agent can be structured as follows.\n\n1. Sol identifies ambiguities in the requirements and organizes questions.\n2. Sol determines the implementation plan, scope of changes, and risk factors.\n3. Luna implements the clarified changes in code.\n4. Luna writes and runs tests.\n5. Luna or Terra evaluates the test results and code differences.\n6. Sol reviews only conclusions that are important or have a high likelihood of failure.\n\nThis structure can reduce costs compared with using Sol at every stage while concentrating strong reasoning capabilities on important judgments. However, splitting work across models requires separate designs for routing rules, error handling, log tracing, and evaluation systems.\n\n## Three Efficiency Layers Behind the Price Reductions\n\nOpenAI explains that the cost reductions resulted not merely from pricing policy, but from technical improvements across three layers.\n\n1. Token efficiency of the model itself\n2. Hardware efficiency of the inference system\n3. Efficiency of the agent harness connecting models, tools, and context\n\nThis explanation is based on technical materials released by OpenAI, and the full details of the cost structure have not been disclosed publicly. It is therefore difficult for outsiders to determine how much of the price reduction came from technical efficiency versus strategic pricing policy.\n\n## Model and Inference System Optimization\n\n### Improving Work Per Token\n\nAccording to OpenAI, GPT-5.6 was trained to optimize not only task success rates but also processing efficiency. This means it was designed to reduce unnecessarily long reasoning and repetition and reach the required result with fewer tokens, increasing intelligence and work performed per token.\n\n### Load Balancing and Request Routing\n\nThe inference system distributes requests across data centers and clusters based on region, available capacity, and accelerator type. Within a cluster, it selects model instances by considering factors such as current load, input context length, cache availability, and request characteristics.\n\nOpenAI stated that it used GPT-5.6 Sol and Codex to analyze production traffic, investigate the causes of load imbalances, test routing strategies, and adjust heuristics.\n\n### GPU Kernel Optimization\n\nGPU kernels are the core code that executes a model’s mathematical operations on hardware. Processing costs can vary even on the same GPU depending on memory movement, synchronization, data layout, and parallelization methods.\n\nAccording to OpenAI, GPT-5.6 Sol participated in writing and optimizing production kernels using Triton and Gluon within the Codex environment. The company stated that kernel improvements and related optimizations reduced the end-to-end cost of serving the model by 20%.\n\nEnd-to-end cost refers not to a single operation, but to the cost of processing an actual request in its entirety, including routing, data movement, model execution, and output generation.\n\n### Verifying Kernel Accuracy\n\nEven a fast kernel cannot be used if it produces numerically incorrect results. OpenAI explained that it invested in verification tools, including FpSan, to check the accuracy of AI-written kernels. FpSan stands for Floating-Point Sanitizer and is an open-source tool that detects errors related to floating-point operations.\n\nThis demonstrates that when AI generates production infrastructure code, numerical verification, regression testing, and failure recovery procedures are required alongside performance benchmarks.\n\n## Speculative Decoding and KV Cache\n\n### Speculative Decoding\n\nSpeculative decoding is a method in which a small draft model first proposes upcoming tokens and a larger primary model verifies multiple candidates in parallel. If the proposals are accepted, the number of expensive sequential computations performed by the primary model can be reduced.\n\nOpenAI stated that GPT-5.6 Sol designed and ran hundreds of experiments that varied the size and architecture of the draft model and monitored training. The company explained that this improved token generation efficiency by at least 15%.\n\n### KV Cache and Workload-Specific Configurations\n\nAs a model processes input, it creates a Key-Value cache, or KV cache. The optimal configuration varies according to input and output length, batch size, cache hit rate, the number of concurrent requests, memory capacity, and model sharding method.\n\nOpenAI explains that it used Sol and Codex to analyze actual workloads, evaluate candidate configurations, and finely tune engine configurations for each type of task. The goal is to process more requests on the same hardware.\n\n## Agent Harnesses and Context Costs\n\nAn agent calls models and tools multiple times to resolve a single user request. If a task requires 30 model calls and each call incurs 1 second of unnecessary delay, the total latency can increase by approximately 30 seconds.\n\nOpenAI describes the Rust-based orchestration layer connecting models, tools, and user environments as an agent harness.