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20% OffAccess Top Models at Low CostUnified AI API for OpenAI, Claude, Gemini, and moreGet Free API KeyAClaude Opus 4.6Input:$4/MOutput:$20/MAClaude Sonnet 4.6Input:$2.4/MOutput:$12/MOGPT-5.4 nanoInput:$0.16/MOutput:$1/MOGPT-5.4 miniInput:$0.6/MOutput:$3.6/MGNano Banana 2Input:$0.4/MOutput:$2.4/MDDoubao Seedream 5Per Request:$0.028FFLUX 2 MAXPer Request:$0.008XBlack Forest Labs/FLUX 2 MAXPer Request:$0.056OSora 2 ProPer Second:$0.24OSora 2Per Second:$0.08Mmj_fast_videoMmj_fast_videoPer Request:$0.6XGrok Imagine VideoPer Second:$0.04Ogpt-realtime-1.5Input:$3.2/MOutput:$12.8/MOgpt-audio-1.5Input:$2/MOutput:$8/MOWhisper-1Input:$24/MOutput:$24/MOTTSInput:$12/MOutput:$12/MXmimo-v2-proInput:$0.8/MOutput:$2.4/MXmimo-v2-omniInput:$0.32/MOutput:$1.6/MXmimo-v2-flashInput:$0.08/MOutput:$0.24/MMMiniMax-M2.7Input:$0.24/MOutput:$0.96/MAll Multimodal AI ModelsSee MoreIntegrate once and swap engines effortlessly. Provides an out-of-the-box SDK, interactive Playground, Postman collection, and sample projects. Built-in comparative testing, response visualization, and usage analysis allow engineers to prototype within hours and select the optimal model combination through comparison.Model GroupsSee MoreDiscover models from OpenAI, Anthropic, Google, Aliyun, xAI, Deepseek, and more. Each provider group features unique strengths — from advanced reasoning and code generation to multimodal understanding and real-time inference. Find the right model for your project.Simple IntegrationConnect CometAPI to your stack in minutes — lightweight SDKs, clear docs, and example code to get your first API call working fast.Most Popular ModelsSee MoreExplore our most popular models, trusted by developers for performance, reliability, and ease of integration across real-world applications.GGemini 3.1 Flash-LiteInput:$0.2/MOutput:$1.2/MGemini 3.1 Flash-Lite is a highly cost-efficient and low-latency Tier-3 model in Google’s Gemini 3 series, designed for high-volume production AI workflow where throughput and speed matter more than maximal reasoning depth. It combines a large multimodal context window with efficient inference performance at a lower cost than most flagship counterparts.DDeepSeek-V3DDeepSeek-V3Input:$0.216/MOutput:$0.88/MThe most popular and cost-effective DeepSeek-V3 model. 671B full-blood version. This model supports a maximum context length of 64,000 tokens.Otext-embedding-3-smallOtext-embedding-3-smallInput:$0.016/MOutput:$0.016/MA small text embedding model for efficient processing.XGrok 4XGrok 4Input:$2.4/MOutput:$12/MGrok 4 is an artificial intelligence model provided by XAI. Currently supports text modality, with vision, image generation, and other features coming soon. Possesses extremely powerful technical parameters and ecosystem capabilities: Context Window: Supports context processing of up to 256,000 tokens, leading mainstream models.Oomni-moderation-latestOomni-moderation-latestPer Request:$0.0016FeaturesWhy choose CometAPI for your AI integration needsUsage AnalyticsDetailed insights into your API usage patterns and performance metrics.Pay-as-you-goFlexible pricing model that scales with your usage and budget.PrivacyEnterprise-grade security and privacy protection for your data.What Our Users SayHear from developers and teams who trust CometAPI — real feedback on reliability, ease of integration, performance, and support.FAQFind concise answers to common questions about CometAPI — from API documentation and authentication to pricing, integration steps, and troubleshooting tips.What is CometAPI and how does it work?Does CometAPI offer a free trial for new users?Is there API documentation available for CometAPI?Why is CometAPI so affordable?How do you ensure data privacy? --- SchemaAClaude Opus 4.6Input:$4/MOutput:$20/MClaude Opus 4.6 is Anthropic’s “Opus”-class large language model, released February 2026. It is positioned as a workhorse for knowledge-work and research workflows — improving long-context reasoning, multi-step planning, tool use (including agentic software workflows), and computer-use