Nex-N2-Pro vs Qwen3.6 Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Nex-N2-Pro by Nex AGI and Qwen3.6 Flash by Alibaba represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Qwen3.6 Flash holds a notable pricing advantage for token ingestion. On output generation, Qwen3.6 Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3.6 Flash provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Qwen3.6 Flash leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Nex-N2-Pro
심층 추론 모델Nex AGI의 Nex-N2-Pro 모델은 262K 토큰 컨텍스트 윈도우와 입력 $0.4/1M 토큰, 출력 $1.6/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.40 / 1M
$1.60 / 1M
262.1K tokens
N/A
Qwen3.6 Flash
심층 추론 모델Alibaba의 Qwen3.6 Flash 모델은 1M 토큰 컨텍스트 윈도우와 입력 $0.08/1M 토큰, 출력 $0.32/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.08 / 1M
$0.32 / 1M
1M tokens
N/A
한눈에 보는 핵심 차이점
- Qwen3.6 Flash is 5.0x cheaper for API usage (input + output tokens combined).
- Qwen3.6 Flash has a larger context window (1M vs 262K tokens).
- Qwen3.6 Flash is faster at 145 tok/s vs 85 tok/s.
| Feature | Nex-N2-Pro | Qwen3.6 Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 81 |
Provider | Nex AGI | Alibaba |
Input Price Cost per million input tokens | $0.40 / 1M | $0.08 / 1M |
Output Price Cost per million output tokens | $1.60 / 1M | $0.32 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.27 / 1M | $0.05 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 16K |
Throughput Speed Tokens generated per second | 85 tok/s | 145 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 62% | 61.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $0.01 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nex-N2-Pro · Strengths & Key Advantages
- Nex AGI의 첨단 Mid-Tier Reasoning Foundation Transformer 아키텍처와 최대 262K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.4/1M, 출력 $1.6/1M)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
Qwen3.6 Flash · Strengths & Key Advantages
- Alibaba의 첨단 High-Efficiency Multimodal Vision Transformer 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.08/1M, 출력 $0.32/1M)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 | Nex-N2-Pro | Qwen3.6 Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.12 | $0.03 |
Output Tokens / Task Tokens generated per task | 18K | 16K |
Reasoning Tokens / Task Thinking tokens burned per task | 5K | 2K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Nex-N2-Pro | Qwen3.6 Flash |
|---|---|---|
| Official API Model ID (SDK) | nex-n2-pro | qwen3-6-flash-instruct |
| API Protocol | Nex AGI REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2026-06 | 2026-04 |
| Knowledge Cutoff | March 2026 | March 2026 |
| Architecture | Mid-Tier Reasoning Foundation Transformer | High-Efficiency Multimodal Vision Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.4/1M in) across full 262K window | Flat rate ($0.08/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.04/1M tokens | Cached input at $0.008/1M tokens |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | 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 | Nex AGI Production Safety & Alignment Verified | Alibaba Production Safety & Alignment 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 | text, vision |
| Output Modalities | text | text |
Nex-N2-Pro vs Qwen3.6 Flash: 핵심 요약 문답 (FAQ)
Which model is faster?
Qwen3.6 Flash is faster, generating 145 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Qwen3.6 Flash is cheaper at $0.05 per 1M tokens blended, compared with $0.27 (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 0.46s.
Which model has a larger context window?
Qwen3.6 Flash has a larger context window, supporting 1M tokens compared with 262.1K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose Nex-N2-Pro if your priority is Mid-sized enterprise agent automation, contract review, and business analytics. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3.6 Flash if your workflow prioritizes High-volume multimodal extraction, visual customer service bots, and real-time feeds. 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 Nex-N2-Pro compare to Qwen3.6 Flash in API token pricing and cost per token?+
Nex-N2-Pro costs $0.40 per 1M input tokens and $1.60 per 1M output tokens, whereas Qwen3.6 Flash costs $0.08 input and $0.32 output. Overall, Qwen3.6 Flash 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: Nex-N2-Pro or Qwen3.6 Flash?+
Nex-N2-Pro supports a context window of 262K tokens (~197k words), while Qwen3.6 Flash supports 1,000K tokens (~750k words). Therefore, Qwen3.6 Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nex-N2-Pro or Qwen3.6 Flash?+
Nex-N2-Pro scores at the frontier of coding benchmarks, while Qwen3.6 Flash scores competitively for software generation. For complex autonomous debugging and agentic engineering, Nex-N2-Pro provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Nex-N2-Pro and Qwen3.6 Flash?+
Nex-N2-Pro features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.6 Flash provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nex-N2-Pro and Qwen3.6 Flash?+
Nex-N2-Pro generates approximately 85 tokens per second, while Qwen3.6 Flash delivers 145 tokens per second. In latency-sensitive production environments, Qwen3.6 Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nex-N2-Pro and Qwen3.6 Flash to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.6 Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nex-N2-Pro. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Nex-N2-Pro vs Nex-N2-Mini: Benchmarks, Pricing & Speed Comparison비교
- Qwen3.6 Flash vs Qwen3.5 Plus: Benchmarks, Pricing & Speed Comparison비교
- Qwen3.6 Plus vs Qwen3.6 Flash: Benchmarks, Pricing & Speed Comparison비교
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed Comparison비교
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed Comparison비교
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed Comparison비교
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: GetInfoUs Independent AI Benchmarks & Official API Specs