\n\n### Deferring Exposure of Tools Until Needed\n\nIncluding information about every tool, plugin, skill, and MCP integration in the prompt from the outset increases input tokens and latency. The harness uses deferred discovery, exposing relevant tool information only when needed. OpenAI stated that tool output is limited to 10,000 tokens by default unless the model requests a separate limit.\n\n### Exact Prefix Preservation and Prompt Caching\n\nPrompt caching reduces redundant computation by reusing an identical prefix from previously processed input. To reuse the cache, the prompt prefix must match exactly.\n\nOpenAI’s harness manages history in an append-only structure, adding new messages and tool results at the end. Tools are presented in a deterministic order, while execution settings such as approval policies are applied at runtime without changing the tool definitions themselves. This approach helps increase cache hit rates in repetitive agent loops.\n\n## How to Interpret Enterprise Case Study Figures\n\nOpenAI’s official announcement included evaluations from Replit, Notion, Ramp, Blitzy, Cognition, Dust, and others.\n\n- Notion stated that in its own evaluation, Terra delivered quality similar to GPT-5.5 at half the cost per task and in 60% less time.\n- Blitzy explained that after adopting Luna, its prompt cache reuse rate increased from 24% to 90%, while costs were 87% lower than with its previous default model.\n- Dust stated that for the same agent work, Luna was 40% faster and 40% less expensive than its previous default model.\n- Ramp stated that it uses Luna as the default model for background agent automation.\n\nThese figures are case studies measured using each company’s internal work, prompts, evaluation criteria, and system architecture. Because they are not independent common benchmarks, they should not be applied directly to the work of other organizations. Before adoption, organizations need their own evaluations reflecting actual data and the cost of errors.\n\n## Pre-Adoption Validation Checklist\n\n1. Prepare a representative sample of tasks and correct answers or evaluation criteria.\n2. Compare the success rates and retry rates of Luna, Terra, and Sol under the same conditions.\n3. Include not only token costs but also tool calls, review personnel, and failure recovery costs.\n4. Measure 95th- or 99th-percentile latency in addition to average latency.\n5. Classify stages that support automated testing or schema validation as candidates for lower-cost models.\n6. Apply separate approval and logging policies to work involving personal data, security, or regulatory matters.\n7. Define routing criteria for escalating low-confidence results to Terra or Sol.\n8. Use actual traffic to verify whether the value of reduced latency exceeds the additional cost of Fast mode.\n\n## Significance and Limitations\n\nThis restructuring shows that the basis of AI model competition is shifting from a single top score to cost per result. By assigning Luna to large-scale repetitive work, Terra to everyday knowledge work, and Sol to ambiguous and important judgments, organizations can combine intelligence, speed, and cost according to each task.\n\nHowever, publicly available information does not make it possible to separate how much of the 80% price reduction came from improvements in technical efficiency and how much came from market strategy. The economics of lower-cost models are also not determined by token prices alone. If higher error rates lead to more retries and human review, the total cost of the work may increase.\n\nTherefore, the key metric is not the price of a single model call, but the total cost of producing one result that passes validation. Success rates by model, the number of retries, latency, and review costs must be measured together to determine whether GPT-5.6’s price reductions translate into actual business value.","content_html":"\u003cp\u003eOpenAI has linked improvements in the execution efficiency of the GPT-5.6 family to lower API prices and faster processing. The key is not to apply the single most powerful model to every task, but to combine Sol, Terra, and Luna according to each task’s risk, complexity, latency, and verifiability to reduce the cost per result.\u003c/p\u003e\n\u003cp\u003ePricing and performance figures are based on information announced by OpenAI on July 30, 2026. Customer benchmarks and efficiency improvement figures are measurements from OpenAI or companies cited in the announcement, so they should not be assumed to be reproducible in every environment.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#july-30-2026-api-pricing-changes\" class=\"anchor\" id=\"july-30-2026-api-pricing-changes\"\u003e\u003c/a\u003eJuly 30, 2026 API Pricing Changes\u003c/h2\u003e\n\u003cp\u003eThe revised API prices for GPT-5.6 Luna and Terra are as follows.