Head-to-Head Model Comparison

GPT-5.4 nano vs Gemini 3.1 Flash-Lite

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

GPT-5.4 nanoProfile

Gemini 3.1 Flash-LiteProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

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%

Google

Gemini 3.1 Flash-Lite

Model Ringan & Hemat Biaya

Gemini 3.1 Flash-Lite dari Google adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 1.0M, kecepatan 250 token/detik, indeks kecerdasan 60/100, serta

Intelligence Index60 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

Gemini 3.1 Flash-Lite is 1.1x cheaper for API usage (input + output tokens combined).
Gemini 3.1 Flash-Lite has a larger context window (1M vs 400K tokens).
Gemini 3.1 Flash-Lite is faster at 250 tok/s vs 235 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	GPT-5.4 nano	Gemini 3.1 Flash-Lite
Intelligence Index Composite reasoning & knowledge score (0-100)	45	60
Provider	OpenAI	Google
Input Price Cost per million input tokens	$0.10 / 1M	$0.15 / 1M
Output Price Cost per million output tokens	$0.80 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.11 / 1M	$0.11 / 1M
Context Window Maximum input + output tokens	400K tokens~600 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	128K	33K
Throughput Speed Tokens generated per second	235 tok/s	250 tok/s
SWE-bench Verified Real-world software engineering	48%	N/A
GPQA Diamond Graduate-level reasoning	61.5%	N/A
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.04 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Gemini 3.1 Flash-Lite

1.1x cheaper monthly runtime

Speed

Gemini 3.1 Flash-Lite

+6% faster generation

Context

Gemini 3.1 Flash-Lite

1.05M tokens window

Skor Benchmark Terverifikasi

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

GPT-5.4 nanoGemini 3.1 Flash-Lite

SWE-bench Verified (Autonomous Coding)

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

48%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

61.5%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

75.8%vs78.5%

LMSYS Chatbot Arena Elo

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

1310ptsvs1355pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

8.6%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

26%vsN/A

SciCode (Scientific Computing)

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

28%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

51.5%vsN/A

AIME (Advanced Math Competition)

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

78%vsN/A

Long-Context Reasoning

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

66.5%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

235 tok/s vs250 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

Gemini 3.1 Flash-Lite0.27s

Est. End-to-End Response (500 tokens)TTFT + generation time

GPT-5.4 nano2.53s

Gemini 3.1 Flash-Lite2.27s

Simulator Biaya Token Interaktif

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

GPT-5.4 nano Monthly API Bill

$0.89 / month

$0.1/1M in · $0.8/1M out

Gemini 3.1 Flash-Lite Monthly API Bill

$0.82 / month

$0.15/1M in · $0.6/1M out

Gemini 3.1 Flash-Lite saves approximately $0.07 / month (8%) under this workload.1.1x 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

Gemini 3.1 Flash-Lite · Strengths & Key Advantages

Jendela konteks 1.0M dengan kapasitas output maksimum hingga 33K token (MMLU-Pro 78.5% · Arena Elo 1355)
Kecepatan generasi tinggi 250 token/detik dengan latensi token pertama (TTFT) hanya 270 ms
Skema harga API kompetitif (tarif $0.15/$0.6 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

Token Use & Task Economics

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

Metric	GPT-5.4 nano	Gemini 3.1 Flash-Lite
Cost per Task Estimated USD to finish one task	$0.11	$0.38
Output Tokens / Task Tokens generated per task	44K	14K
Reasoning Tokens / Task Thinking tokens burned per task	19K	5K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-5.4 nano	Gemini 3.1 Flash-Lite
Official API Model ID (SDK)	gpt-5-4-nano	models/gemini-3-1-flash-lite
API Protocol	OpenAI Responses API & Chat Completions v1	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-03	2026-03
Knowledge Cutoff	August 2025	November 2025
Architecture	Nano agent-ready reasoning model	Compact efficient multimodal transformer
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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.1/1M in) across full 400K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix caching at $0.01/1M (no extra cache-write fee)	Implicit + Explicit Context Caching at $0.035/1M with configurable TTL
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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Vertex AI Supervised Adapter Tuning & Context Distillation
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	Google DeepMind Frontier Safety Framework Verified
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 Gemini 3.1 Flash-Lite: Jawaban Cepat (FAQ)

Which is more intelligent, GPT-5.4 nano or Gemini 3.1 Flash-Lite?

Gemini 3.1 Flash-Lite is more intelligent. It scores 60 compared with 45 on the composite Intelligence Index.

Which model is faster?

Gemini 3.1 Flash-Lite is faster, generating 250 tokens per second compared with 235 tokens per second.

Which model has lower latency?

Gemini 3.1 Flash-Lite has lower latency, with a time to first token of 0.27s compared with 0.40s.

Which model has a larger context window?

Gemini 3.1 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 400K 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 Gemini 3.1 Flash-Lite (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Gemini 3.1 Flash-Lite unggul dalam penalaran dan pemrograman, Gemini 3.1 Flash-Lite memberikan kecepatan generasi token lebih tinggi, dan GPT-5.4 nano lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GPT-5.4 nano atau Gemini 3.1 Flash-Lite?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Gemini 3.1 Flash-Lite memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API GPT-5.4 nano vs Gemini 3.1 Flash-Lite?+

GPT-5.4 nano mengenakan biaya $0.10 / $0.80 per 1M token input/output, sedangkan Gemini 3.1 Flash-Lite sebesar $0.15 / $0.60. Secara keseluruhan, GPT-5.4 nano lebih hemat biaya.

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

GPT-5.4 nano mendukung konteks 400K token dibandingkan 1.05M token pada Gemini 3.1 Flash-Lite, sementara Gemini 3.1 Flash-Lite 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

GPT-5.4 nano vs Gemini 3.1 Flash-Lite

Ganti & Bandingkan Model Apa Saja

GPT-5.4 nano

Gemini 3.1 Flash-Lite

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GPT-5.4 nano · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 3.1 Flash-Lite · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GPT-5.4 nano vs Gemini 3.1 Flash-Lite: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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