GPT-5 nano vs GPT-5 Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5 nano by OpenAI and GPT-5 Chat 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, GPT-5 Chat holds a notable pricing advantage for token ingestion. On output generation, GPT-5 Chat is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5 nano provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5 nano 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.
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GPT-5 nano
Modelo de Raciocínio ProfundoO GPT-5 nano da OpenAI é um modelo de inteligência artificial com janela de contexto de 400K tokens e preço de API de $0.05/1M na entrada e $0.4/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.05 / 1M
$0.40 / 1M
400K tokens
34.5%
GPT-5 Chat
Modelo de Raciocínio ProfundoO GPT-5 Chat da OpenAI é um modelo de inteligência artificial com janela de contexto de 128K tokens e preço de API de $0/1M na entrada e $0/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.00 / 1M
$0.00 / 1M
128K tokens
70%
Principais Diferenças em Resumo
- GPT-5 Chat is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-5 nano has a larger context window (400K vs 128K tokens).
- GPT-5 nano is faster at 250 tok/s vs 140 tok/s.
- GPT-5 Chat leads on SWE-bench coding (70.0% vs 34.5%).
| Feature | GPT-5 nano | GPT-5 Chat |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 35 | 56 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.05 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 128K | 16K |
Throughput Speed Tokens generated per second | 250 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 34.5% | 70% |
GPQA Diamond Graduate-level reasoning | 48% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5 nano · Strengths & Key Advantages
- Arquitetura Nano unified reasoning model avançada da OpenAI com contexto de 400K tokens
- Excelente relação custo-benefício ($0.05/1M entrada, $0.4/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
GPT-5 Chat · Strengths & Key Advantages
- Arquitetura Chat-tuned unified reasoning model avançada da OpenAI com contexto de 128K tokens
- Excelente relação custo-benefício ($0/1M entrada, $0/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5 nano | GPT-5 Chat |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $1.50 |
Output Tokens / Task Tokens generated per task | 42K | 54K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 28K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5 nano | GPT-5 Chat |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-nano | gpt-5-chat |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-08 | 2025-08 |
| Knowledge Cutoff | September 2024 | September 2024 |
| Architecture | Nano unified reasoning model | Chat-tuned unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.05/1M in) across full 400K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.005/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native MCP Client/Server ready · Parallel function calling |
| 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 | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
GPT-5 nano vs GPT-5 Chat: Respostas Rápidas (FAQ)
Which is more intelligent, GPT-5 nano or GPT-5 Chat?
GPT-5 Chat is more intelligent. It scores 56 compared with 35 on the composite Intelligence Index.
Which model is faster?
GPT-5 nano is faster, generating 250 tokens per second compared with 140 tokens per second.
Which model is cheaper to run?
GPT-5 Chat is cheaper at $0.00 per 1M tokens blended, compared with $0.05 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5 nano has lower latency, with a time to first token of 0.38s compared with 1.05s.
Which model has a larger context window?
GPT-5 nano has a larger context window, supporting 400K tokens compared with 128K tokens.
Veredito
Verdict & Model Selection Recommendation: Choose GPT-5 nano if your priority is Bulk classification, tagging and cheap agent routing. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5 Chat if your workflow prioritizes Interactive chat and writing inside ChatGPT. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Perguntas Frequentes (FAQ)
How does GPT-5 nano compare to GPT-5 Chat in API token pricing and cost per token?+
GPT-5 nano costs $0.05 per 1M input tokens and $0.40 per 1M output tokens, whereas GPT-5 Chat costs $0.00 input and $0.00 output. Overall, GPT-5 Chat is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-5 nano or GPT-5 Chat?+
GPT-5 nano supports a context window of 400K tokens (~300k words), while GPT-5 Chat supports 128K tokens (~96k words). Therefore, GPT-5 nano can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5 nano or GPT-5 Chat?+
GPT-5 nano scores 34.5% on SWE-bench Verified while GPT-5 Chat scores 70%. For autonomous software engineering and multi-file debugging, GPT-5 Chat holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5 nano and GPT-5 Chat?+
GPT-5 nano features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5 Chat provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5 nano and GPT-5 Chat?+
GPT-5 nano generates approximately 250 tokens per second, while GPT-5 Chat delivers 140 tokens per second. In latency-sensitive production environments, GPT-5 nano provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5 nano and GPT-5 Chat to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5 Chat to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5 nano. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Fontes oficiais: GetInfoUs Independent AI Benchmarks & Official API Specs