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Head-to-Head Model Comparison

Qwen3.6 Flash vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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Compare Qwen3.6 Flash by Alibaba against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.

1:1 모델 맞대결 비교 (VS)

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Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:
Alibaba

Qwen3.6 Flash

네이티브 멀티모달 AI

Alibaba의 Qwen3.6 Flash 모델은 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 145 토큰의 생성 속도, 종합 지능 지수 81/100점(1M 토큰당 입력 $0.08 / 출력 $0.32)을 제공합니다.

Intelligence Index81 / 100
Input Token:

$0.08 / 1M

Output Token:

$0.32 / 1M

Context Window:

1M tokens

SWE-bench Verified:

N/A

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

  • Qwen3.6 Flash is 28.9x cheaper for API usage (input + output tokens combined).
  • Qwen3.6 Flash is faster at 145 tok/s vs 115 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen3.6 FlashGemini 4 Argon
Intelligence Index
Composite reasoning & knowledge score (0-100)
8197
Provider
AlibabaGoogle
Input Price
Cost per million input tokens
$0.08 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$0.32 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.05 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
16K1000K
Throughput Speed
Tokens generated per second
145 tok/s115 tok/s
SWE-bench Verified
Real-world software engineering
N/A81.9%
GPQA Diamond
Graduate-level reasoning
61.5%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

Evaluations ongoing

Cost
Qwen3.6 Flash
28.9x cheaper monthly runtime
Speed
Qwen3.6 Flash
+26% faster generation
Context
Tied
1M tokens window

검증된 벤치마크 점수 비교

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

Qwen3.6 FlashGemini 4 Argon
SWE-bench Verified (Autonomous Coding)

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

N/Avs81.9%
GPQA Diamond (Doctorate-Level Science & Math)

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

61.5%vs81.5%
MMLU-Pro (Multi-task Language Understanding)

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

78%vs90.8%
LMSYS Chatbot Arena Elo

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

1320ptsvs1525pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs18.9%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs57.4%
SciCode (Scientific Computing)

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

N/Avs56.4%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs80.5%
AIME (Advanced Math Competition)

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

N/Avs94.8%
Long-Context Reasoning

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

N/Avs96.4%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

145 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
Qwen3.6 Flash0.31s
Gemini 4 Argon0.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen3.6 Flash3.76s
Gemini 4 Argon5.20s

실시간 API 토큰 비용 시뮬레이터

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)
Qwen3.6 Flash Monthly API Bill
$0.42 / month
$0.08/1M in · $0.32/1M out
Gemini 4 Argon Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Qwen3.6 Flash saves approximately $11.73 / month (97%) under this workload.28.9x Savings

Qwen3.6 Flash · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 16K 출력 토큰 생성 지원 (GPQA Diamond 61.5% · MMLU-Pro 78%)
  • 초당 145 토큰의 빠른 처리 속도와 310ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $0.08 / 출력 $0.32)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
  • 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.

MetricQwen3.6 FlashGemini 4 Argon
Cost per Task
Estimated USD to finish one task
$0.03$2.45
Output Tokens / Task
Tokens generated per task
16K75K
Reasoning Tokens / Task
Thinking tokens burned per task
2K36K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureQwen3.6 FlashGemini 4 Argon
Official API Model ID (SDK)qwen3-6-flash-instructmodels/gemini-4-argon
API ProtocolAlibaba REST API (OpenAI-Compatible)Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-042026-09
Knowledge CutoffMarch 2026June 2026
ArchitectureHigh-Efficiency Multimodal Vision TransformerPathways Multimodal Sparse Mixture-of-Experts
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.08/1M in) across full 1000K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.008/1M tokensImplicit + Explicit Context Caching at $0.1/1M with configurable TTL
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 & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationVertex AI Supervised Adapter Tuning & Context Distillation
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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
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 ClassificationAlibaba Production Safety & Alignment VerifiedGoogle DeepMind Frontier Safety Framework Verified
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, audio, video
Output Modalitiestexttext

Qwen3.6 Flash vs Gemini 4 Argon: 핵심 요약 문답 (FAQ)

Which is more intelligent, Qwen3.6 Flash or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 81 on the composite Intelligence Index.

Which model is faster?

Qwen3.6 Flash is faster, generating 145 tokens per second compared with 115 tokens per second.

Which model is cheaper to run?

Qwen3.6 Flash is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3.6 Flash has lower latency, with a time to first token of 0.31s compared with 0.85s.

종합 평가

Choose Qwen3.6 Flash for 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Gemini 4 Argon for 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..

자주 묻는 질문 (FAQ)

Which is cheaper: Qwen3.6 Flash or Gemini 4 Argon?+

Qwen3.6 Flash costs $0.08/1M input and $0.32/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: Qwen3.6 Flash or Gemini 4 Argon?+

Qwen3.6 Flash supports 1M tokens whereas Gemini 4 Argon 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.

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.

공식 출처: Alibaba & Google Official Documentation