Qwen3 Max vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3 Max by Alibaba against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Qwen3 Max
Model Penalaran MendalamQwen3-Max dari Alibaba adalah model AI canggih dengan jendela konteks 262K token serta harga API $2.0/1M token input dan $6.0/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$2.00 / 1M
$6.00 / 1M
262.1K tokens
62.5%
GPT-6.1 Sol
Model Penalaran MendalamGPT-6.1 Sol dari OpenAI adalah model AI canggih dengan jendela konteks 1M token serta harga API $2.0/1M token input dan $10.0/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Perbedaan Utama Sekilas
- Qwen3 Max is 1.4x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 262K tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 72 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 62.5%).
| Feature | Qwen3 Max | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 88 | 94 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $2.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.28 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 72 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 62.5% | 82.3% |
GPQA Diamond Graduate-level reasoning | 71.2% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.40 / 1M | $0.10 / 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.
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.
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 Max · Strengths & Key Advantages
- Arsitektur Advanced Dense/MoE Hybrid Frontier Transformer mutakhir dari Alibaba dengan dukungan konteks 262K token
- Efisiensi biaya API yang kompetitif ($2.0/1M input, $6.0/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
GPT-6.1 Sol · Strengths & Key Advantages
- Arsitektur Near-Astra unified reasoning model mutakhir dari OpenAI dengan dukungan konteks 1M token
- Efisiensi biaya API yang kompetitif ($2.0/1M input, $10.0/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3 Max | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $2.05 |
Output Tokens / Task Tokens generated per task | 25K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 10K | 36K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3 Max | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | qwen3-max-instruct | gpt-6-1-sol |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-09 | 2026-09 |
| Knowledge Cutoff | July 2025 | April 2026 |
| Architecture | Advanced Dense/MoE Hybrid Frontier Transformer | Near-Astra unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 262K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.4/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published |
| 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 |
Qwen3 Max vs GPT-6.1 Sol: Jawaban Cepat (FAQ)
Which is more intelligent, Qwen3 Max or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 88 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 72 tokens per second.
Which model is cheaper to run?
Qwen3 Max is cheaper at $1.28 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3 Max has lower latency, with a time to first token of 0.59s compared with 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Kesimpulan
Choose Qwen3 Max for otomasi perusahaan, pengembangan perangkat lunak, agen ai, dan alur kerja produksi berbasis alibaba, or choose GPT-6.1 Sol for otomasi perusahaan, pengembangan perangkat lunak, agen ai, dan alur kerja produksi berbasis openai.
Pertanyaan Umum (FAQ)
Which is cheaper: Qwen3 Max or GPT-6.1 Sol?+
Qwen3 Max costs $2/1M input and $6/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Qwen3 Max or GPT-6.1 Sol?+
Qwen3 Max supports 262K tokens whereas GPT-6.1 Sol supports 1M tokens.
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.
Sumber resmi: Alibaba & OpenAI Official Documentation