tasks such as automated slide and spreadsheet generation.NewCommercial UsePlaygroundOverviewFeaturesPricingAPIVersions ItemClaude Opus 4.6 (public specs)Model familyClaude Opus (Opus 4.6)Model idclaude-opus-4-6ProviderAnthropicInput typesText, structured files (documents, spreadsheets), vision inputs (images/screenshots) — via Messages API and Files APIOutput typesText (long-form, code, structured outputs), streamed messages; supports document and structured outputsContext window1,000,000 tokens (beta); compaction available to extend effective contextMax output tokens128,000Effort / reasoning controlslow, medium, high (default), max; plus adaptive thinking to let the model pick extended reasoning when usefulLong-context featuresContext compaction (beta) with header compact-2026-01-12; streaming recommended for large outputs What is Claude Opus 4.6 Claude Opus 4.6 is Anthropic’s most capable Opus-class model (released Feb 5, 2026), tuned for complex, long-horizon knowledge-work and agentic coding workflows. It emphasizes reliable planning, sustained multi-step execution, and robustness on large codebases and enterprise tasks such as financial analysis, legal reasoning, and multi-document research. Main features of Claude Opus 4.6 Ultra-long context (beta): Support for a 1,000,000-token context window (beta) and server-side context compaction to summarize and preserve long-running conversational state. Very large outputs: Up to 128k output tokens, enabling single-request generation of large documents, reports, or multi-file code patches. Agentic workflows & agent teams: Research-preview support in Claude Code for spinning up multiple agents that coordinate in parallel for review, testing, and multi-step engineering tasks. Effort & adaptive thinking controls: Four effort levels (low/medium/high/max) and adaptive thinking that lets the model selectively apply extended reasoning when appropriate. Improved coding and tool use: Upgrades for code review, debugging, terminal-style agent workflows, and integrations with office tools (Claude in Excel / PowerPoint). Context compaction & long-run tooling: Server-side compaction strategy and SDK support (streaming) to manage long-running agents without manual state pruning. Benchmark performance of Claude Opus 4.6 Anthropic reports leading performance on multiple evaluations: highest scores on Terminal-Bench 2.0 (agentic coding), top industry result on DeepSearchQA, a 144 Elo advantage over OpenAI’s GPT-5.2 on GDPval-AA, and a BigLaw Bench score of 90.2% (per Anthropic’s release and system card). See official system card and release notes for methodology and caveats. Benchmark / taskOpus 4.6 reported result (Anthropic)Terminal-Bench 2.0 (coding)65.4% (industry-leading per Anthropic).OSWorld (computer-using tasks)72.7% (Anthropic’s best computer-using model).GDPVal / Elo (adaptive thinking, partner benchmarks)Anthropic / partner prelaunch results report high Elo relative to other frontier models (vendor materials show Opus 4.6 leading on several agentic and coding benchmarks). Independent press summaries note Opus 4.6 outperforming GPT-5.2 on specific professional evaluations. Note: Benchmarks and reported comparisons are Anthropic’s published results from the Feb 5, 2026 announcement and system card; users should consult the system card for test methodology and dataset details. Claude Opus 4.6 vs Opus 4.5 vs GPT-5.2 (comparative snapshot) ModelStrengthsNotesClaude Opus 4.6Best-in-class long-horizon reasoning, agentic coding, 1M-token beta, 128k outputsAnthropic reports gains over Opus 4.5 and OpenAI GPT-5.2 on GDPval-AA and Terminal-Bench.Claude Opus 4.5Strong coding and agentic capabilities (previous baseline)4.6 improves planning and long-run stability.OpenAI GPT-5.2 (reference)Strong multimodal reasoning and broad deploymentAnthropic cites a ~144 Elo gap (GDPval-AA) in favor of Opus 4.6; comparisons vary by task and benchmark. Representative enterprise use cases Repository-scale code refactors, multi-file migrations, and automated patch generation. Long-running agentic workflows: automated research, multi-document synthesis, and CI orchestration. Financial analysis and legal research that require multi-source cross-checking and structured outputs. Document → slide workflows: ingest large spreadsheets, synthesize findings, and generate brand-consistent PowerPoint decks. Defensive security workflows: triage and reproduce vulnerabilities under controlled, audited conditions. How to access and use Claude Opus 4.6 API Step 1: Sign Up for API Key Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit. Step 2: Send Requests to claude-opus-4-6 API Select the “claude-opus-4-6” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace with your actual CometAPI key from your account. Where to call it:  Anthropic Messages format and Chat format. Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer. Step 3: Retrieve and Verify Results Process the API response to get the generated answer. After processing, the API responds with the task status and output data.FAQWhat distinguishes Claude Opus 4.6’s reasoning approach from earlier Claude models?Claude Opus 4.6 introduces adaptive thinking across multiple effort levels, allowing it to dynamically balance depth and speed of reasoning, improving performance on complex, multi-step problems compared with prior extended thinking modes. :contentReference[oaicite:1]{index=1}What is the maximum output length and do I need to use streaming?Opus 4.6 supports up to 128,000 output tokens; SDKs and the platform recommend streaming (e.g., .stream() / streaming messages) for large max_tokens to avoid HTTP timeouts.What are 'effort' and 'adaptive thinking' controls and when should I change them?Opus 4.6 supports four effort levels — low, medium, high (default), and max — and an adaptive thinking mode that lets the model select when to apply extended reasoning; lower effort reduces 'overthinking' and cost while higher effort favors deeper reasoning and accuracy.How does Opus 4.6 compare to Opus 4.5 and to OpenAI's GPT-5.2 on professional tasks?Anthropic reports Opus 4.6 outperforms Opus 4.5 and cites a ~144 Elo advantage over OpenAI's GPT-5.2 on GDPval-AA, plus top results on Terminal-Bench 2.0 and DeepSearchQA; see the system card for benchmark methodologies and caveats.Is Claude Opus 4.6 suitable for agentic workflows and what are 'agent teams'?Yes — Opus 4.6 is designed for sustained agentic tasks and Claude Code supports 'agent teams' (research preview) that run multiple subagents in parallel to coordinate on large, split tasks like codebase reviews.What safety measures has Anthropic added to Opus 4.6 given its stronger coding and cybersecurity abilities?Anthropic reports extensive safety testing and six new cybersecurity probes targeted at misuse, plus ongoing system-card evaluations to monitor misaligned behavior and update safeguards.Which file types and office workflows does Opus 4.6 support (e.g., Excel, PowerPoint)?Opus 4.6 integrates with office workflows: Anthropic improved Claude in Excel for complex data tasks and released Claude in PowerPoint as a research preview; it also accepts documents, spreadsheets, and vision inputs where supported.How do I use context compaction in the Messages API for long-running conversations?Enable the compaction beta by including the compact-2026-01-12 beta header and add a compact_20260112 edit in context_management.edits; compaction summarizes older context automatically to extend effective session length.Features for Claude Opus 4.6Explore the key features of Claude Opus 4.6, designed to enhance performance and usability. Discover how these capabilities can benefit your projects and improve user experience.Pricing for Claude Opus 4.6Explore competitive pricing for Claude Opus 4.6, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how Claude Opus 4.6 can enhance your projects while keeping costs manageable.Comet Price (USD / M Tokens)Official Price (USD / M Tokens)DiscountInput:$4/MOutput:$20/MInput:$5/MOutput:$25/M-20%Sample code and API for Claude Opus 4.6Access comprehensive sample code and API resources for Claude Opus 4.6 to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of