GPT-5.4 vs GPT-5.4 mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5.4 by OpenAI and GPT-5.4 mini 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-5.4 mini holds a notable pricing advantage for token ingestion. On output generation, GPT-5.4 mini is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.4 provides an expanded memory horizon with 1,050K tokens. In latency-sensitive production environments, GPT-5.4 mini 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-5.4
Listo para agentes con 1Mun agente-ready GPT generación construido para codificación y uso de computadora flujos de trabajo un menor costo.
$2.50 / 1M
$15.00 / 1M
1.05M tokens
79.2%
GPT-5.4 mini
GPT pequeño y fuerteun sólido pequeño GPT para codificación sub-agentes y alto-volumen trabajo.
$0.50 / 1M
$4.00 / 1M
400K tokens
68.5%
Diferencias Clave de un Vistazo
- GPT-5.4 mini is 4.0x cheaper for API usage (input + output tokens combined).
- GPT-5.4 has a larger context window (1M vs 400K tokens).
- GPT-5.4 mini is faster at 175 tok/s vs 126 tok/s.
- GPT-5.4 leads on SWE-bench coding (79.2% vs 68.5%).
| Característica | GPT-5.4 | GPT-5.4 mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 59 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $2.50 / 1M | $0.50 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $4.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.17 / 1M | $0.53 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,575 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 126 tok/s | 175 tok/s |
SWE-bench Verified Real-world software engineering | 79.2% | 68.5% |
GPQA Diamond Graduate-level reasoning | 78.6% | 72.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.05 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones 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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.4 · Strengths & Key Advantages
- primer GPT-5-clase modelo con un 1M-token ventana de contexto
- nativo uso de computadora y browser automatización
- Well equilibrado costo para largo agente se ejecuta
- nativo uso de computadora y browser control
Considerations & Trade-offs
- entrada precio doubles beyond 272K tokens
- uso de computadora actions still need human revisión en critical paths
GPT-5.4 mini · Strengths & Key Advantages
- Excellent precio-un-capacidad para sub-agentes
- rápido enough para parallel herramienta bucles
- confiable estructurado salida
- excelente rendimiento para parallel sub-agente fleets
Considerations & Trade-offs
- Weaker que GPT-5.4 en hard razonamiento
- Shorter contexto que el 5.4 insignia
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.4 | GPT-5.4 mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.10 | $0.62 |
Output Tokens / Task Tokens generated per task | 62K | 52K |
Reasoning Tokens / Task Thinking tokens burned per task | 34K | 27K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | GPT-5.4 | GPT-5.4 mini |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-4 | gpt-5-4-mini |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-03 | 2026-03 |
| Knowledge Cutoff | August 2025 | August 2025 |
| Architecture | Agent-ready unified reasoning model | Compact agent-ready reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2.5/1M in) across full 1050K window | Flat rate ($0.5/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.25/1M (no extra cache-write fee) | Automatic prefix caching at $0.05/1M (no extra cache-write fee) |
| 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 · 100% Strict JSON Schema |
| 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-5.4 vs GPT-5.4 mini: Respuestas Rápidas (FAQ)
Which is more intelligent, GPT-5.4 or GPT-5.4 mini?
GPT-5.4 is more intelligent. It scores 78 compared with 59 on the composite Intelligence Index.
Which model is faster?
GPT-5.4 mini is faster, generating 175 tokens per second compared with 126 tokens per second.
Which model is cheaper to run?
GPT-5.4 mini is cheaper at $0.53 per 1M tokens blended, compared with $2.17 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.4 mini has lower latency, with a time to first token of 0.76s compared with 1.08s.
Which model has a larger context window?
GPT-5.4 has a larger context window, supporting 1.05M tokens compared with 400K tokens.
Veredicto
Verdict & Model Selection Recommendation: Choose GPT-5.4 if your priority is Long-horizon agentic and computer-use workflows. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.4 mini if your workflow prioritizes Sub-agents, parallel tool calls and high-volume coding tasks. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does GPT-5.4 compare to GPT-5.4 mini in API token pricing and cost per token?+
GPT-5.4 costs $2.50 per 1M input tokens and $15.00 per 1M output tokens, whereas GPT-5.4 mini costs $0.50 input and $4.00 output. Overall, GPT-5.4 mini 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-5.4 or GPT-5.4 mini?+
GPT-5.4 supports a context window of 1,050K tokens (~788k words), while GPT-5.4 mini supports 400K tokens (~300k words). Therefore, GPT-5.4 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.4 or GPT-5.4 mini?+
GPT-5.4 scores 79.2% on SWE-bench Verified while GPT-5.4 mini scores 68.5%. For autonomous software engineering and multi-file debugging, GPT-5.4 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.4 and GPT-5.4 mini?+
GPT-5.4 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.4 mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.4 and GPT-5.4 mini?+
GPT-5.4 generates approximately 126 tokens per second, while GPT-5.4 mini delivers 175 tokens per second. In latency-sensitive production environments, GPT-5.4 mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.4 and GPT-5.4 mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.4 mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.4. 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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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: GetInfoUs Independent AI Benchmarks & Official API Specs