The cheapest GPT-5.4 lane for simple routing, extraction and bulk automation.
Technical Specifications
- Provider
- OpenAI
- Category
- lightweight
- Release Date
- 2026-03
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- gpt-5-4-nano
- Supported SDK & API Protocol
- OpenAI Responses API & Chat Completions v1
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.1/1M in) across full 400K window
- Prompt Cache Write & TTL Policy
- Automatic prefix caching at $0.01/1M (no extra cache-write fee)
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Native Web Search tool + Hosted Code Interpreter & File Search RAG
- Fine-Tuning & Distillation Support
- Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed OpenAI Cloud API
- System Card & Safety Framework
- OpenAI Preparedness Framework Audited & System Card Published
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
GPT-5.4 nano Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $4.70/mo vs $20 flat plan).
Break-even point: ~654 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to GPT-5.4 nano:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 48%
- GPQA Diamond
- 61.5%
- MMLU-Pro
- 75.8%
- HumanEval
- 90.5%
- Humanity's Last Exam
- 8.6%
- Terminal-Bench
- 26%
- SciCode
- 28%
- LiveCodeBench
- 51.5%
- AIME
- 78%
- Long-Context Reasoning
- 66.5%
- Chatbot Arena Elo
- 1310pts
Strengths
- Very cheap for a model with reasoning and tools
- Low latency at high throughput
- Cheapest tier that still supports tool calling
Limitations & Trade-offs
- Weak reasoning — fine for routing, poor for analysis
- Prone to shallow answers on ambiguous prompts
Editorial Assessment
Volume workhorse pricing for the GPT-5.4 generation.
Best for: Bulk automation, tagging and cheap agent routing
Compare GPT-5.4 nano
Frequently Asked Questions about GPT-5.4 nano
How much does the GPT-5.4 nano API cost per 1M tokens?+
GPT-5.4 nano by OpenAI is priced at $0.1 per 1M input tokens and $0.8 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.01 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the GPT-5.4 nano pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in GPT-5.4 nano's context window?+
GPT-5.4 nano features a 400K tokens context window, which equals approximately 300,000 English words or ~800 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 128,000 tokens.
Can I run GPT-5.4 nano locally on my own GPU (Open Weights)?+
No, GPT-5.4 nano is a proprietary closed-weights model hosted by OpenAI. It cannot be downloaded for local GPU execution and must be accessed via OpenAI's official API or cloud platform.
Does OpenAI train on data sent to the GPT-5.4 nano API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (OpenAI Terms). Full commercial ownership of generated outputs. Supported deployment channels include: OpenAI Platform API, Microsoft Azure OpenAI Foundry.
How does GPT-5.4 nano perform on coding and reasoning benchmarks?+
GPT-5.4 nano achieves a composite Intelligence Index of 45/100, scoring 48% on SWE-bench Verified, 61.5% on GPQA Diamond, 75.8% on MMLU-Pro, and 1310 pts on Chatbot Arena Elo.
What is GPT-5.4 nano best used for?+
GPT-5.4 nano is best suited for: Bulk automation, tagging and cheap agent routing. Assessment: Volume workhorse pricing for the GPT-5.4 generation.
OpenAI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to OpenAI's flagship reasoning tier:
GPT-6 Astra
GPT-6.1 Sol
GPT-6 Sol
GPT-6 Luna
GPT-5.6 Sol
GPT-5.6 Terra
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to GPT-5.4 nano:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Sources: OpenAI Official Pricing & Technical Reports (Late 2026)↗