GPT-2 de OpenAI es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 1K, velocidad de 120 tokens/s, índice de inteligencia de 12/100 y tarifa de acceso gratuito de pesos abiertos.
Technical Specifications
- Provider
- OpenAI
- Category
- historical
- Release Date
- 2019-02
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- 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-2
- Supported SDK & API Protocol
- OpenAI Responses API & Chat Completions v1
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in OpenAI lineup)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- Text-only completion (No GUI action loop)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- Web Search Grounding & Code Sandbox
- Native Web Search tool + Hosted Code Interpreter & File Search RAG
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- OpenAI Preparedness Framework Audited & System Card Published
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
GPT-2 Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
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-2:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 15.2%
- Chatbot Arena Elo
- 750pts
Strengths
- Ventana de contexto de 1K con hasta 1K tokens de salida máxima (MMLU-Pro 15.2% · Arena Elo 750)
- Rendimiento ágil de 120 tokens/segundo con latencia inicial (TTFT) de solo 300 ms
- Pesos abiertos (1.5B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Limitations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Editorial Assessment
Con una puntuación de inteligencia de 12/100, contexto de 1K y 120 tok/s, GPT-2 destaca como una opción sumamente sólida dentro del ecosistema de OpenAI.
Best for: Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.
Compare GPT-2
Frequently Asked Questions about GPT-2
How much does the GPT-2 API cost per 1M tokens?+
GPT-2 by OpenAI is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the GPT-2 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-2's context window?+
GPT-2 features a 1K tokens context window, which equals approximately 768 English words or ~2 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 1,024 tokens.
Can I run GPT-2 locally on my own GPU (Open Weights)?+
Yes, GPT-2 (1.5B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does OpenAI train on data sent to the GPT-2 API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does GPT-2 perform on coding and reasoning benchmarks?+
GPT-2 achieves a composite Intelligence Index of 12/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 15.2% on MMLU-Pro, and 750 pts on Chatbot Arena Elo.
What is GPT-2 best used for?+
GPT-2 is best suited for: Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.. Assessment: Con una puntuación de inteligencia de 12/100, contexto de 1K y 120 tok/s, GPT-2 destaca como una opción sumamente sólida dentro del ecosistema de OpenAI.
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-2:
La unidad básica de texto que procesa un modelo de lenguaje, que normalmente equivale a 3 o 4 caracteres o aproximadamente 0,75 palabras. Por ejemplo, una frase típica se divide en varios tokens.
Un conjunto estandarizado de pruebas para evaluar y comparar objetivamente las capacidades de distintos modelos de IA en tareas como programación (SWE-bench) o matemáticas avanzadas (AIME).
La cantidad máxima de tokens que un modelo puede considerar a la vez, incluyendo tanto la entrada que lee como la salida que genera. Una ventana de 1.000.000 de tokens puede procesar unas 750.000 palabras en una sola petición.
La arquitectura de red neuronal introducida en 2017 que impulsa los LLM modernos, empleando mecanismos de autoatención para procesar secuencias completas en paralelo.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: OpenAI paper: Language Models are Unsupervised Multitask Learners (2019)↗