Qwen3.8 Max Preview vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.8 Max Preview by Alibaba and GPT-6 Astra by OpenAI 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, Qwen3.8 Max Preview holds a notable pricing advantage for token ingestion. On output generation, Qwen3.8 Max Preview is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3.8 Max Preview provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, GPT-6 Astra 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.
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Qwen3.8 Max Preview
开源权重模型Alibaba 推出的 Qwen3.8 Max Preview 人工智能模型，具备 1M 上下文窗口，输入价格为 $2.0/百万 Token，输出价格为 $6.0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
GPT-6 Astra
深度推理模型OpenAI 推出的 GPT-6 Astra 人工智能模型，具备 1M 上下文窗口，输入价格为 $10.0/百万 Token，输出价格为 $50.0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
核心差异一览
- Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen3.8 Max Preview has a larger context window (1M vs 272K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 60 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 75.2%).
| Feature | Qwen3.8 Max Preview | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 96 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 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 | 32K | 128K |
Throughput Speed Tokens generated per second | 60 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 75.2% | 82.4% |
GPQA Diamond Graduate-level reasoning | 79.5% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework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.
Qwen3.8 Max Preview · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Colossus-Scale Open Sparse Mixture-of-Experts 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $2.0/1M，输出 $6.0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
GPT-6 Astra · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 Omni-Adaptive Autoregressive with Dynamic CoT 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $10.0/1M，输出 $50.0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.8 Max Preview | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 35K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 32K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.8 Max Preview | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen3-8-max-preview-instruct | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | April 2026 | June 2026 |
| Architecture | Colossus-Scale Open Sparse Mixture-of-Experts | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 2.4T total (95B active MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 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 |
Qwen3.8 Max Preview vs GPT-6 Astra: 常见问题解答 (FAQ)
Which is more intelligent, Qwen3.8 Max Preview or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 93 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.8 Max Preview has lower latency, with a time to first token of 0.68s compared with 0.95s.
Which model has a larger context window?
Qwen3.8 Max Preview has a larger context window, supporting 1M tokens compared with 272K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Qwen3.8 Max Preview if your priority is National research centers, sovereign enterprise clouds, and frontier AI research. Its performance in demanding tasks justifies its operational token cost. Choose GPT-6 Astra if your workflow prioritizes Autonomous browser/computer workflows, real-time voice agents, and high-difficulty mathematical reasoning. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Qwen3.8 Max Preview compare to GPT-6 Astra in API token pricing and cost per token?+
Qwen3.8 Max Preview costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas GPT-6 Astra costs $10.00 input and $50.00 output. Overall, Qwen3.8 Max Preview 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: Qwen3.8 Max Preview or GPT-6 Astra?+
Qwen3.8 Max Preview supports a context window of 1,000K tokens (~750k words), while GPT-6 Astra supports 272K tokens (~204k words). Therefore, Qwen3.8 Max Preview can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3.8 Max Preview or GPT-6 Astra?+
Qwen3.8 Max Preview scores 75.2% on SWE-bench Verified while GPT-6 Astra scores 82.4%. For autonomous software engineering and multi-file debugging, GPT-6 Astra holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3.8 Max Preview and GPT-6 Astra?+
Qwen3.8 Max Preview features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-6 Astra provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3.8 Max Preview and GPT-6 Astra?+
Qwen3.8 Max Preview generates approximately 60 tokens per second, while GPT-6 Astra delivers 100 tokens per second. In latency-sensitive production environments, GPT-6 Astra provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.8 Max Preview and GPT-6 Astra to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.8 Max Preview to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-6 Astra. 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.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs