Qwen Max vs Qwen-VL Max
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen Max dari Alibaba dan Qwen-VL Max dari Alibaba adalah dua model AI unggulan. Qwen Max menawarkan jendela konteks 32.8K token dengan harga $1.60/$6.40 per 1 juta token, sedangkan Qwen-VL Max menyediakan konteks 131.1K token seharga $0.70/$2.80 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
Ganti & Bandingkan Model Apa Saja
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Qwen Max
Model Generasi KlasikQwen Max dari Alibaba adalah model AI yang dirancang untuk asisten percakapan, peringkasan dokumen, dan beban kerja bisnis umum, menghadirkan jendela konteks 33K, kecepatan 65 token/detik, indeks kecerdasan 78/100, serta tarif $1.6/$6.4 per
$1.60 / 1M
$6.40 / 1M
32.8K tokens
32.5%
Qwen-VL Max
AI Multimodal AsliQwen-VL Max dari Alibaba adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 131K, kecepatan 75 token/detik, indeks kecerdasan 76/100, serta tarif $0.7
$0.70 / 1M
$2.80 / 1M
131.1K tokens
N/A
Perbedaan Utama Sekilas
- Qwen-VL Max is 2.3x cheaper for API usage (input + output tokens combined).
- Qwen-VL Max has a larger context window (131K vs 33K tokens).
- Qwen-VL Max is faster at 75 tok/s vs 65 tok/s.
| Feature | Qwen Max | Qwen-VL Max |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 76 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $1.60 / 1M | $0.70 / 1M |
Output Price Cost per million output tokens | $6.40 / 1M | $2.80 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.07 / 1M | $0.47 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 65 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 32.5% | N/A |
GPQA Diamond Graduate-level reasoning | 48% | 46.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.16 / 1M | $0.07 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor 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.
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.
Qwen Max · Strengths & Key Advantages
- Jendela konteks 33K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 32.5% · GPQA Diamond 48%)
- Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 580 ms
- Skema harga API kompetitif (tarif $1.6/$6.4 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
Qwen-VL Max · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (GPQA Diamond 46.5% · MMLU-Pro 76%)
- Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 650 ms
- Skema harga API kompetitif (tarif $0.7/$2.8 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 | Qwen Max | Qwen-VL Max |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.18 | $0.15 |
Output Tokens / Task Tokens generated per task | 18K | 16K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 5K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen Max | Qwen-VL Max |
|---|---|---|
| Official API Model ID (SDK) | qwen-max-instruct | qwen-vl-max-instruct |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2024-04 | 2024-04 |
| Knowledge Cutoff | September 2023 | December 2023 |
| Architecture | Dense Autoregressive Transformer with Multi-Head Attention | Multimodal Vision-Language Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.6/1M in) across full 33K window | Flat rate ($0.7/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.16/1M tokens | Cached input at $0.07/1M tokens |
| 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 | API Prompt Engineering & RAG Distillation |
| 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) | 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, vision |
| Output Modalities | text | text |
Qwen Max vs Qwen-VL Max: Jawaban Cepat (FAQ)
Which is more intelligent, Qwen Max or Qwen-VL Max?
Qwen Max is more intelligent. It scores 78 compared with 76 on the composite Intelligence Index.
Which model is faster?
Qwen-VL Max is faster, generating 75 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Qwen-VL Max is cheaper at $0.47 per 1M tokens blended, compared with $1.07 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen Max has lower latency, with a time to first token of 0.58s compared with 0.65s.
Which model has a larger context window?
Qwen-VL Max has a larger context window, supporting 131.1K tokens compared with 32.8K tokens.
Kesimpulan
Baik Qwen Max (Sangat cocok untuk asisten percakapan, peringkasan dokumen, dan beban kerja bisnis umum, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen-VL Max (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen Max unggul dalam penalaran dan pemrograman, Qwen-VL Max memberikan kecepatan generasi token lebih tinggi, dan Qwen-VL Max lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Qwen Max atau Qwen-VL Max?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen Max memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Qwen Max vs Qwen-VL Max?+
Qwen Max mengenakan biaya $1.60 / $6.40 per 1M token input/output, sedangkan Qwen-VL Max sebesar $0.70 / $2.80. Secara keseluruhan, Qwen-VL Max lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Qwen Max mendukung konteks 32.8K token dibandingkan 131.1K token pada Qwen-VL Max, sementara Qwen-VL 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
- Qwen Max vs Reka Core: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Qwen-VL Max vs Qwen Plus: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Qwen-VL Max vs Reka Core: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- QwQ Plus vs Qwen Max: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aion-3.0 vs Aion-Mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aion-3.0 vs Gemini 3.6 Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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).