Qwen3.7 Plus vs Qwen3.7 Max
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.7 Plus by Alibaba and Qwen3.7 Max 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, Qwen3.7 Plus holds a notable pricing advantage for token ingestion. On output generation, Qwen3.7 Plus 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 Plus 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 Plus
高度な推論モデルAlibaba が提供する Qwen3.7 Plus は、最大 1M トークンのコンテキストウィンドウを備え、入力 $0.8/100万トークン・出力 $3.2/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.80 / 1M
$3.20 / 1M
1M tokens
68%
Qwen3.7 Max
高度な推論モデルAlibaba が提供する Qwen3.7 Max は、最大 1M トークンのコンテキストウィンドウを備え、入力 $1.8/100万トークン・出力 $7.2/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$1.80 / 1M
$7.20 / 1M
1M tokens
73.5%
ひと目でわかる主な違い
- Qwen3.7 Plus is 2.2x cheaper for API usage (input + output tokens combined).
- Qwen3.7 Plus is faster at 92 tok/s vs 80 tok/s.
- Qwen3.7 Max leads on SWE-bench coding (73.5% vs 68.0%).
| Feature | Qwen3.7 Plus | Qwen3.7 Max |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 89 | 92 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.80 / 1M | $1.80 / 1M |
Output Price Cost per million output tokens | $3.20 / 1M | $7.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.54 / 1M | $1.21 / 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 | 16K | 32K |
Throughput Speed Tokens generated per second | 92 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | 68% | 73.5% |
GPQA Diamond Graduate-level reasoning | 75% | 78.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.08 / 1M | $0.18 / 1M |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.7 Plus · Strengths & Key Advantages
- Alibaba の高度な High-Value Multimodal Foundation Transformer アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.8/1M、出力 $3.2/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Qwen3.7 Max · Strengths & Key Advantages
- Alibaba の高度な Next-Generation Deep Frontier Reasoning Transformer アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $1.8/1M、出力 $7.2/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.7 Plus | Qwen3.7 Max |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.24 | $0.38 |
Output Tokens / Task Tokens generated per task | 26K | 32K |
Reasoning Tokens / Task Thinking tokens burned per task | 9K | 14K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.7 Plus | Qwen3.7 Max |
|---|---|---|
| Official API Model ID (SDK) | qwen3-7-plus-instruct | qwen3-7-max-instruct |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2026-06 | 2026-05 |
| Knowledge Cutoff | April 2026 | April 2026 |
| Architecture | High-Value Multimodal Foundation Transformer | Next-Generation Deep Frontier Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.8/1M in) across full 1000K window | Flat rate ($1.8/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.08/1M tokens | Cached input at $0.18/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| 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 | 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, vision | text |
| Output Modalities | text | text |
Qwen3.7 Plus vs Qwen3.7 Max: よくある質問 (FAQ)
Which is more intelligent, Qwen3.7 Plus or Qwen3.7 Max?
Qwen3.7 Max is more intelligent. It scores 92 compared with 89 on the composite Intelligence Index.
Which model is faster?
Qwen3.7 Plus is faster, generating 92 tokens per second compared with 80 tokens per second.
Which model is cheaper to run?
Qwen3.7 Plus is cheaper at $0.54 per 1M tokens blended, compared with $1.21 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.7 Plus has lower latency, with a time to first token of 0.44s compared with 0.58s.
総合評価
Verdict & Model Selection Recommendation: Choose Qwen3.7 Plus if your priority is Large-scale multimodal codebases, video document parsing, and enterprise assistants. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3.7 Max if your workflow prioritizes Autonomous engineering agents, enterprise legal/financial due diligence, and scientific research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Qwen3.7 Plus compare to Qwen3.7 Max in API token pricing and cost per token?+
Qwen3.7 Plus costs $0.80 per 1M input tokens and $3.20 per 1M output tokens, whereas Qwen3.7 Max costs $1.80 input and $7.20 output. Overall, Qwen3.7 Plus 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 Plus or Qwen3.7 Max?+
Both Qwen3.7 Plus and Qwen3.7 Max 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 Plus or Qwen3.7 Max?+
Qwen3.7 Plus scores 68% on SWE-bench Verified while Qwen3.7 Max scores 73.5%. For autonomous software engineering and multi-file debugging, Qwen3.7 Max holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3.7 Plus and Qwen3.7 Max?+
Qwen3.7 Plus features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.7 Max provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3.7 Plus and Qwen3.7 Max?+
Qwen3.7 Plus generates approximately 92 tokens per second, while Qwen3.7 Max delivers 80 tokens per second. In latency-sensitive production environments, Qwen3.7 Plus provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.7 Plus and Qwen3.7 Max to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.7 Plus 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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs