Qwen3.7 Max vs Qwen3.6 Max Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.7 Max dari Alibaba dan Qwen3.6 Max Preview dari Alibaba adalah dua model AI unggulan. Qwen3.7 Max menawarkan jendela konteks 1M token dengan harga $1.80/$7.20 per 1 juta token, sedangkan Qwen3.6 Max Preview menyediakan konteks 262.1K token seharga $1.50/$6.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Qwen3.7 Max
Model Agen Otonom & AlatQwen3.7 Max dari Alibaba adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 1M, kecepatan 80 token/detik, indeks kecerdasan 92/100, serta
$1.80 / 1M
$7.20 / 1M
1M tokens
73.5%
Qwen3.6 Max Preview
Model Unggulan Garis DepanQwen3.6 Max Preview dari Alibaba adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 262K, kecepatan 75 token/detik, indeks kecerdasan 90/100, sert
$1.50 / 1M
$6.00 / 1M
262.1K tokens
70%
Perbedaan Utama Sekilas
- Qwen3.6 Max Preview is 1.2x cheaper for API usage (input + output tokens combined).
- Qwen3.7 Max has a larger context window (1M vs 262K tokens).
- Qwen3.7 Max is faster at 80 tok/s vs 75 tok/s.
- Qwen3.7 Max leads on SWE-bench coding (73.5% vs 70.0%).
| Feature | Qwen3.7 Max | Qwen3.6 Max Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 92 | 90 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $1.80 / 1M | $1.50 / 1M |
Output Price Cost per million output tokens | $7.20 / 1M | $6.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.21 / 1M | $1.01 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 32K | 16K |
Throughput Speed Tokens generated per second | 80 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 73.5% | 70% |
GPQA Diamond Graduate-level reasoning | 78.5% | 76% |
Cached Input Price Prompt caching discount per 1M tokens | $0.18 / 1M | $0.15 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.7 Max · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 73.5% · GPQA Diamond 78.5%)
- Kecepatan generasi tinggi 80 token/detik dengan latensi token pertama (TTFT) hanya 580 ms
- Skema harga API kompetitif (tarif $1.8/$7.2 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Qwen3.6 Max Preview · Strengths & Key Advantages
- Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 70% · GPQA Diamond 76%)
- Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 540 ms
- Skema harga API kompetitif (tarif $1.5/$6 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.7 Max | Qwen3.6 Max Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $0.32 |
Output Tokens / Task Tokens generated per task | 32K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 14K | 11K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.7 Max | Qwen3.6 Max Preview |
|---|---|---|
| Official API Model ID (SDK) | qwen3-7-max-instruct | qwen3-6-max-preview-instruct |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2026-05 | 2026-04 |
| Knowledge Cutoff | April 2026 | March 2026 |
| Architecture | Next-Generation Deep Frontier Reasoning Transformer | High-Scale Frontier Agentic 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.8/1M in) across full 1000K window | Flat rate ($1.5/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Cached input at $0.18/1M tokens | Cached input at $0.15/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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) | 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 | text |
| Output Modalities | text | text |
Qwen3.7 Max vs Qwen3.6 Max Preview: Jawaban Cepat (FAQ)
Which is more intelligent, Qwen3.7 Max or Qwen3.6 Max Preview?
Qwen3.7 Max is more intelligent. It scores 92 compared with 90 on the composite Intelligence Index.
Which model is faster?
Qwen3.7 Max is faster, generating 80 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 $1.21 (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 0.58s.
Which model has a larger context window?
Qwen3.7 Max has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Kesimpulan
Baik Qwen3.7 Max (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3.6 Max Preview (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3.7 Max unggul dalam penalaran dan pemrograman, Qwen3.7 Max memberikan kecepatan generasi token lebih tinggi, dan Qwen3.6 Max Preview lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Qwen3.7 Max atau Qwen3.6 Max Preview?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.7 Max memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Qwen3.7 Max vs Qwen3.6 Max Preview?+
Qwen3.7 Max mengenakan biaya $1.80 / $7.20 per 1M token input/output, sedangkan Qwen3.6 Max Preview sebesar $1.50 / $6.00. Secara keseluruhan, Qwen3.6 Max Preview lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Qwen3.7 Max mendukung konteks 1M token dibandingkan 262.1K token pada Qwen3.6 Max Preview, sementara Qwen3.7 Max unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Sumber resmi: Dokumentasi API resmi Alibaba & Alibaba serta evaluasi benchmark independen (2026).