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003cth\u003ePer 1 Million Input Tokens\u003c/th\u003e\n\u003cth\u003ePer 1 Million Output Tokens\u003c/th\u003e\n\u003cth\u003eReduction\u003c/th\u003e\n\u003cth\u003ePrimary Role\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Model\"\u003eGPT-5.6 Luna\u003c/td\u003e\n\u003ctd data-label=\"Per 1 Million Input Tokens\"\u003e$0.20\u003c/td\u003e\n\u003ctd data-label=\"Per 1 Million Output Tokens\"\u003e$1.20\u003c/td\u003e\n\u003ctd data-label=\"Reduction\"\u003e80%\u003c/td\u003e\n\u003ctd data-label=\"Primary Role\"\u003eHigh-volume processing, repetitive tasks, clearly defined execution work\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Model\"\u003eGPT-5.6 Terra\u003c/td\u003e\n\u003ctd data-label=\"Per 1 Million Input Tokens\"\u003e$2\u003c/td\u003e\n\u003ctd data-label=\"Per 1 Million Output Tokens\"\u003e$12\u003c/td\u003e\n\u003ctd data-label=\"Reduction\"\u003e20%\u003c/td\u003e\n\u003ctd data-label=\"Primary Role\"\u003eEveryday work requiring a balance of quality, cost, and speed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Model\"\u003eGPT-5.6 Sol\u003c/td\u003e\n\u003ctd data-label=\"Per 1 Million Input Tokens\"\u003eNo change in the announcement\u003c/td\u003e\n\u003ctd data-label=\"Per 1 Million Output Tokens\"\u003eNo change in the announcement\u003c/td\u003e\n\u003ctd data-label=\"Reduction\"\u003eNone\u003c/td\u003e\n\u003ctd data-label=\"Primary Role\"\u003eComplex reasoning, planning, important decisions\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003ePaid subscription prices and total allocation budgets for ChatGPT and Codex are not changing as a result of this announcement. However, fewer credits will be deducted when using Terra and Luna. OpenAI stated that pricing changes through AWS would be rolled out gradually beginning later on July 30.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#api-cost-calculation-example\" class=\"anchor\" id=\"api-cost-calculation-example\"\u003e\u003c/a\u003eAPI Cost Calculation Example\u003c/h3\u003e\n\u003cp\u003eToken costs can be calculated as follows.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eTotal cost = Number of input tokens ÷ 1,000,000 × input price + Number of output tokens ÷ 1,000,000 × output price\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor example, if 10 million input tokens and 2 million output tokens are processed, the cost based solely on token prices is as follows.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003cth\u003eInput Cost\u003c/th\u003e\n\u003cth\u003eOutput Cost\u003c/th\u003e\n\u003cth\u003eTotal\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Model\"\u003eLuna\u003c/td\u003e\n\u003ctd data-label=\"Input Cost\"\u003e$2\u003c/td\u003e\n\u003ctd data-label=\"Output Cost\"\u003e$2.40\u003c/td\u003e\n\u003ctd data-label=\"Total\"\u003e$4.40\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Model\"\u003eTerra\u003c/td\u003e\n\u003ctd data-label=\"Input Cost\"\u003e$20\u003c/td\u003e\n\u003ctd data-label=\"Output Cost\"\u003e$24\u003c/td\u003e\n\u003ctd data-label=\"Total\"\u003e$44\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eActual charges may be affected by service-specific conditions, including whether caching is applied, the selected processing method, and the tool-calling structure.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#roles-within-the-gpt-56-family\" class=\"anchor\" id=\"roles-within-the-gpt-56-family\"\u003e\u003c/a\u003eRoles Within the GPT-5.6 Family\u003c/h2\u003e\n\u003cp\u003eGPT-5.6 is not a single model but a tiered family with different cost and performance characteristics.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#gpt-56-sol\" class=\"anchor\" id=\"gpt-56-sol\"\u003e\u003c/a\u003eGPT-5.6 Sol\u003c/h3\u003e\n\u003cp\u003eSol handles the highest level of reasoning and complex problem-solving. It is suitable for ambiguous requirements, decisions with a high cost of failure, long-term planning, and reviews of important results.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#gpt-56-terra\" class=\"anchor\" id=\"gpt-56-terra\"\u003e\u003c/a\u003eGPT-5.6 Terra\u003c/h3\u003e\n\u003cp\u003eTerra aims to balance performance, cost, and response speed. It is suitable for tasks that require greater judgment than Luna but do not always require Sol, such as internal organizational Q\u0026amp;A, bounded agent work, and general analysis and coding.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#gpt-56-luna\" class=\"anchor\" id=\"gpt-56-luna\"\u003e\u003c/a\u003eGPT-5.6 Luna\u003c/h3\u003e\n\u003cp\u003eLuna is the fastest and least expensive tier. Because it supports tool calls and multistep workflows in addition to generating simple, short text, it can serve as an execution model for repeating clearly defined tasks at scale.\u003c/p\u003e\n\u003cp\u003eRepresentative applications include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClassifying documents and customer inquiries at scale\u003c/li\u003e\n\u003cli\u003eExtracting structured data\u003c/li\u003e\n\u003cli\u003eRepetitive code modifications\u003c/li\u003e\n\u003cli\u003eWriting and running tests\u003c/li\u003e\n\u003cli\u003eGenerating documents under clear rules\u003c/li\u003e\n\u003cli\u003eReviewing content at scale\u003c/li\u003e\n\u003cli\u003eBackground agent automation\u003c/li\u003e\n\u003cli\u003eRepetitive research assistance\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOpenAI claims that Luna delivers performance similar to that of models considered state-of-the-art a year ago at approximately 6% of the estimated cost per task and nearly 9 times the speed. Here, 6% is not a direct comparison of token prices, but a comparison of the estimated cost required to achieve the same task result.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#features-of-sol-fast-mode\" class=\"anchor\" id=\"features-of-sol-fast-mode\"\u003e\u003c/a\u003eFeatures of Sol Fast Mode\u003c/h2\u003e\n\u003cp\u003eFast mode for the API has been introduced for GPT-5.6 Sol. It replaces the existing Priority Processing and corresponds to Codex’s \u003ccode\u003e/fast\u003c/code\u003e feature.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eItem\u003c/th\u003e\n\u003cth\u003eFast mode\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eSpeed\u003c/td\u003e\n\u003ctd data-label=\"Fast mode\"\u003eUp to 2.5 times faster than Standard processing\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eModel intelligence\u003c/td\u003e\n\u003ctd data-label=\"Fast mode\"\u003eThe same as Standard, according to OpenAI’s announcement\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003ePrice\u003c/td\u003e\n\u003ctd data-label=\"Fast mode\"\u003eTwice the price of Standard processing\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eBackward compatibility\u003c/td\u003e\n\u003ctd data-label=\"Fast mode\"\u003eRequests with the \u003ccode\u003epriority\u003c/code\u003e tag are automatically processed in Fast mode\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThe phrase “up to 2.5 times faster” does not guarantee that latency for every request will decrease by exactly the same ratio. Actual perceived speed may vary depending on prompt length, output length, service load, and the number of tool calls.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#when-fast-mode-is-suitable\" class=\"anchor\" id=\"when-fast-mode-is-suitable\"\u003e\u003c/a\u003eWhen Fast Mode Is Suitable\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eReal-time services where users wait for responses\u003c/li\u003e\n\u003cli\u003eDevelopment environments that rapidly iterate on code changes and verification\u003c/li\u003e\n\u003cli\u003eTasks where Sol calls determine the latency of the entire agent\u003c/li\u003e\n\u003cli\u003eSituations where even a delay of a few minutes matters, such as incident response or outage analysis\u003c/li\u003e\n\u003cli\u003eWork where the cost caused by processing delays exceeds the additional API cost\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStandard processing may be more economical for tasks without urgent completion deadlines, such as background batches, overnight analysis, and asynchronous processing.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#outcome-oriented-model-selection-criteria\" class=\"anchor\" id=\"outcome-oriented-model-selection-criteria\"\u003e\u003c/a\u003eOutcome-Oriented Model Selection Criteria\u003c/h2\u003e\n\u003cp\u003eModel selection is not a matter of choosing the model at the top of a leaderboard. The following questions should be reviewed for each task.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDecision Factor\u003c/th\u003e\n\u003cth\u003eQuestion to Ask\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision Factor\"\u003eImpact of failure\u003c/td\u003e\n\u003ctd data-label=\"Question to Ask\"\u003eHow would an error affect customers, revenue, security, or regulatory compliance?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision Factor\"\u003eError tolerance\u003c/td\u003e\n\u003ctd data-label=\"Question to Ask\"\u003eCan a human reviewer or automated rule catch errors?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision Factor\"\u003eLatency\u003c/td\u003e\n\u003ctd data-label=\"Question to Ask\"\u003eIs a user waiting in real time, or can the task be processed asynchronously?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision Factor\"\u003eThroughput\u003c/td\u003e\n\u003ctd data-label=\"Question to Ask\"\u003eHow many cases must be processed per day or month?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision Factor\"\u003eProblem clarity\u003c/td\u003e\n\u003ctd data-label=\"Question to Ask\"\u003eAre the input, rules, and expected output sufficiently defined?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision Factor\"\u003eValue of reasoning\u003c/td\u003e\n\u003ctd data-label=\"Question to Ask\"\u003eDoes stronger reasoning meaningfully improve the quality of the actual result?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision Factor\"\u003eVerifiability\u003c/td\u003e\n\u003ctd data-label=\"Question to Ask\"\u003eCan the result be evaluated through tests, schemas, or cross-checking?\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eClear tasks that can be verified automatically are likely to be suitable for Luna. Terra can be considered when a consistent level of judgment is needed. Sol is suitable for resolving ambiguity, making high-risk judgments, or conducting final reviews of failure outcomes.