Nex-N2-Pro vs Qwen3.6 Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
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.
بدّل وقارن بين أي نموذجين
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Nex-N2-Pro
نموذج استدلال عميقنموذج Nex-N2-Pro المقدم من Nex AGI يوفر نافذة سياق تصل إلى 262K رمز، بتكلفة $0.4 لكل مليون رمز إدخال و$1.6 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.40 / 1M
$1.60 / 1M
262.1K tokens
N/A
Qwen3.6 Flash
نموذج استدلال عميقنموذج Qwen3.6 Flash المقدم من Alibaba يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $0.08 لكل مليون رمز إدخال و$0.32 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.08 / 1M
$0.32 / 1M
1M tokens
N/A
أهم الفروقات في لمحة
- 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 |
من يتفوق في كل مجال؟
At-a-glance decision frameworkEvaluations ongoing
نتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nex-N2-Pro · Strengths & Key Advantages
- بنية Mid-Tier Reasoning Foundation Transformer متطورة من Nex AGI مع سعة سياق تبلغ 262K رمز
- كفاءة عالية في التكلفة ($0.4/1M إدخال و$1.6/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Qwen3.6 Flash · Strengths & Key Advantages
- بنية High-Efficiency Multimodal Vision Transformer متطورة من Alibaba مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($0.08/1M إدخال و$0.32/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
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 |
المواصفات التقنية
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: إجابات سريعة (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.
الحكم النهائي
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.
الأسئلة الشائعة (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
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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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs