Qwen-Omni Turbo vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen-Omni Turbo by Alibaba against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Qwen-Omni Turbo
Amiral Gemisi Öncü ModelAlibaba tarafından geliştirilen Qwen-Omni Turbo, 32K token bağlam penceresi ve 1M girdi başına $0.3, 1M çıktı başına $1.2 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.30 / 1M
$1.20 / 1M
32.8K tokens
N/A
GPT-6.1 Sol
Derin Akıl Yürütme ModeliOpenAI tarafından geliştirilen GPT-6.1 Sol, 1M token bağlam penceresi ve 1M girdi başına $2.0, 1M çıktı başına $10.0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Bir Bakışta Temel Farklar
- Qwen-Omni Turbo is 7.7x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 33K tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 90 tok/s.
| Feature | Qwen-Omni Turbo | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 74 | 94 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.30 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.20 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 90 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.10 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkEvaluations ongoing
Doğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen-Omni Turbo · Strengths & Key Advantages
- Alibaba imzalı gelişmiş End-to-End Omni-Modal Multimodal Transformer mimarisi ve 32K token bağlam kapasitesi
- Dengeli API maliyeti ($0.3/1M girdi, $1.2/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
GPT-6.1 Sol · Strengths & Key Advantages
- OpenAI imzalı gelişmiş Near-Astra unified reasoning model mimarisi ve 1M token bağlam kapasitesi
- Dengeli API maliyeti ($2.0/1M girdi, $10.0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen-Omni Turbo | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $2.05 |
Output Tokens / Task Tokens generated per task | 12K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 36K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen-Omni Turbo | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | qwen-omni-turbo-instruct | gpt-6-1-sol |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-01 | 2026-09 |
| Knowledge Cutoff | November 2024 | April 2026 |
| Architecture | End-to-End Omni-Modal Multimodal 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 ($0.3/1M in) across full 33K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) | 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, vision, audio | text, vision |
| Output Modalities | text, audio | text |
Qwen-Omni Turbo vs GPT-6.1 Sol: Hızlı Yanıtlar (SSS)
Which is more intelligent, Qwen-Omni Turbo or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 74 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Qwen-Omni Turbo is cheaper at $0.20 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen-Omni Turbo has lower latency, with a time to first token of 0.38s 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 32.8K tokens.
Sonuç
Choose Qwen-Omni Turbo for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve alibaba tabanlı üretim sistemleri, or choose GPT-6.1 Sol for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve openai tabanlı üretim sistemleri.
Sıkça Sorulan Sorular (SSS)
Which is cheaper: Qwen-Omni Turbo or GPT-6.1 Sol?+
Qwen-Omni Turbo costs $0.3/1M input and $1.2/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Qwen-Omni Turbo or GPT-6.1 Sol?+
Qwen-Omni Turbo supports 33K 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.
Resmi kaynaklar: Alibaba & OpenAI Official Documentation