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#planning-with-sol-and-executing-with-luna\" class=\"anchor\" id=\"planning-with-sol-and-executing-with-luna\"\u003e\u003c/a\u003ePlanning With Sol and Executing With Luna\u003c/h2\u003e\n\u003cp\u003eDifferent models can be assigned to different stages even within a single agent task. For example, a coding agent can be structured as follows.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSol identifies ambiguities in the requirements and organizes questions.\u003c/li\u003e\n\u003cli\u003eSol determines the implementation plan, scope of changes, and risk factors.\u003c/li\u003e\n\u003cli\u003eLuna implements the clarified changes in code.\u003c/li\u003e\n\u003cli\u003eLuna writes and runs tests.\u003c/li\u003e\n\u003cli\u003eLuna or Terra evaluates the test results and code differences.\u003c/li\u003e\n\u003cli\u003eSol reviews only conclusions that are important or have a high likelihood of failure.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis structure can reduce costs compared with using Sol at every stage while concentrating strong reasoning capabilities on important judgments. However, splitting work across models requires separate designs for routing rules, error handling, log tracing, and evaluation systems.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#three-efficiency-layers-behind-the-price-reductions\" class=\"anchor\" id=\"three-efficiency-layers-behind-the-price-reductions\"\u003e\u003c/a\u003eThree Efficiency Layers Behind the Price Reductions\u003c/h2\u003e\n\u003cp\u003eOpenAI explains that the cost reductions resulted not merely from pricing policy, but from technical improvements across three layers.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eToken efficiency of the model itself\u003c/li\u003e\n\u003cli\u003eHardware efficiency of the inference system\u003c/li\u003e\n\u003cli\u003eEfficiency of the agent harness connecting models, tools, and context\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis explanation is based on technical materials released by OpenAI, and the full details of the cost structure have not been disclosed publicly. It is therefore difficult for outsiders to determine how much of the price reduction came from technical efficiency versus strategic pricing policy.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#model-and-inference-system-optimization\" class=\"anchor\" id=\"model-and-inference-system-optimization\"\u003e\u003c/a\u003eModel and Inference System Optimization\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#improving-work-per-token\" class=\"anchor\" id=\"improving-work-per-token\"\u003e\u003c/a\u003eImproving Work Per Token\u003c/h3\u003e\n\u003cp\u003eAccording to OpenAI, GPT-5.6 was trained to optimize not only task success rates but also processing efficiency. This means it was designed to reduce unnecessarily long reasoning and repetition and reach the required result with fewer tokens, increasing intelligence and work performed per token.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#load-balancing-and-request-routing\" class=\"anchor\" id=\"load-balancing-and-request-routing\"\u003e\u003c/a\u003eLoad Balancing and Request Routing\u003c/h3\u003e\n\u003cp\u003eThe inference system distributes requests across data centers and clusters based on region, available capacity, and accelerator type. Within a cluster, it selects model instances by considering factors such as current load, input context length, cache availability, and request characteristics.\u003c/p\u003e\n\u003cp\u003eOpenAI stated that it used GPT-5.6 Sol and Codex to analyze production traffic, investigate the causes of load imbalances, test routing strategies, and adjust heuristics.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#gpu-kernel-optimization\" class=\"anchor\" id=\"gpu-kernel-optimization\"\u003e\u003c/a\u003eGPU Kernel Optimization\u003c/h3\u003e\n\u003cp\u003eGPU kernels are the core code that executes a model’s mathematical operations on hardware. Processing costs can vary even on the same GPU depending on memory movement, synchronization, data layout, and parallelization methods.\u003c/p\u003e\n\u003cp\u003eAccording to OpenAI, GPT-5.6 Sol participated in writing and optimizing production kernels using Triton and Gluon within the Codex environment. The company stated that kernel improvements and related optimizations reduced the end-to-end cost of serving the model by 20%.\u003c/p\u003e\n\u003cp\u003eEnd-to-end cost refers not to a single operation, but to the cost of processing an actual request in its entirety, including routing, data movement, model execution, and output generation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#verifying-kernel-accuracy\" class=\"anchor\" id=\"verifying-kernel-accuracy\"\u003e\u003c/a\u003eVerifying Kernel Accuracy\u003c/h3\u003e\n\u003cp\u003eEven a fast kernel cannot be used if it produces numerically incorrect results. OpenAI explained that it invested in verification tools, including FpSan, to check the accuracy of AI-written kernels. FpSan stands for Floating-Point Sanitizer and is an open-source tool that detects errors related to floating-point operations.