Claude Opus 4.6 in your projects.CopyPythonJavaScriptCurlimport anthropic import os # Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "" BASE_URL = "https://api.cometapi.com" client = anthropic.Anthropic( base_url=BASE_URL, api_key=COMETAPI_KEY, ) message = client.messages.create( model="claude-opus-4-6", max_tokens=1024, messages=[{"role": "user", "content": "Hello, Claude"}], ) print(message.content[0].text)Versions of Claude Opus 4.6The reason Claude Opus 4.6 has multiple snapshots may include potential factors such as variations in output after updates requiring older snapshots for consistency, providing developers a transition period for adaptation and migration, and different snapshots corresponding to global or regional endpoints to optimize user experience. For detailed differences between versions, please refer to the official documentation. versionclaude-opus-4-6More Models --- SchemaAClaude Sonnet 4.6Input:$2.4/MOutput:$12/MClaude Sonnet 4.6 is our most capable Sonnet model yet. It’s a full upgrade of the model’s skills across coding, computer use, long-context reasoning, agent planning, knowledge work, and design. Sonnet 4.6 also features a 1M token context window in beta.NewCommercial UsePlaygroundOverviewFeaturesPricingAPIVersionsTechnical specifications — Claude Sonnet 4.6 ItemClaude Sonnet 4.6 (public summary)ProviderAnthropicModel familySonnet (Claude v4.x family) — Sonnet 4.6 variantModel id (canonical)claude-sonnet-4-6Input typesText (primary). Limited/secondary support for structured tool/JSON I/O. Not positioned as a primary image-generation model.Output typesText (natural language, structured JSON, code, and tool-call payloads)Context window~200,000 tokens (approx.) — designed for multi-document and long-session coherenceFunction-calling / tool useYes — structured tool invocation, JSON-constrained outputs, agent-style orchestration supportedMultimodalityLimited — Sonnet is focused on text and structured tool integration; not optimized for image generation.Release note highlightsStability/improvements in long-context reasoning, lower-latency Sonnet variant tuned for speed–accuracy tradeoffs, improved instruction adherence. What Is Claude Sonnet 4.6 Claude Sonnet 4.6 is the latest evolution of Anthropic’s Sonnet model line, designed to deliver near-Opus performance at a more accessible price point. It upgrades Sonnet from its earlier 4.5 iteration, bringing stronger instruction following, vastly expanded context support, improved coding and computer use skills, and broader multi-step reasoning abilities — all while maintaining pricing parity with Sonnet 4.5. Unlike Opus models, which are flagship and optimized for heavy agentic workloads, Sonnet 4.6 targets developers and general knowledge work where broad capability and cost-effectiveness matter. Main Features of Claude Sonnet 4.6 1M Token Context Window (Beta): Sonnet 4.6 supports up to one million tokens of context in beta — roughly enough to ingest entire codebases, stacks of legal contracts, or multiple academic papers in a single request. Improved Coding Performance: Compared with Sonnet 4.5, Sonnet 4.6 shows significant gains in real-world developer tasks and benchmarks like SWE-Bench Verified (~79.6% score reported), making it suitable for complex coding tasks. Enhanced Computer Use: New levels of competency in tasks involving operating software (spreadsheets, multi-step web form workflows, etc.) approaching human-level performance on OSWorld-Verified tests. Adaptive Thinking: The model incorporates enhanced reasoning strategies and can dynamically allocate internal computation to tackle complex problems step by step. Stronger Instruction Following: Users report more consistency and precision in following detailed requests, with fewer hallucinations and better task completion. Safety & Prompt Injection Resistance: Anthropic has improved robustness over Sonnet 4.5 in resisting prompt injection attacks and similar vulnerabilities. Benchmark Performance of Claude Sonnet 4.6 EvaluationClaude Sonnet 4.6 (approx.)NotesSWE-Bench Verified~79.6%Strong coding performance