Qwen3.6 Flash vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3.6 Flash by Alibaba against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Qwen3.6 Flash
原生多模态视觉模型Alibaba 推出的 Qwen3.6 Flash 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 1M 上下文窗口、145 Token/秒生成速度与 81/100 综合智能指数（每百万 Token $0.08 输入 / $0.32 输出）。
$0.08 / 1M
$0.32 / 1M
1M tokens
N/A
GPT-6 Astra
原生语音与音频大模型OpenAI 推出的 GPT-6 Astra 是一款专注于超自然语音合成、实时音频理解与低延迟语音交互的大模型，具备 272K 上下文窗口、100 Token/秒生成速度与 96/100 综合智能指数（每百万 Token $10 输入 / $50 输出）。
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
核心差异一览
- Qwen3.6 Flash is 146.4x cheaper for API usage (input + output tokens combined).
- Qwen3.6 Flash has a larger context window (1M vs 272K tokens).
- Qwen3.6 Flash is faster at 145 tok/s vs 100 tok/s.
| Feature | Qwen3.6 Flash | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 96 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.08 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.32 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 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 | 145 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | 61.5% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $1.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.6 Flash · Strengths & Key Advantages
- 提供 1M 上下文窗口与最高 16K 输出容量，核心基准表现优异（GPQA Diamond 61.5% · MMLU-Pro 78%）
- 高达 145 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 310 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.08 输入 / $0.32 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
GPT-6 Astra · Strengths & Key Advantages
- 提供 272K 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 82.4% · GPQA Diamond 80.9%）
- 高达 100 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 950 毫秒
- 极具竞争力的 API 定价（每百万 Token $10 输入 / $50 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.6 Flash | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.15 |
Output Tokens / Task Tokens generated per task | 16K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 32K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.6 Flash | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen3-6-flash-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-Efficiency Multimodal Vision Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.08/1M in) across full 1000K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.008/1M tokens | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · Vision (~258–765 tokens per image tile) | 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, vision | text, vision, audio |
| Output Modalities | text | text, audio |
Qwen3.6 Flash vs GPT-6 Astra: 常见问题解答 (FAQ)
Which is more intelligent, Qwen3.6 Flash or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 81 on the composite Intelligence Index.
Which model is faster?
Qwen3.6 Flash is faster, generating 145 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Qwen3.6 Flash is cheaper at $0.05 per 1M tokens blended, compared with $7.70 (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 0.95s.
Which model has a larger context window?
Qwen3.6 Flash has a larger context window, supporting 1M tokens compared with 272K tokens.
综合结论
Choose Qwen3.6 Flash for 原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。, or choose GPT-6 Astra for 专注于超自然语音合成、实时音频理解与低延迟语音交互的任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。.
常见问题 (FAQ)
Which is cheaper: Qwen3.6 Flash or GPT-6 Astra?+
Qwen3.6 Flash costs $0.08/1M input and $0.32/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Qwen3.6 Flash or GPT-6 Astra?+
Qwen3.6 Flash supports 1M 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.
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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.
数据来源: Alibaba & OpenAI Official Documentation