\u003c/p\u003e\n\u003cp\u003eThis demonstrates that when AI generates production infrastructure code, numerical verification, regression testing, and failure recovery procedures are required alongside performance benchmarks.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#speculative-decoding-and-kv-cache\" class=\"anchor\" id=\"speculative-decoding-and-kv-cache\"\u003e\u003c/a\u003eSpeculative Decoding and KV Cache\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#speculative-decoding\" class=\"anchor\" id=\"speculative-decoding\"\u003e\u003c/a\u003eSpeculative Decoding\u003c/h3\u003e\n\u003cp\u003eSpeculative decoding is a method in which a small draft model first proposes upcoming tokens and a larger primary model verifies multiple candidates in parallel. If the proposals are accepted, the number of expensive sequential computations performed by the primary model can be reduced.\u003c/p\u003e\n\u003cp\u003eOpenAI stated that GPT-5.6 Sol designed and ran hundreds of experiments that varied the size and architecture of the draft model and monitored training. The company explained that this improved token generation efficiency by at least 15%.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#kv-cache-and-workload-specific-configurations\" class=\"anchor\" id=\"kv-cache-and-workload-specific-configurations\"\u003e\u003c/a\u003eKV Cache and Workload-Specific Configurations\u003c/h3\u003e\n\u003cp\u003eAs a model processes input, it creates a Key-Value cache, or KV cache. The optimal configuration varies according to input and output length, batch size, cache hit rate, the number of concurrent requests, memory capacity, and model sharding method.\u003c/p\u003e\n\u003cp\u003eOpenAI explains that it used Sol and Codex to analyze actual workloads, evaluate candidate configurations, and finely tune engine configurations for each type of task. The goal is to process more requests on the same hardware.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#agent-harnesses-and-context-costs\" class=\"anchor\" id=\"agent-harnesses-and-context-costs\"\u003e\u003c/a\u003eAgent Harnesses and Context Costs\u003c/h2\u003e\n\u003cp\u003eAn agent calls models and tools multiple times to resolve a single user request. If a task requires 30 model calls and each call incurs 1 second of unnecessary delay, the total latency can increase by approximately 30 seconds.\u003c/p\u003e\n\u003cp\u003eOpenAI describes the Rust-based orchestration layer connecting models, tools, and user environments as an agent harness.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#deferring-exposure-of-tools-until-needed\" class=\"anchor\" id=\"deferring-exposure-of-tools-until-needed\"\u003e\u003c/a\u003eDeferring Exposure of Tools Until Needed\u003c/h3\u003e\n\u003cp\u003eIncluding information about every tool, plugin, skill, and MCP integration in the prompt from the outset increases input tokens and latency. The harness uses deferred discovery, exposing relevant tool information only when needed. OpenAI stated that tool output is limited to 10,000 tokens by default unless the model requests a separate limit.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#exact-prefix-preservation-and-prompt-caching\" class=\"anchor\" id=\"exact-prefix-preservation-and-prompt-caching\"\u003e\u003c/a\u003eExact Prefix Preservation and Prompt Caching\u003c/h3\u003e\n\u003cp\u003ePrompt caching reduces redundant computation by reusing an identical prefix from previously processed input. To reuse the cache, the prompt prefix must match exactly.\u003c/p\u003e\n\u003cp\u003eOpenAI’s harness manages history in an append-only structure, adding new messages and tool results at the end. Tools are presented in a deterministic order, while execution settings such as approval policies are applied at runtime without changing the tool definitions themselves. This approach helps increase cache hit rates in repetitive agent loops.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-to-interpret-enterprise-case-study-figures\" class=\"anchor\" id=\"how-to-interpret-enterprise-case-study-figures\"\u003e\u003c/a\u003eHow to Interpret Enterprise Case Study Figures\u003c/h2\u003e\n\u003cp\u003eOpenAI’s official announcement included evaluations from Replit, Notion, Ramp, Blitzy, Cognition, Dust, and others.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNotion stated that in its own evaluation, Terra delivered quality similar to GPT-5.5 at half the cost per task and in 60% less time.\u003c/li\u003e\n\u003cli\u003eBlitzy explained that after adopting Luna, its prompt cache reuse rate increased from 24% to 90%, while costs were 87% lower than with its previous default model.\u003c/li\u003e\n\u003cli\u003eDust stated that for the same agent work, Luna was 40% faster and 40% less expensive than its previous default model.