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Head-to-Head Model Comparison

GPT-5.4 nano vs GPT-5.3 Chat

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-5.4 nano vs GPT-5.3 Chat: Benchmarks, Pricing & Speed Comparison

GPT-5.4 nano dari OpenAI dan GPT-5.3 Chat dari OpenAI adalah dua model AI unggulan. GPT-5.4 nano menawarkan jendela konteks 400K token dengan harga $0.10/$0.80 per 1 juta token, sedangkan GPT-5.3 Chat menyediakan konteks 128K token seharga $0.00/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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OpenAI

GPT-5.4 nano

Model Ringan & Hemat Biaya

GPT-5.4 nano dari OpenAI adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 400K, kecepatan 235 token/detik, indeks kecerdasan 45/100, serta tarif $0

Intelligence Index45 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.80 / 1M

Context Window:

400K tokens

SWE-bench Verified:

48%

OpenAI

GPT-5.3 Chat

Model Unggulan Garis Depan

GPT-5.3 Chat dari OpenAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 128K, kecepatan 138 token/detik, indeks kecerdasan 69/100, serta akses

Intelligence Index69 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

77%

Perbedaan Utama Sekilas

  • 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%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-5.4 nanoGPT-5.3 Chat
Intelligence Index
Composite reasoning & knowledge score (0-100)
4569
Provider
OpenAIOpenAI
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 text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
128K16K
Throughput Speed
Tokens generated per second
235 tok/s138 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

Siapa Unggul di Mana?

At-a-glance decision framework
Coding
GPT-5.3 Chat
+29.0% SWE-bench lead
Cost
GPT-5.3 Chat
1.0x cheaper monthly runtime
Speed
GPT-5.4 nano
+70% faster generation
Context
GPT-5.4 nano
400K tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

GPT-5.4 nanoGPT-5.3 Chat
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

48%vs77%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

61.5%vs78%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

75.8%vs88%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1310ptsvs1416pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

8.6%vs14.4%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

26%vs52.5%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

28%vs48%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

51.5%vs76%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

78%vs93.6%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

66.5%vs83%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

235 tok/s vs138 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
GPT-5.4 nano0.40s
GPT-5.3 Chat1.06s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-5.4 nano2.53s
GPT-5.3 Chat4.68s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
GPT-5.4 nano Monthly API Bill
$0.89 / month
$0.1/1M in · $0.8/1M out
GPT-5.3 Chat Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
GPT-5.3 Chat saves approximately $0.89 / month (100%) under this workload.1.0x Savings

GPT-5.4 nano · Strengths & Key Advantages

  • Jendela konteks 400K dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 48% · GPQA Diamond 61.5%)
  • Kecepatan generasi tinggi 235 token/detik dengan latensi token pertama (TTFT) hanya 400 ms
  • Skema harga API kompetitif (tarif $0.1/$0.8 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
  • Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

GPT-5.3 Chat · Strengths & Key Advantages

  • Jendela konteks 128K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 77% · GPQA Diamond 78%)
  • Kecepatan generasi tinggi 138 token/detik dengan latensi token pertama (TTFT) hanya 1060 ms
  • Skema harga API kompetitif (akses gratis open-weights) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
  • Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGPT-5.4 nanoGPT-5.3 Chat
Cost per Task
Estimated USD to finish one task
$0.11$2.20
Output Tokens / Task
Tokens generated per task
44K56K
Reasoning Tokens / Task
Thinking tokens burned per task
19K31K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-5.4 nanoGPT-5.3 Chat
Official API Model ID (SDK)gpt-5-4-nanogpt-5-3-chat
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2026-032026-03
Knowledge CutoffAugust 2025November 2025
ArchitectureNano agent-ready reasoning modelChat-tuned unified reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 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 windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix caching at $0.01/1M (no extra cache-write fee)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

GPT-5.4 nano vs GPT-5.3 Chat: Jawaban Cepat (FAQ)

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.

Kesimpulan

Baik GPT-5.4 nano (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-5.3 Chat (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT-5.3 Chat unggul dalam penalaran dan pemrograman, GPT-5.4 nano memberikan kecepatan generasi token lebih tinggi, dan GPT-5.3 Chat lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GPT-5.4 nano atau GPT-5.3 Chat?+

Dalam indeks kecerdasan gabungan dan benchmark coding, GPT-5.3 Chat memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API GPT-5.4 nano vs GPT-5.3 Chat?+

GPT-5.4 nano mengenakan biaya $0.10 / $0.80 per 1M token input/output, sedangkan GPT-5.3 Chat sebesar $0.00 / $0.00. Secara keseluruhan, GPT-5.3 Chat lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

GPT-5.4 nano mendukung konteks 400K token dibandingkan 128K token pada GPT-5.3 Chat, sementara GPT-5.4 nano unggul dalam kecepatan output token per detik.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Sumber resmi: Dokumentasi API resmi OpenAI & OpenAI serta evaluasi benchmark independen (2026).