close to Opus-class.OSWorld-Verified (Computer Use)~72.5%Near human-level task performance; powerful for workflows.ARC-AGI-2~60.4%Reflects broad reasoning strength. As a mid-tier model, Sonnet 4.6 narrows the performance gap with Opus models significantly, making it suitable for many tasks previously reserved for flagship class. Claude Sonnet 4.6 vs Other Claude Models ModelBest ForKey DifferencesClaude Sonnet 4.6Balanced coding, reasoning, large contextsMassive context window beta, cost-efficient, strong for workflow tasks.Claude Sonnet 4.5Mid-tier general tasksLower benchmarks, smaller context window before 4.6.Claude Opus 4.6Deep reasoning & agentic codingStronger raw reasoning and agent capabilities; pricier. Compared to Sonnet 4.5, the 4.6 release boosts contextual understanding and performance on office-style tasks; compared to Opus models, Sonnet sits slightly below in flagship reasoning power but often closer than expected in coding and general task benchmarks. Limitations of Claude Sonnet 4.6 Beta Context Window: The 1M token context is currently in beta — adoption and stability may vary depending on API usage and plan. Latency & Cost: Handling very large contexts increases computational cost and may introduce higher latency on API calls relative to smaller contexts. Benchmark Granularity: While strong in reported tests, Sonnet may lag a bit behind Opus on the most complex reasoning or multidisciplinary benchmarks. Representative Use Cases of Claude Sonnet 4.6 Large Codebase Assistance: Ideal for ingesting and reasoning about entire software systems, refactorings, or cross-file dependencies. Document & Research Synthesis: Useful for long-document analysis where context extends beyond typical limits. Workflow Automation: Solving multi-step computer tasks, such as spreadsheets and form automation. General Knowledge Work: Suitable for knowledge workers needing reliable instruction following and reasoning without the cost of flagship models. How to access and use Claude Sonnet 4.6 API Step 1: Sign Up for API Key Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit. Step 2: Send Requests to claude-sonnet-4-6 API Select the “claude-opus-4-6” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace with your actual CometAPI key from your account. Where to call it:  Anthropic Messages format and Chat format. Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer. Step 3: Retrieve and Verify Results Process the API response to get the generated answer. After processing, the API responds with the task status and output data.FAQHow large is the context window in the Claude Sonnet 4.6 API?Claude Sonnet 4.6 supports a 1,000,000-token context window in beta, allowing developers to process entire codebases, contracts, or research datasets within a single request.How does Claude Sonnet 4.6 compare with Claude Opus 4.6?Sonnet 4.6 is a mid-tier model designed to deliver near-Opus performance at lower cost, while Opus 4.6 remains Anthropic’s flagship model for the most complex reasoning and research tasks.Can Claude Sonnet 4.6 handle large software engineering projects?Yes. Sonnet 4.6 performs strongly on software engineering benchmarks such as SWE-Bench Verified, achieving around 79.6%, making it well suited for repository-scale coding and debugging.What new capabilities were added in Claude Sonnet 4.6 compared with Sonnet 4.5?Sonnet 4.6 introduces a 1M token context window, improved coding accuracy, better instruction following, and stronger computer-use capabilities across software environments.Is Claude Sonnet 4.6 suitable for agent workflows and automation?Yes. The model supports tool calling, web search, and programmatic workflows, making it effective for building AI agents that perform multi-step tasks.Which platforms support the Claude Sonnet 4.6 API?Claude Sonnet 4.6 is available through CometAPI’s API .Features for Claude Sonnet 4.6Explore the key features of Claude Sonnet 4.6, designed to enhance performance and usability. Discover how these capabilities can benefit your projects and improve user experience.Pricing for Claude Sonnet 4.6Explore