Head-to-Head Model Comparison

DeepSeek V4.1-Flash vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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DeepSeek V4.1-FlashProfile

Gemini 4 ArgonProfile

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DeepSeek

DeepSeek V4.1-Flash

오픈 웨이트 파운데이션 모델

DeepSeek의 DeepSeek V4.1-Flash 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 110 토큰의 생성 속도, 종합 지능 지수 58/100점(1M 토큰당 입력 $0.3 / 출력 $1.2)을 제공합니다.

Intelligence Index58 / 100

Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

74.2%

Google

Gemini 4 Argon

네이티브 멀티모달 AI

Google의 Gemini 4 Argon 모델은 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 115 토큰의 생성 속도, 종합 지능 지수 97/100점(1M 토큰당 입력 $2 / 출력 $10)을 제공합니다.

Intelligence Index97 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

한눈에 보는 핵심 차이점

DeepSeek V4.1-Flash is 7.9x cheaper for API usage (input + output tokens combined).
Gemini 4 Argon is faster at 115 tok/s vs 110 tok/s.
Gemini 4 Argon leads on SWE-bench coding (81.9% vs 74.2%).

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

Feature	DeepSeek V4.1-Flash	Gemini 4 Argon
Intelligence Index Composite reasoning & knowledge score (0-100)	58	97
Provider	DeepSeek	Google
Input Price Cost per million input tokens	$0.30 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$1.20 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.18 / 1M	$1.47 / 1M
Context Window Maximum input + output tokens	1M tokens~1,500 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	384K	1000K
Throughput Speed Tokens generated per second	110 tok/s	115 tok/s
SWE-bench Verified Real-world software engineering	74.2%	81.9%
GPQA Diamond Graduate-level reasoning	90.9%	81.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.10 / 1M

부문별 우위 모델

At-a-glance decision framework

Coding

Gemini 4 Argon

+7.7% SWE-bench lead

Cost

DeepSeek V4.1-Flash

7.9x cheaper monthly runtime

Speed

Gemini 4 Argon

+5% faster generation

Context

Tied

1M tokens window

검증된 벤치마크 점수 비교

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

DeepSeek V4.1-FlashGemini 4 Argon

SWE-bench Verified (Autonomous Coding)

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

74.2%vs81.9%

GPQA Diamond (Doctorate-Level Science & Math)

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

90.9%vs81.5%

MMLU-Pro (Multi-task Language Understanding)

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

89.5%vs90.8%

LMSYS Chatbot Arena Elo

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

1455ptsvs1525pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

16.8%vs18.9%

Terminal-Bench (Agentic Coding & Terminal Use)

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

31.2%vs57.4%

SciCode (Scientific Computing)

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

48%vs56.4%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

73%vs80.5%

AIME (Advanced Math Competition)

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

92.4%vs94.8%

Long-Context Reasoning

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

93%vs96.4%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs115 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

DeepSeek V4.1-Flash0.46s

Gemini 4 Argon0.85s

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

DeepSeek V4.1-Flash5.01s

Gemini 4 Argon5.20s

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)

DeepSeek V4.1-Flash Monthly API Bill

$1.54 / month

$0.3/1M in · $1.2/1M out

Gemini 4 Argon Monthly API Bill

$12.15 / month

$2/1M in · $10/1M out

DeepSeek V4.1-Flash saves approximately $10.61 / month (87%) under this workload.7.9x Savings

DeepSeek V4.1-Flash · Strengths & Key Advantages

1M 컨텍스트 윈도우 및 최대 384K 출력 토큰 생성 지원 (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
초당 110 토큰의 빠른 처리 속도와 460ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (763B total (8B prefill / 16B decode ))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능

Considerations & Trade-offs

비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Gemini 4 Argon · Strengths & Key Advantages

1M 컨텍스트 윈도우 및 최대 1M 출력 토큰 생성 지원 (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
초당 115 토큰의 빠른 처리 속도와 850ms에 불과한 첫 토큰 지연 시간(TTFT)
합리적인 API 요금 체계(1M 토큰당 입력 $2 / 출력 $10)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원

Considerations & Trade-offs

독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Token Use & Task Economics

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

Metric	DeepSeek V4.1-Flash	Gemini 4 Argon
Cost per Task Estimated USD to finish one task	$0.27	$2.45
Output Tokens / Task Tokens generated per task	58K	75K
Reasoning Tokens / Task Thinking tokens burned per task	28K	36K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Feature	DeepSeek V4.1-Flash	Gemini 4 Argon
Official API Model ID (SDK)	deepseek-reasoner	models/gemini-4-argon
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-09	2026-09
Knowledge Cutoff	June 2026	June 2026
Architecture	Causal Encoder-Decoder (20 enc + 20 dec layers)	Pathways Multimodal Sparse Mixture-of-Experts
Parameters	763B total (8B prefill / 16B decode active)	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.3/1M in) across full 1000K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix caching at $0.006/1M (no extra cache-write fee)	Implicit + Explicit Context Caching at $0.1/1M with configurable TTL
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Google DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision	text, vision, audio, video
Output Modalities	text	text

DeepSeek V4.1-Flash vs Gemini 4 Argon: 핵심 요약 문답 (FAQ)

Which is more intelligent, DeepSeek V4.1-Flash or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 58 on the composite Intelligence Index.

Which model is faster?

Gemini 4 Argon is faster, generating 115 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

DeepSeek V4.1-Flash is cheaper at $0.18 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek V4.1-Flash has lower latency, with a time to first token of 0.46s compared with 0.85s.

종합 평가

DeepSeek V4.1-Flash(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Gemini 4 Argon(텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Gemini 4 Argon가 앞서며, 토큰 생성 속도에서는 Gemini 4 Argon가 빠르고, 대규모 API 운영 비용 면에서는 DeepSeek V4.1-Flash가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 DeepSeek V4.1-Flash와 Gemini 4 Argon 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Gemini 4 Argon가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

DeepSeek V4.1-Flash와 Gemini 4 Argon의 API 요금 차이는 얼마인가요?+

DeepSeek V4.1-Flash는 100만 토큰당 입력 $0.30 / 출력 $1.20이며, Gemini 4 Argon는 입력 $2.00 / 출력 $10.00입니다. 혼합 비용 기준으로는 DeepSeek V4.1-Flash가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 DeepSeek V4.1-Flash가 1M 토큰, Gemini 4 Argon가 1M 토큰를 지원하며, 출력 속도 면에서는 Gemini 4 Argon가 더 빠릅니다.

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)

DeepSeek V4.1-Flash vs Gemini 4 Argon

원하는 AI 모델 선택 및 실시간 비교

DeepSeek V4.1-Flash

Gemini 4 Argon

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

DeepSeek V4.1-Flash · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 4 Argon · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

DeepSeek V4.1-Flash vs Gemini 4 Argon: 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

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