GPT-1 vs GPT-2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-1 by OpenAI and GPT-2 by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, GPT-2 provides an expanded memory horizon with 1K tokens. In latency-sensitive production environments, GPT-1 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
Changez et Comparez les Modèles
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GPT-1
Pionnier de Transformerd'OpenAI foundational generative pre-trained transformer modèle qui established unsupervised pre-training followed par supervised ajustement fin.
$0.00 / 1M
$0.00 / 1M
512 tokens
N/A
GPT-2
Jalon zero-shotd'OpenAI landmark unsupervised modèle de langage demonstrating zero-shot tâche transfer à travers reading compréhension, traduction, et résumé.
$0.00 / 1M
$0.00 / 1M
1K tokens
N/A
Différences Clés en un Coup d’Œil
- Both models cost the same under this workload — choose on capability, not price.
- GPT-2 has a larger context window (1K vs 1K tokens).
- GPT-1 is faster at 150 tok/s vs 120 tok/s.
| Caractéristique | GPT-1 | GPT-2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 5 | 12 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 512 tokens~1 A4 pages of text | 1K tokens~2 A4 pages of text |
Max Output Tokens Per single request | 1K | 1K |
Throughput Speed Tokens generated per second | 150 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-1 · Strengths & Key Advantages
- Proved unsupervised pre-training transferability à diverse NLP benchmarks
- Foundation de tous moderne GPT et transformer autoregressive modèles
- Demonstrated natural langue compréhension à travers GLUE datasets
Considerations & Trade-offs
- limité à 512-token fenêtre de contexte
- manque de conversationnel alignment et instruction tuning
GPT-2 · Strengths & Key Advantages
- Pioneered zero-shot tâche completion sans explicit tâche-specific training sets
- élevé-cohérence multi-paragraph texte génération
- Openly released modèle weights pour mondial recherche
Considerations & Trade-offs
- fenêtre de contexte limité à 1,024 tokens
- Prone à repetition et factual hallucinations sur long passages
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-1 | GPT-2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.20 |
Output Tokens / Task Tokens generated per task | 24K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | GPT-1 | GPT-2 |
|---|---|---|
| Official API Model ID (SDK) | gpt-1 | gpt-2 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2018-06 | 2019-02 |
| Knowledge Cutoff | 2018 | 2019 |
| Architecture | 12-layer Decoder Transformer | 48-layer Decoder Transformer |
| Parameters | 117M | 1.5B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
GPT-1 vs GPT-2: Réponses Rapides (FAQ)
Which is more intelligent, GPT-1 or GPT-2?
GPT-2 is more intelligent. It scores 12 compared with 5 on the composite Intelligence Index.
Which model is faster?
GPT-1 is faster, generating 150 tokens per second compared with 120 tokens per second.
Which model has lower latency?
GPT-1 has lower latency, with a time to first token of 0.25s compared with 0.30s.
Which model has a larger context window?
GPT-2 has a larger context window, supporting 1K tokens compared with 512 tokens.
Verdict
Verdict & Model Selection Recommendation: Choose GPT-1 if your priority is Academic historical research and understanding the roots of modern transformer architectures. Its performance in demanding tasks justifies its operational token cost. Choose GPT-2 if your workflow prioritizes Historical NLP research, lightweight local text generation experiments, and open-weights study. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does GPT-1 compare to GPT-2 in API token pricing and cost per token?+
Both GPT-1 and GPT-2 are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: GPT-1 or GPT-2?+
GPT-1 supports a context window of 512 tokens (~384 words), while GPT-2 supports 1K tokens (~1k words). Therefore, GPT-2 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-1 or GPT-2?+
GPT-1 scores at the frontier of coding benchmarks, while GPT-2 scores competitively for software generation. For complex autonomous debugging and agentic engineering, GPT-1 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GPT-1 and GPT-2?+
GPT-1 is optimized for low-latency direct generation. In contrast, GPT-2 focuses on standard fast inference.
How does inference speed (tokens per second) compare between GPT-1 and GPT-2?+
GPT-1 generates approximately 150 tokens per second, while GPT-2 delivers 120 tokens per second. In latency-sensitive production environments, GPT-1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-1 and GPT-2 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to GPT-1, and route deep architectural or complex reasoning queries to GPT-1. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs