Head-to-Head Model Comparison

Nano Banana Pro vs GPT-6.1 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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Compare Nano Banana Pro by Google against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.

Perbandingan Langsung (VS)

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Popular Matchups:
Google

Nano Banana Pro

Model Generasi Gambar AI

Nano Banana Pro dari Google adalah model AI yang dirancang untuk pembuatan gambar AI resolusi tinggi, desain visual, dan perenderan tipografi akurat, menghadirkan jendela konteks 66K, kecepatan 50 token/detik, indeks kecerdasan 83/100, sert

Intelligence Index83 / 100
Input Token:

$3.50 / 1M

Output Token:

$35.00 / 1M

Context Window:

65.5K tokens

SWE-bench Verified:

N/A

OpenAI

GPT-6.1 Sol

Model Unggulan Garis Depan

GPT-6.1 Sol dari OpenAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1M, kecepatan 120 token/detik, indeks kecerdasan 94/100, serta tarif $2

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

Perbedaan Utama Sekilas

  • GPT-6.1 Sol is 3.1x cheaper for API usage (input + output tokens combined).
  • GPT-6.1 Sol has a larger context window (1M vs 66K tokens).
  • GPT-6.1 Sol is faster at 120 tok/s vs 50 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureNano Banana ProGPT-6.1 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
8394
Provider
GoogleOpenAI
Input Price
Cost per million input tokens
$3.50 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$35.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$4.76 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
65.5K tokens~98 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
16K128K
Throughput Speed
Tokens generated per second
50 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
N/A82.3%
GPQA Diamond
Graduate-level reasoning
N/A80.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.80 / 1M$0.10 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
GPT-6.1 Sol
3.1x cheaper monthly runtime
Speed
GPT-6.1 Sol
+140% faster generation
Context
GPT-6.1 Sol
1M tokens window

Skor Benchmark Terverifikasi

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

Nano Banana ProGPT-6.1 Sol
SWE-bench Verified (Autonomous Coding)

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

N/Avs82.3%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs80.8%
MMLU-Pro (Multi-task Language Understanding)

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

N/Avs89.9%
LMSYS Chatbot Arena Elo

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

1415ptsvs1519pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs18.1%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs63.6%
SciCode (Scientific Computing)

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

N/Avs54.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.7%
AIME (Advanced Math Competition)

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

N/Avs93.5%
Long-Context Reasoning

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

N/Avs91%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

50 tok/s vs120 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
Nano Banana Pro1.20s
GPT-6.1 Sol1.12s
Est. End-to-End Response (500 tokens)TTFT + generation time
Nano Banana Pro11.20s
GPT-6.1 Sol5.29s

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)
Nano Banana Pro Monthly API Bill
$37.95 / month
$3.5/1M in · $35/1M out
GPT-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
GPT-6.1 Sol saves approximately $25.80 / month (68%) under this workload.3.1x Savings

Nano Banana Pro · Strengths & Key Advantages

  • Jendela konteks 66K dengan kapasitas output maksimum hingga 16K token (Arena Elo 1415)
  • Kecepatan generasi tinggi 50 token/detik dengan latensi token pertama (TTFT) hanya 1200 ms
  • Skema harga API kompetitif (tarif $3.5/$35 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
  • Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

GPT-6.1 Sol · Strengths & Key Advantages

  • Jendela konteks 1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
  • Kecepatan generasi tinggi 120 token/detik dengan latensi token pertama (TTFT) hanya 1120 ms
  • Skema harga API kompetitif (tarif $2/$10 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
  • Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

Token Use & Task Economics

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

MetricNano Banana ProGPT-6.1 Sol
Cost per Task
Estimated USD to finish one task
$0.05$2.05
Output Tokens / Task
Tokens generated per task
2K62K
Reasoning Tokens / Task
Thinking tokens burned per task
1K36K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureNano Banana ProGPT-6.1 Sol
Official API Model ID (SDK)models/nano-banana-progpt-6-1-sol
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI Responses API & Chat Completions v1
Release Date2025-112026-09
Knowledge CutoffSeptember 2025April 2026
ArchitectureHigh-fidelity design-grade image generation and compositional editorNear-Astra 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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($3.5/1M in) across full 66K windowFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.8/1M with configurable TTLAutomatic prefix caching at $0.1/1M (no extra cache-write fee)
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 · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationSupervised 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 ClassificationGoogle DeepMind Frontier Safety Framework VerifiedOpenAI 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 Modalitiesimagetext

Nano Banana Pro vs GPT-6.1 Sol: Jawaban Cepat (FAQ)

Which is more intelligent, Nano Banana Pro or GPT-6.1 Sol?

GPT-6.1 Sol is more intelligent. It scores 94 compared with 83 on the composite Intelligence Index.

Which model is faster?

GPT-6.1 Sol is faster, generating 120 tokens per second compared with 50 tokens per second.

Which model is cheaper to run?

GPT-6.1 Sol is cheaper at $1.47 per 1M tokens blended, compared with $4.76 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-6.1 Sol has lower latency, with a time to first token of 1.12s compared with 1.20s.

Which model has a larger context window?

GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 65.5K tokens.

Kesimpulan

Choose Nano Banana Pro for sangat cocok untuk pembuatan gambar ai resolusi tinggi, desain visual, dan perenderan tipografi akurat, sistem rag perusahaan, dan integrasi api skala produksi., or choose GPT-6.1 Sol for sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi ai, sistem rag perusahaan, dan integrasi api skala produksi..

Pertanyaan Umum (FAQ)

Which is cheaper: Nano Banana Pro or GPT-6.1 Sol?+

Nano Banana Pro costs $3.5/1M input and $35/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: Nano Banana Pro or GPT-6.1 Sol?+

Nano Banana Pro supports 66K tokens whereas GPT-6.1 Sol supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

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

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: Google & OpenAI Official Documentation