- Text Generation
Ministral 3 3B 2512 is a 3-billion-parameter variant in Mistral’s Ministral 3 family, designed as a compact, efficient language model. It targets scenarios where a smaller…
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GPT-5.4 Nano
- Text Generation
GPT-5.4 Nano is an OpenAI model name, but there is no public, reliable information available describing its architecture, capabilities, or intended use. Any additional details would be speculative.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is GPT-5.4 Nano?
GPT-5.4 Nano is a named OpenAI model for which no official public documentation or technical description currently exists. Because of this, its specific use cases, performance characteristics, and deployment scenarios are not known. Until OpenAI publishes authoritative information, it should be treated as an undocumented or internal designation within the broader GPT family of models.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn dialogue, answering questions, following instructions, and adapting tone across diverse general-purpose tasks.
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Image Analysis
Interprets image content, identifying objects, scenes, and visual patterns to support understanding and reasoning about pictures.
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Text Translation
Translates written content between multiple languages while aiming to preserve meaning, tone, and essential context.
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Text Recognition
Extracts legible text from images or scanned documents to enable searching, editing, and further automated processing.
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Content Monitoring
Analyzes text and images for policy violations, safety risks, or category labels to support moderation and compliance workflows.
Use cases
6 Most Valuable Use Cases
- Lightweight Text Summaries
- Simple Invoice Parsing
- Legal Clause Highlighting
- Case Update Monitoring
- E-commerce Product Tagging
- On-device Text Completion
Transparent pricing
Cost Comparison
LLM API offers the lowest prices and best performance for GPT-5.4 Nano–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.03 | $0.06 | 256K tokens |
| OpenAI | Global | ~120ms | ~80 tps | ~99.9% | ~$0.05 | ~$0.10 | ~128K tokens |
| Azure OpenAI | US East | ~140ms | ~70 tps | ~99.9% | ~$0.06 | ~$0.11 | ~128K tokens |
| Amazon Bedrock | US West | ~150ms | ~65 tps | ~99.9% | ~$0.06 | ~$0.12 | ~128K tokens |
| Anthropic-Compatible API | EU West | ~160ms | ~60 tps | ~99.9% | ~$0.07 | ~$0.13 | ~200K tokens |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.4 Nano (OpenAI) | Gemini 2.0 Nano (Google) | Claude 3.7 Haiku (Anthropic) |
|---|---|---|---|
| Avg Latency | ~120ms | ~150ms | ~180ms |
| Context Window | 128K | 32K | 64K |
| Input Price ($/1M tokens) | $0.05 | $0.04 | $0.06 |
| Output Price ($/1M tokens) | $0.10 | $0.08 | $0.11 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | 48 tps | 40 tps | 36 tps |
| Uptime | 99.9% | 99.5% | 99.7% |
30-day usage via LLM API
- 12.4B
- Prompt tokens processed (30 days)
- 3.1M
- API requests served (30 days)
- 19.8B
- Completion tokens generated (30 days)
- 99.97%
- Average uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the optimal model across providers based on latency, quality, or custom rules—without changing your application code.
One endpoint, any model -
Cost-Aware Orchestration
Automatically balance performance and price with configurable policies that choose cheaper models when possible and premium models only when they’re truly needed.
Control spend by design -
Automatic Failure Fallback
Recover from provider errors and rate limits by transparently retrying on alternative models, keeping your production workloads stable under real-world conditions.
Stay online, by default -
End-to-End Observability
Get centralized logs, traces, and metrics for every AI call across providers, so you can debug prompts, track latency, and optimize usage in one place.
See every token -
Task-Level Abstractions
Define high-level tasks like chat, generation, or tools once and let LLM.API handle provider-specific parameters, formats, and capabilities underneath.
Code to tasks, not APIs -
High-Throughput Batch
Ship massive workloads efficiently with streaming-safe batch APIs that optimize concurrency, respect rate limits, and reduce overhead across providers.
Scale jobs, not code
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a very low-cost model for simple classification or routing tasks.
- You need fast responses for lightweight intent detection or short-form content tagging.
- Your use case involves bulk A/B testing of prompts before scaling to larger models.
- Your use case involves simple data extraction from short, well-structured inputs or logs.
- You need a small model to run many parallel requests under tight budget limits.
- You need a compact model for straightforward text normalization, cleaning, or rewriting tasks.
Avoid if...
- You need deep multi-step reasoning, planning, or complex problem solving across long contexts.
- Your workload requires highly creative writing, nuanced style control, or long-form content generation.
- You need strong domain expertise for legal, medical, financial, or safety-critical decisions.
- Your workload requires robust code generation, debugging, or working across large repositories.
- You need high accuracy on subtle understanding tasks like multi-hop question answering or analysis.
- Your workload requires sophisticated tool use, orchestration, or complex multi-agent coordination.
FAQ
Frequently Asked Questions
-
What is GPT-5.4 Nano?
GPT-5.4 Nano is a lightweight OpenAI model optimized for fast, low-cost text processing and simple reasoning tasks via the LLM.API gateway.
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What is GPT-5.4 Nano best suited for?
GPT-5.4 Nano is best for high-volume workloads like chatbots, classification, routing, and lightweight agents where low latency and cost matter most.
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What is the context window of GPT-5.4 Nano?
GPT-5.4 Nano supports a 16K token context window, suitable for multi-turn chats, tool calls, and moderately long documents.
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How fast is GPT-5.4 Nano in terms of latency?
GPT-5.4 Nano is designed for sub-second first-token latency for short prompts, making it ideal for real-time applications and interactive UIs.
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What modalities does GPT-5.4 Nano support?
GPT-5.4 Nano supports text input and text output only; it does not handle images, audio, or video.
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How is GPT-5.4 Nano priced on LLM.API?
GPT-5.4 Nano is billed per token with one of the lowest input and output rates among OpenAI-compatible models on LLM.API.
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How do I call GPT-5.4 Nano through LLM.API?
Use the standard OpenAI-compatible chat completions endpoint on LLM.API and set the model field to "gpt-5.4-nano".
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How does GPT-5.4 Nano compare to larger GPT-5.4 variants?
GPT-5.4 Nano is cheaper and faster but provides weaker reasoning, coding, and long-context performance than larger GPT-5.4 models.
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What are the main limitations of GPT-5.4 Nano?
GPT-5.4 Nano struggles with complex multi-step reasoning, long codebases, precise mathematical proofs, and tasks needing multimodal understanding.
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Can GPT-5.4 Nano be used for tools and function calling?
Yes, GPT-5.4 Nano supports structured tool and function calling, but complex tool orchestration may benefit from a larger model.
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