GPT-5.4 nano vs GPT-5.3 Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5.4 nano by OpenAI and GPT-5.3 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-5.3 Chat holds a notable pricing advantage for token ingestion. On output generation, GPT-5.3 Chat is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.4 nano provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5.4 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.
İstediğiniz Modelleri Seçin ve Karşılaştırın
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GPT-5.4 nano
Derin Akıl Yürütme ModeliOpenAI tarafından geliştirilen GPT-5.4 nano, 400K token bağlam penceresi ve 1M girdi başına $0.1, 1M çıktı başına $0.8 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.10 / 1M
$0.80 / 1M
400K tokens
48%
GPT-5.3 Chat
Derin Akıl Yürütme ModeliOpenAI tarafından geliştirilen GPT-5.3 Chat, 128K token bağlam penceresi ve 1M girdi başına $0, 1M çıktı başına $0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.00 / 1M
$0.00 / 1M
128K tokens
77%
Bir Bakışta Temel Farklar
- GPT-5.3 Chat is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-5.4 nano has a larger context window (400K vs 128K tokens).
- GPT-5.4 nano is faster at 235 tok/s vs 138 tok/s.
- GPT-5.3 Chat leads on SWE-bench coding (77.0% vs 48.0%).
| Feature | GPT-5.4 nano | GPT-5.3 Chat |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 45 | 69 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.80 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.11 / 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 | 235 tok/s | 138 tok/s |
SWE-bench Verified Real-world software engineering | 48% | 77% |
GPQA Diamond Graduate-level reasoning | 61.5% | 78% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.4 nano · Strengths & Key Advantages
- OpenAI imzalı gelişmiş Nano agent-ready reasoning model mimarisi ve 400K token bağlam kapasitesi
- Dengeli API maliyeti ($0.1/1M girdi, $0.8/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
GPT-5.3 Chat · Strengths & Key Advantages
- OpenAI imzalı gelişmiş Chat-tuned unified reasoning model mimarisi ve 128K token bağlam kapasitesi
- Dengeli API maliyeti ($0/1M girdi, $0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.4 nano | GPT-5.3 Chat |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.11 | $2.20 |
Output Tokens / Task Tokens generated per task | 44K | 56K |
Reasoning Tokens / Task Thinking tokens burned per task | 19K | 31K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.4 nano | GPT-5.3 Chat |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-4-nano | gpt-5-3-chat |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-03 | 2026-03 |
| Knowledge Cutoff | August 2025 | November 2025 |
| Architecture | Nano agent-ready 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 ($0.1/1M in) across full 400K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.01/1M (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-5.4 nano vs GPT-5.3 Chat: Hızlı Yanıtlar (SSS)
Which is more intelligent, GPT-5.4 nano or GPT-5.3 Chat?
GPT-5.3 Chat is more intelligent. It scores 69 compared with 45 on the composite Intelligence Index.
Which model is faster?
GPT-5.4 nano is faster, generating 235 tokens per second compared with 138 tokens per second.
Which model is cheaper to run?
GPT-5.3 Chat is cheaper at $0.00 per 1M tokens blended, compared with $0.11 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.4 nano has lower latency, with a time to first token of 0.40s compared with 1.06s.
Which model has a larger context window?
GPT-5.4 nano has a larger context window, supporting 400K tokens compared with 128K tokens.
Sonuç
Verdict & Model Selection Recommendation: Choose GPT-5.4 nano if your priority is Bulk automation, tagging and cheap agent routing. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.3 Chat if your workflow prioritizes Conversational and writing work in ChatGPT. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Sıkça Sorulan Sorular (SSS)
How does GPT-5.4 nano compare to GPT-5.3 Chat in API token pricing and cost per token?+
GPT-5.4 nano costs $0.10 per 1M input tokens and $0.80 per 1M output tokens, whereas GPT-5.3 Chat costs $0.00 input and $0.00 output. Overall, GPT-5.3 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-5.4 nano or GPT-5.3 Chat?+
GPT-5.4 nano supports a context window of 400K tokens (~300k words), while GPT-5.3 Chat supports 128K tokens (~96k words). Therefore, GPT-5.4 nano can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.4 nano or GPT-5.3 Chat?+
GPT-5.4 nano scores 48% on SWE-bench Verified while GPT-5.3 Chat scores 77%. For autonomous software engineering and multi-file debugging, GPT-5.3 Chat holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.4 nano and GPT-5.3 Chat?+
GPT-5.4 nano features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.3 Chat provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.4 nano and GPT-5.3 Chat?+
GPT-5.4 nano generates approximately 235 tokens per second, while GPT-5.3 Chat delivers 138 tokens per second. In latency-sensitive production environments, GPT-5.4 nano provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.4 nano and GPT-5.3 Chat to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.3 Chat to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.4 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.
Resmi kaynaklar: GetInfoUs Independent AI Benchmarks & Official API Specs