Qwen3.7 Max vs Qwen3.8 Max Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.7 Max by Alibaba and Qwen3.8 Max Preview 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, Qwen3.8 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, both models provide an identical memory horizon with 1,000K tokens. In latency-sensitive production environments, Qwen3.7 Max 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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Qwen3.7 Max
深度推理模型Alibaba 推出的 Qwen3.7 Max 人工智能模型，具备 1M 上下文窗口，输入价格为 $1.8/百万 Token，输出价格为 $7.2/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$1.80 / 1M
$7.20 / 1M
1M tokens
73.5%
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%
核心差异一览
- Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen3.7 Max is faster at 80 tok/s vs 60 tok/s.
- Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 73.5%).
| Feature | Qwen3.7 Max | Qwen3.8 Max Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 92 | 93 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $1.80 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $7.20 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.21 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 32K | 32K |
Throughput Speed Tokens generated per second | 80 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 73.5% | 75.2% |
GPQA Diamond Graduate-level reasoning | 78.5% | 79.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.18 / 1M | $0.00 / 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.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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.
Qwen3.7 Max · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Next-Generation Deep Frontier Reasoning Transformer 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $1.8/1M，输出 $7.2/1M），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
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.7 Max | Qwen3.8 Max Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $0.00 |
Output Tokens / Task Tokens generated per task | 32K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 14K | 15K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.7 Max | Qwen3.8 Max Preview |
|---|---|---|
| Official API Model ID (SDK) | qwen3-7-max-instruct | qwen3-8-max-preview-instruct |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-05 | 2026-07 |
| Knowledge Cutoff | April 2026 | April 2026 |
| Architecture | Next-Generation Deep Frontier Reasoning Transformer | Colossus-Scale Open Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.4T total (95B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($1.8/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.18/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Alibaba Production Safety & Alignment Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Qwen3.7 Max vs Qwen3.8 Max Preview: 常见问题解答 (FAQ)
Which is more intelligent, Qwen3.7 Max or Qwen3.8 Max Preview?
Qwen3.8 Max Preview is more intelligent. It scores 93 compared with 92 on the composite Intelligence Index.
Which model is faster?
Qwen3.7 Max is faster, generating 80 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 $1.21 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.7 Max has lower latency, with a time to first token of 0.58s compared with 0.68s.
综合结论
Verdict & Model Selection Recommendation: Choose Qwen3.7 Max if your priority is Autonomous engineering agents, enterprise legal/financial due diligence, and scientific research. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3.8 Max Preview if your workflow prioritizes National research centers, sovereign enterprise clouds, and frontier AI research. 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 Qwen3.7 Max compare to Qwen3.8 Max Preview in API token pricing and cost per token?+
Qwen3.7 Max costs $1.80 per 1M input tokens and $7.20 per 1M output tokens, whereas Qwen3.8 Max Preview costs $0.00 input and $0.00 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: Qwen3.7 Max or Qwen3.8 Max Preview?+
Both Qwen3.7 Max and Qwen3.8 Max Preview offer an identical context window of 1,000K tokens (~750k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Qwen3.7 Max or Qwen3.8 Max Preview?+
Qwen3.7 Max scores 73.5% on SWE-bench Verified while Qwen3.8 Max Preview scores 75.2%. For autonomous software engineering and multi-file debugging, Qwen3.8 Max Preview holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3.7 Max and Qwen3.8 Max Preview?+
Qwen3.7 Max features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.8 Max Preview provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3.7 Max and Qwen3.8 Max Preview?+
Qwen3.7 Max generates approximately 80 tokens per second, while Qwen3.8 Max Preview delivers 60 tokens per second. In latency-sensitive production environments, Qwen3.7 Max provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.7 Max and Qwen3.8 Max Preview 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 Qwen3.7 Max. 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