\u003c/li\u003e\n\u003cli\u003eRamp stated that it uses Luna as the default model for background agent automation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese figures are case studies measured using each company’s internal work, prompts, evaluation criteria, and system architecture. Because they are not independent common benchmarks, they should not be applied directly to the work of other organizations. Before adoption, organizations need their own evaluations reflecting actual data and the cost of errors.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#pre-adoption-validation-checklist\" class=\"anchor\" id=\"pre-adoption-validation-checklist\"\u003e\u003c/a\u003ePre-Adoption Validation Checklist\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003ePrepare a representative sample of tasks and correct answers or evaluation criteria.\u003c/li\u003e\n\u003cli\u003eCompare the success rates and retry rates of Luna, Terra, and Sol under the same conditions.\u003c/li\u003e\n\u003cli\u003eInclude not only token costs but also tool calls, review personnel, and failure recovery costs.\u003c/li\u003e\n\u003cli\u003eMeasure 95th- or 99th-percentile latency in addition to average latency.\u003c/li\u003e\n\u003cli\u003eClassify stages that support automated testing or schema validation as candidates for lower-cost models.\u003c/li\u003e\n\u003cli\u003eApply separate approval and logging policies to work involving personal data, security, or regulatory matters.\u003c/li\u003e\n\u003cli\u003eDefine routing criteria for escalating low-confidence results to Terra or Sol.\u003c/li\u003e\n\u003cli\u003eUse actual traffic to verify whether the value of reduced latency exceeds the additional cost of Fast mode.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca href=\"#significance-and-limitations\" class=\"anchor\" id=\"significance-and-limitations\"\u003e\u003c/a\u003eSignificance and Limitations\u003c/h2\u003e\n\u003cp\u003eThis restructuring shows that the basis of AI model competition is shifting from a single top score to cost per result. By assigning Luna to large-scale repetitive work, Terra to everyday knowledge work, and Sol to ambiguous and important judgments, organizations can combine intelligence, speed, and cost according to each task.\u003c/p\u003e\n\u003cp\u003eHowever, publicly available information does not make it possible to separate how much of the 80% price reduction came from improvements in technical efficiency and how much came from market strategy. The economics of lower-cost models are also not determined by token prices alone. If higher error rates lead to more retries and human review, the total cost of the work may increase.\u003c/p\u003e\n\u003cp\u003eTherefore, the key metric is not the price of a single model call, but the total cost of producing one result that passes validation. Success rates by model, the number of retries, latency, and review costs must be measured together to determine whether GPT-5.6’s price reductions translate into actual business value.\u003c/p\u003e\n","tags":["OpenAI","GPT-5.6","API Pricing","Reasoning Efficiency","Agent"],"faqs":[{"question":"What are the updated API prices for GPT-5.6 Luna?","answer":"As of July 30, 2026, Luna costs $0.20 per 1 million input tokens and $1.20 per 1 million output tokens. This represents an 80% reduction from the previous prices announced by OpenAI."},{"question":"What are the updated API prices for GPT-5.6 Terra?","answer":"Terra costs $2 per 1 million input tokens and $12 per 1 million output tokens. OpenAI announced a 20% price reduction."},{"question":"Has the price of GPT-5.6 Sol also been reduced?","answer":"No. In this announcement by OpenAI, the price of Standard processing for Sol remains unchanged. Instead, Fast mode has been added, which is up to 2.5 times faster than Standard and costs twice as much."},{"question":"Does using Fast mode reduce the quality of the model's responses?","answer":"OpenAI explains that Fast mode increases processing speed without reducing Sol's level of intelligence. However, “up to 2.5 times faster” indicates an upper limit, and actual latency may vary depending on request length, output volume, tool calls, and system load."},{"question":"Do existing Priority Processing requests need to be modified?","answer":"According to OpenAI's announcement, the `priority` tag in existing API requests will continue to work and will automatically route requests to Fast mode processing. In production environments, it is advisable to separately verify when this takes effect and the actual billing details."},{"question":"What types of tasks is Luna suitable for?","answer":"It is suitable for high-volume, repetitive tasks with clear rules and expected outputs that can be automatically verified. Typical examples include document classification, data extraction, repetitive code modifications, test execution, content review, and background automation."},{"question":"Which model should I choose, Terra or Luna?","answer":"If low cost and high throughput are the priorities and the task is well-defined, you can evaluate Luna first. If more contextual understanding and judgment are needed but advanced reasoning at Sol's level is not required, Terra may be suitable."