Qwen3.6 Max Preview vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3.6 Max Preview by Alibaba against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
Qwen3.6 Max Preview
심층 추론 모델Alibaba의 Qwen3.6 Max Preview 모델은 262K 토큰 컨텍스트 윈도우와 입력 $1.5/1M 토큰, 출력 $6/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$1.50 / 1M
$6.00 / 1M
262.1K tokens
70%
GPT-6 Astra
심층 추론 모델OpenAI의 GPT-6 Astra 모델은 1M 토큰 컨텍스트 윈도우와 입력 $10.0/1M 토큰, 출력 $50.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
한눈에 보는 핵심 차이점
- Qwen3.6 Max Preview is 7.8x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 262K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 75 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 70.0%).
| Feature | Qwen3.6 Max Preview | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 90 | 96 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $1.50 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.01 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 75 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 70% | 82.4% |
GPQA Diamond Graduate-level reasoning | 76% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.15 / 1M | $1.00 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
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.
Qwen3.6 Max Preview · Strengths & Key Advantages
- Alibaba의 첨단 High-Scale Frontier Agentic Transformer 아키텍처와 최대 262K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $1.5/1M, 출력 $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
GPT-6 Astra · Strengths & Key Advantages
- OpenAI의 첨단 Omni-Adaptive Autoregressive with Dynamic CoT 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $10.0/1M, 출력 $50.0/1M)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 | Qwen3.6 Max Preview | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.32 | $2.15 |
Output Tokens / Task Tokens generated per task | 28K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 11K | 32K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.6 Max Preview | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen3-6-max-preview-instruct | gpt-6-astra |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | March 2026 | June 2026 |
| Architecture | High-Scale Frontier Agentic Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.5/1M in) across full 262K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.15/1M tokens | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| 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 | Alibaba Production Safety & Alignment Verified | OpenAI Preparedness Framework Audited & System Card Published |
| 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, audio |
| Output Modalities | text | text, audio |
Qwen3.6 Max Preview vs GPT-6 Astra: 핵심 요약 문답 (FAQ)
Which is more intelligent, Qwen3.6 Max Preview or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 90 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen3.6 Max Preview is cheaper at $1.01 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.6 Max Preview has lower latency, with a time to first token of 0.54s compared with 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 262.1K tokens.
종합 평가
Choose Qwen3.6 Max Preview for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 alibaba 기반 프로덕션 파이프라인, or choose GPT-6 Astra for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 openai 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: Qwen3.6 Max Preview or GPT-6 Astra?+
Qwen3.6 Max Preview costs $1.5/1M input and $6/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Qwen3.6 Max Preview or GPT-6 Astra?+
Qwen3.6 Max Preview supports 262K tokens whereas GPT-6 Astra supports 272K tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- 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
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed Comparison비교
- Aya Expanse 8B vs Command R+: Benchmarks, Pricing & Speed Comparison비교
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: 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: Alibaba & OpenAI Official Documentation