competitive pricing for Claude Sonnet 4.6, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how Claude Sonnet 4.6 can enhance your projects while keeping costs manageable.Comet Price (USD / M Tokens)Official Price (USD / M Tokens)DiscountInput:$2.4/MOutput:$12/MInput:$3/MOutput:$15/M-20%Sample code and API for Claude Sonnet 4.6Access comprehensive sample code and API resources for Claude Sonnet 4.6 to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of Claude Sonnet 4.6 in your projects.CopyPythonJavaScriptCurlimport anthropic import os # Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "" BASE_URL = "https://api.cometapi.com" message = anthropic.Anthropic( base_url=BASE_URL, api_key=COMETAPI_KEY, ) messages = message.messages.create( model="claude-sonnet-4-6", max_tokens=1024, messages=[{"role": "user", "content": "Hello, Claude"}], ) print(messages.content[0].text)Versions of Claude Sonnet 4.6The reason Claude Sonnet 4.6 has multiple snapshots may include potential factors such as variations in output after updates requiring older snapshots for consistency, providing developers a transition period for adaptation and migration, and different snapshots corresponding to global or regional endpoints to optimize user experience. For detailed differences between versions, please refer to the official documentation. versionclaude-sonnet-4-6claude-sonnet-4-6-thinkingMore Models --- OGPT-5.4 nanoInput:$0.16/MOutput:$1/MContext:400,000 Max Output:128,000 GPT-5.4 nano is designed for tasks where speed and cost matter most like classification, data extraction, ranking, and sub-agents.NewCommercial UsePlaygroundOverviewFeaturesPricingAPIVersionsTechnical Specifications of GPT-5.4 Nano ItemGPT-5.4 Nano (estimated from official + cross-validation)Model familyGPT-5.4 series (ultra-lightweight “nano” variant)ProviderOpenAIInput typesTextOutput typesTextContext window128,000 – 200,000 tokens (range based on nano tier patterns)Max output tokens32,000 – 64,000 tokens (estimated)Knowledge cutoff~May 31, 2024 (inherited mini/nano lineage)Reasoning supportLimited (optimized for efficiency over depth)Tool supportBasic function calling (limited agent capabilities)PositioningUltra-low-cost, high-throughput inference model What is GPT-5.4 Nano? GPT-5.4 Nano is the smallest and most cost-efficient model in the GPT-5.4 family, designed for massive-scale, low-compute workloads. It prioritizes speed, throughput, and cost efficiency over deep reasoning, making it ideal for simple, repeatable tasks. Unlike GPT-5.4 or GPT-5.4 Mini, Nano is optimized for high-frequency API usage, where millions of requests must be processed quickly and cheaply. Key Features of GPT-5.4 Nano Ultra-low latency inference: Designed for real-time pipelines and high-QPS systems Extreme cost efficiency: Ideal for large-scale deployments (classification, tagging, routing) Lightweight reasoning: Handles simple instructions reliably but not deep chains High throughput optimization: Built for batch processing and parallel workloads Stable structured output: Works well for JSON formatting, extraction, and labeling tasks Pipeline-friendly design: Commonly used as a “worker model” in multi-model architectures Benchmark Performance of GPT-5.4 Nano Not positioned for frontier benchmarks (e.g., SWE-Bench, GPQA) Optimized for: Classification accuracy consistency Structured output reliability Latency benchmarks (substantially faster than Mini/Pro tiers) Typically achieves high precision on narrow tasks but significantly lower performance on reasoning-heavy benchmarks 👉 Key takeaway: GPT-5.4 Nano excels in efficiency benchmarks, not reasoning leaderboards. GPT-5.4-Nano vs Other Models ModelStrengthContext WindowBest Use CaseGPT-5.4Maximum intelligence~1M tokensComplex reasoning, researchGPT-5.4 MiniBalanced performance + speed~400K tokensCoding, agentsGPT-5.4 NanoFastest + cheapest~400K tokensClassification, extractionGPT-5 NanoOlder nano baseline~400K tokensBasic NLP tasks 👉 Key takeaway: Use Nano for scale Use Mini for balanced intelligence Use Full/Pro for complex reasoning Limitations of GPT-5.4 Nano