},{"question":"Shouldn't I use Sol for every agent step?","answer":"You can, but it may be less cost-effective. Separating responsibilities so that Sol handles planning and high-risk decisions while Luna or Terra handles well-defined execution and testing may reduce overall costs while maintaining quality at critical stages."},{"question":"Is Luna's 6% task cost a comparison of per-token prices?","answer":"No. The approximately 6% cited by OpenAI is the estimated cost per task required to achieve results comparable to the comparison target. It is not a figure calculated simply by directly comparing per-token prices and should be understood as a comparison that incorporates evaluation tasks and success rates."},{"question":"How should the actual cost-effectiveness of API models be evaluated?","answer":"It should account not only for token prices but also for success rates, the number of retries, tool call costs, response latency, human review time, and error recovery costs. The most useful metric is the total cost per result that passes validation."}],"sources":[{"url":"https://openai.com/index/advancing-the-price-performance-frontier-with-gpt-5-6/","title":"Advancing the price-performance frontier with GPT-5.6 | OpenAI","type":"source"},{"url":"https://openai.com/index/gpt-5-6-frontier-intelligence-efficiency/","title":"How GPT-5.6 fuses frontier intelligence with frontier efficiency | OpenAI","type":"source"}],"images":[{"id":395,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NDY2OSwicHVyIjoiYmxvYl9pZCJ9fQ==--5c50f46b468b0057808eb3e6351730cbce2f4b78/ai-4f6037ef.webp","is_representative":true,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"퍼즐, AI 칩 계층, 작업 컨베이어, 감소하는 동전 더미로 표현한 모델 선택 흐름","caption":"AI 모델의 작업 분배와 비용 절감, 검증 과정을 시각화한 개념도다.","description":null},"en":{"alt":"Model selection flow with puzzles, layered AI chips, a task conveyor, and shrinking coin stacks","caption":"The diagram visualizes AI task routing, cost reduction, and output verification.","description":null},"ja":{"alt":"パズル、階層化AIチップ、タスク用コンベア、減っていくコインで示すモデル選択フロー","caption":"AIモデルのタスク振り分け、コスト削減、検証の流れを表した概念図。","description":null},"es":{"alt":"Flujo de selección de modelos con piezas, chips de IA, cinta de tareas y pilas de monedas decrecientes","caption":"El diagrama representa la asignación de tareas, la reducción de costes y la verificación con IA.","description":null},"id":{"alt":"Alur pemilihan model dengan puzzle, chip AI bertingkat, konveyor tugas, dan tumpukan koin yang menyusut","caption":"Diagram ini menggambarkan perutean tugas AI, penghematan biaya, dan verifikasi hasil.","description":null},"pt":{"alt":"Fluxo de seleção de modelos com peças, chips de IA, esteira de tarefas e pilhas de moedas decrescentes","caption":"O diagrama mostra o roteamento de tarefas, a redução de custos e a verificação por IA.","description":null},"zh-hant":{"alt":"以拼圖、分層 AI 晶片、任務輸送帶與遞減硬幣堆呈現模型選擇流程","caption":"此概念圖呈現 AI 任務分流、成本降低與結果驗證流程。","description":null},"de":{"alt":"Modellauswahl mit Puzzleteilen, gestaffelten KI-Chips, Aufgabenband und schrumpfenden Münzstapeln","caption":"Die Grafik veranschaulicht KI-Aufgabenverteilung, Kostensenkung und Ergebnisprüfung.","description":null}}},{"id":396,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NDY3NSwicHVyIjoiYmxvYl9pZCJ9fQ==--920476799a53738205d619a3a057685b5af9d05d/ai-334533bc.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"AI 모델별 처리 흐름과 속도, 비용, 하드웨어 구성을 비교한 인포그래픽","caption":"세 가지 AI 처리 경로의 성능과 비용 차이 및 연결된 인프라를 시각화한다.","description":null},"en":{"alt":"Infographic comparing AI model processing flows, speed, cost, and hardware infrastructure","caption":"Three AI processing paths visualize differences in performance, cost, and connected infrastructure.","description":null},"ja":{"alt":"AIモデル別の処理フロー、速度、コスト、ハードウェア構成を比較する図","caption":"3つのAI処理経路について、性能とコスト、接続インフラの違いを可視化している。","description":null},"es":{"alt":"Infografía comparativa de flujos, velocidad, coste e infraestructura de modelos de IA","caption":"Tres rutas de procesamiento de IA muestran diferencias de rendimiento, coste e infraestructura conectada.","description":null},"id":{"alt":"Infografik perbandingan alur, kecepatan, biaya, dan infrastruktur perangkat keras model AI","caption":"Tiga jalur pemrosesan AI memperlihatkan perbedaan kinerja, biaya, dan infrastruktur yang terhubung.","description":null},"pt":{"alt":"Infográfico comparando fluxos, velocidade, custo e infraestrutura de modelos de IA","caption":"Três rotas de processamento de IA mostram diferenças de desempenho, custo e infraestrutura conectada.","description":null},"zh-hant":{"alt":"比較各種 AI 模型處理流程、速度、成本與硬體架構的資訊圖表","caption":"三條 AI 處理路徑呈現效能、成本與連接基礎設施的差異。","description":null},"de":{"alt":"Infografik zum Vergleich von KI-Modellabläufen, Geschwindigkeit, Kosten und Hardware","caption":"Drei KI-Verarbeitungspfade zeigen Unterschiede bei Leistung, Kosten und verbundener Infrastruktur.","description":null}}}],"published_at":"2026-08-01T07:15:42+09:00","updated_at":"2026-08-01T07:15:42+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant","de"],"url":"https://injoys.com/en/articles/openai-gpt-5-6-price-performance-model-routing"}