Head-to-Head Model Comparison

Gemini Robotics-ER 1.6 vs Gemini 3.1 Flash-Lite

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책

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Gemini Robotics-ER 1.6Profile

Gemini 3.1 Flash-LiteProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini Robotics-ER 1.6

심층 추론 모델

Google의 Gemini Robotics-ER 1.6 모델은 131K 토큰 컨텍스트 윈도우와 입력 $2.5/1M 토큰, 출력 $10/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index82 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Google

Gemini 3.1 Flash-Lite

심층 추론 모델

Google의 Gemini 3.1 Flash-Lite 모델은 1.0M 토큰 컨텍스트 윈도우와 입력 $0.15/1M 토큰, 출력 $0.6/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index60 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

한눈에 보는 핵심 차이점

Gemini 3.1 Flash-Lite is 16.5x cheaper for API usage (input + output tokens combined).
Gemini 3.1 Flash-Lite has a larger context window (1M vs 131K tokens).
Gemini 3.1 Flash-Lite is faster at 250 tok/s vs 90 tok/s.

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

Feature	Gemini Robotics-ER 1.6	Gemini 3.1 Flash-Lite
Intelligence Index Composite reasoning & knowledge score (0-100)	82	60
Provider	Google	Google
Input Price Cost per million input tokens	$2.50 / 1M	$0.15 / 1M
Output Price Cost per million output tokens	$10.00 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.85 / 1M	$0.11 / 1M
Context Window Maximum input + output tokens	131.1K tokens~197 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	16K	33K
Throughput Speed Tokens generated per second	90 tok/s	250 tok/s
SWE-bench Verified Real-world software engineering	N/A	N/A
Cached Input Price Prompt caching discount per 1M tokens	$0.50 / 1M	$0.04 / 1M

부문별 우위 모델

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Gemini 3.1 Flash-Lite

16.5x cheaper monthly runtime

Speed

Gemini 3.1 Flash-Lite

+178% faster generation

Context

Gemini 3.1 Flash-Lite

1.05M tokens window

검증된 벤치마크 점수 비교

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

Gemini Robotics-ER 1.6Gemini 3.1 Flash-Lite

MMLU-Pro (Multi-task Language Understanding)

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

N/Avs78.5%

LMSYS Chatbot Arena Elo

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

1410ptsvs1355pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs250 tok/s

지연 시간(Latency) 및 응답 속도

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

Gemini Robotics-ER 1.60.70s

Gemini 3.1 Flash-Lite0.27s

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

Gemini Robotics-ER 1.66.26s

Gemini 3.1 Flash-Lite2.27s

실시간 API 토큰 비용 시뮬레이터

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)

Gemini Robotics-ER 1.6 Monthly API Bill

$13.50 / month

$2.5/1M in · $10/1M 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 $12.68 / month (94%) under this workload.16.5x Savings

Gemini Robotics-ER 1.6 · Strengths & Key Advantages

Google의 첨단 Embodied Spatial Vision-Language-Action (VLA) Model 아키텍처와 최대 131K 토큰 컨텍스트 지원
경쟁력 있는 API 가격(입력 $2.5/1M, 출력 $10/1M)과 빠른 응답 속도
코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘

Considerations & Trade-offs

대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Gemini 3.1 Flash-Lite · Strengths & Key Advantages

Google의 첨단 Compact efficient multimodal transformer 아키텍처와 최대 1.0M 토큰 컨텍스트 지원
경쟁력 있는 API 가격(입력 $0.15/1M, 출력 $0.6/1M)과 빠른 응답 속도
코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘

Considerations & Trade-offs

대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Token Use & Task Economics

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

Metric	Gemini Robotics-ER 1.6	Gemini 3.1 Flash-Lite
Cost per Task Estimated USD to finish one task	$1.80	$0.38
Output Tokens / Task Tokens generated per task	12K	14K
Reasoning Tokens / Task Thinking tokens burned per task	6K	5K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Feature	Gemini Robotics-ER 1.6	Gemini 3.1 Flash-Lite
Official API Model ID (SDK)	models/gemini-robotics-er-1-6	models/gemini-3-1-flash-lite
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-04	2026-03
Knowledge Cutoff	February 2026	November 2025
Architecture	Embodied Spatial Vision-Language-Action (VLA) 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 ($2.5/1M in) across full 131K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.5/1M with configurable TTL	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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	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	Google DeepMind Frontier Safety Framework Verified	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

Gemini Robotics-ER 1.6 vs Gemini 3.1 Flash-Lite: 핵심 요약 문답 (FAQ)

Which is more intelligent, Gemini Robotics-ER 1.6 or Gemini 3.1 Flash-Lite?

Gemini Robotics-ER 1.6 is more intelligent. It scores 82 compared with 60 on the composite Intelligence Index.

Which model is faster?

Gemini 3.1 Flash-Lite is faster, generating 250 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

Gemini 3.1 Flash-Lite is cheaper at $0.11 per 1M tokens blended, compared with $1.85 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.1 Flash-Lite has lower latency, with a time to first token of 0.27s compared with 0.70s.

Which model has a larger context window?

Gemini 3.1 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 131.1K tokens.

종합 평가

Verdict & Model Selection Recommendation: Choose Gemini Robotics-ER 1.6 if your priority is Autonomous warehouse robots, humanoid task planning, and physical laboratory automation. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.1 Flash-Lite if your workflow prioritizes High-throughput microservices, translation, and large-scale document parsing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

자주 묻는 질문 (FAQ)

How does Gemini Robotics-ER 1.6 compare to Gemini 3.1 Flash-Lite in API token pricing and cost per token?+

Gemini Robotics-ER 1.6 costs $2.50 per 1M input tokens and $10.00 per 1M output tokens, whereas Gemini 3.1 Flash-Lite costs $0.15 input and $0.60 output. Overall, Gemini 3.1 Flash-Lite 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: Gemini Robotics-ER 1.6 or Gemini 3.1 Flash-Lite?+

Gemini Robotics-ER 1.6 supports a context window of 131K tokens (~98k words), while Gemini 3.1 Flash-Lite supports 1,049K tokens (~786k words). Therefore, Gemini 3.1 Flash-Lite can process more textual data and document context in a single prompt.

Which AI model is better for software engineering and coding: Gemini Robotics-ER 1.6 or Gemini 3.1 Flash-Lite?+

Gemini Robotics-ER 1.6 scores at the frontier of coding benchmarks, while Gemini 3.1 Flash-Lite scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini Robotics-ER 1.6 provides robust code generation and tool calling.

What is the difference in reasoning capability and extended thinking between Gemini Robotics-ER 1.6 and Gemini 3.1 Flash-Lite?+

Gemini Robotics-ER 1.6 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 3.1 Flash-Lite provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between Gemini Robotics-ER 1.6 and Gemini 3.1 Flash-Lite?+

Gemini Robotics-ER 1.6 generates approximately 90 tokens per second, while Gemini 3.1 Flash-Lite delivers 250 tokens per second. In latency-sensitive production environments, Gemini 3.1 Flash-Lite provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining Gemini Robotics-ER 1.6 and Gemini 3.1 Flash-Lite to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.1 Flash-Lite to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini Robotics-ER 1.6. This tiered routing pattern cuts overall API bills by up to 70%.

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)

Gemini Robotics-ER 1.6 vs Gemini 3.1 Flash-Lite

원하는 AI 모델 선택 및 실시간 비교

Gemini Robotics-ER 1.6

Gemini 3.1 Flash-Lite

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

Gemini Robotics-ER 1.6 · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 3.1 Flash-Lite · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

Gemini Robotics-ER 1.6 vs Gemini 3.1 Flash-Lite: 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

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