Poor performance on multi-step reasoning or complex logic tasks Limited effectiveness in code generation or advanced analysis Reduced multimodal capability (primarily text-focused) Not suitable for decision-critical or high-accuracy reasoning tasks Representative Use Cases Text classification & tagging — sentiment, categories, moderation Data extraction pipelines — structured JSON output at scale Routing & orchestration — decide which model/tool to call next Search indexing & preprocessing — chunk labeling, metadata generation High-volume automation tasks — millions of lightweight API calls How to access GPT-5.4 Nano API Step 1: Sign Up for API Key Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit. Step 2: Send Requests to GPT-5.4 Nano API Select the “gpt-5.4-nano” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace with your actual CometAPI key from your account. base url is Chat Completions and Responses. Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer. Step 3: Retrieve and Verify Results Process the API response to get the generated answer. After processing, the API responds with the task status and output data.FAQWhat tasks is GPT-5.4 Nano API best suited for?GPT-5.4 Nano is best suited for high-volume tasks like classification, tagging, routing, and structured data extraction where speed and cost efficiency are critical.How does GPT-5.4 Nano compare to GPT-5.4 Mini?GPT-5.4 Nano is significantly faster and cheaper but has much weaker reasoning and coding capabilities compared to GPT-5.4 Mini.Can GPT-5.4 Nano API handle complex reasoning or multi-step workflows?No, GPT-5.4 Nano is not designed for deep reasoning and performs poorly on complex multi-step tasks compared to larger models.Is GPT-5.4 Nano API suitable for real-time high-throughput systems?Yes, it is optimized for ultra-low latency and high throughput, making it ideal for real-time pipelines and large-scale API workloads.Does GPT-5.4 Nano support structured outputs like JSON?Yes, GPT-5.4 Nano is highly effective at generating consistent structured outputs such as JSON for extraction and labeling tasks.When should I use GPT-5.4 Nano instead of GPT-5.4 or Mini?Use GPT-5.4 Nano when cost and speed matter more than reasoning quality, especially in simple, repeatable tasks at scale.What are the limitations of GPT-5.4 Nano API?Its main limitations include weak reasoning ability, limited coding performance, and reduced effectiveness for complex or decision-critical applications.Features for GPT-5.4 nanoExplore the key features of GPT-5.4 nano, designed to enhance performance and usability. Discover how these capabilities can benefit your projects and improve user experience.Pricing for GPT-5.4 nanoExplore competitive pricing for GPT-5.4 nano, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how GPT-5.4 nano can enhance your projects while keeping costs manageable.Comet Price (USD / M Tokens)Official Price (USD / M Tokens)DiscountInput:$0.16/MOutput:$1/MInput:$0.2/MOutput:$1.25/M-20%Sample code and API for GPT-5.4 nanoAccess comprehensive sample code and API resources for GPT-5.4 nano to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of GPT-5.4 nano in your projects.CopyPythonJavaScriptCurlfrom openai import OpenAI import os # Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "" BASE_URL = "https://api.cometapi.com/v1" client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY) response = client.responses.create( model="gpt-5.4-nano", input="How much gold would it take to coat the Statue of Liberty in a 1mm layer?", reasoning={"effort": "none"}, ) print(response.output_text)Versions of GPT-5.4 nanoThe reason GPT-5.4 nano has multiple snapshots may include potential factors such as variations in output after updates requiring older snapshots for consistency, providing developers a transition period for adaptation and migration, and different snapshots corresponding to global or regional endpoints to optimize user experience. For detailed differences between versions, please refer to the official documentation. versiongpt-5.4-nanogpt-5.4-nano-2026-03-17More Models