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Head-to-Head Model Comparison

Qwen-Omni Turbo vs GPT-6.1 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

تمت المراجعة بواسطة فريق أبحاث GetInfoUsآخر مراجعة: October 2026السياسة التحريرية
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Compare Qwen-Omni Turbo by Alibaba against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.

مقارنة مباشرة (VS)

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Alibaba

Qwen-Omni Turbo

نموذج الصوت والكلام الطبيعي

نموذج Qwen-Omni Turbo من Alibaba مصمم خصيصاً لـتوليد الكلام البشري الطبيعي ومعالجة الصوت الفورية والنسخ الصوتي الدقيق، حيث يوفر نافذة سياق بسعة 33K وسرعة 90 رمز/ثانية ومؤشر ذكاء 74/100 (بتكلفة $0.3/$1.2 لكل مليون رمز).

Intelligence Index74 / 100
Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

OpenAI

GPT-6.1 Sol

نموذج رائد متقدم

نموذج GPT-6.1 Sol من OpenAI مصمم خصيصاً لـالاستدلال المؤسسي المتقدم وهندسة البرمجيات وتطبيقات الإنتاج واسعة النطاق، حيث يوفر نافذة سياق بسعة 1M وسرعة 120 رمز/ثانية ومؤشر ذكاء 94/100 (بتكلفة $2/$10 لكل مليون رمز).

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

أهم الفروقات في لمحة

  • 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.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen-Omni TurboGPT-6.1 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
7494
Provider
AlibabaOpenAI
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 text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
90 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
N/A82.3%
GPQA Diamond
Graduate-level reasoning
N/A80.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.03 / 1M$0.10 / 1M

من يتفوق في كل مجال؟

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Qwen-Omni Turbo
7.7x cheaper monthly runtime
Speed
GPT-6.1 Sol
+33% faster generation
Context
GPT-6.1 Sol
1M tokens window

نتائج الاختبارات المعيارية الموثقة

Official evaluation metrics verified by independent third-party research labs.

Qwen-Omni TurboGPT-6.1 Sol
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs82.3%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs80.8%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

69%vs89.9%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1235ptsvs1519pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs18.1%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs63.6%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs54.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.7%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs93.5%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs91%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs120 tok/s

زمن الاستجابة والسرعة

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Qwen-Omni Turbo0.38s
GPT-6.1 Sol1.12s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen-Omni Turbo5.94s
GPT-6.1 Sol5.29s

محاكي تكلفة الرموز التفاعلي

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Qwen-Omni Turbo Monthly API Bill
$1.57 / month
$0.3/1M in · $1.2/1M out
GPT-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Qwen-Omni Turbo saves approximately $10.58 / month (87%) under this workload.7.7x Savings

Qwen-Omni Turbo · Strengths & Key Advantages

  • نافذة سياق بسعة 33K وحد أقصى للمخرجات يصل إلى 8K رمز (MMLU-Pro 69% · Arena Elo 1235)
  • سرعة توليد عالية تبلغ 90 رمز/ثانية مع زمن استجابة لأول رمز (TTFT) قدره 380 مللي ثانية
  • تسعير API تنافسي (بتكلفة $0.3/$1.2 لكل مليون رمز) مع دعم التخزين المؤقت للأوامر والمخرجات المهيكلة بصيغة JSON
Considerations & Trade-offs
  • نموذج سحابي مغلق الأوزان لا يدعم تنزيل الأوزان للتشغيل المحلي المعزول عن الإنترنت
  • في المسائل البحثية فائقة التعقيد، قد تتفوق نماذج الاستدلال الثقيلة المتخصصة في عمق التحليل

GPT-6.1 Sol · Strengths & Key Advantages

  • نافذة سياق بسعة 1M وحد أقصى للمخرجات يصل إلى 128K رمز (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
  • سرعة توليد عالية تبلغ 120 رمز/ثانية مع زمن استجابة لأول رمز (TTFT) قدره 1120 مللي ثانية
  • تسعير API تنافسي (بتكلفة $2/$10 لكل مليون رمز) مع دعم التخزين المؤقت للأوامر والمخرجات المهيكلة بصيغة JSON
Considerations & Trade-offs
  • نموذج سحابي مغلق الأوزان لا يدعم تنزيل الأوزان للتشغيل المحلي المعزول عن الإنترنت
  • في حالات الاستخدام الكثيف، يُنصح بتفعيل التخزين المؤقت (Prompt Caching) وBatch API لترشيد النفقات

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricQwen-Omni TurboGPT-6.1 Sol
Cost per Task
Estimated USD to finish one task
$0.06$2.05
Output Tokens / Task
Tokens generated per task
12K62K
Reasoning Tokens / Task
Thinking tokens burned per task
2K36K

المواصفات التقنية

Architecture, release timeline, capabilities, and modalities.

FeatureQwen-Omni TurboGPT-6.1 Sol
Official API Model ID (SDK)qwen-omni-turbo-instructgpt-6-1-sol
API ProtocolAlibaba REST API (OpenAI-Compatible)OpenAI Responses API & Chat Completions v1
Release Date2025-012026-09
Knowledge CutoffNovember 2024April 2026
ArchitectureEnd-to-End Omni-Modal Multimodal TransformerNear-Astra unified reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM10,000 – 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 windowFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.03/1M tokensAutomatic prefix caching at $0.1/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationAlibaba Production Safety & Alignment VerifiedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, vision, audiotext, vision
Output Modalitiestext, audiotext

Qwen-Omni Turbo vs GPT-6.1 Sol: إجابات سريعة (FAQ)

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.

الحكم النهائي

Choose Qwen-Omni Turbo for مثالي لـتوليد الكلام البشري الطبيعي ومعالجة الصوت الفورية والنسخ الصوتي الدقيق وأنظمة rag المؤسسية وربط واجهات البرمجة (api) القابلة للتوسع., or choose GPT-6.1 Sol for مثالي لـالاستدلال المؤسسي المتقدم وهندسة البرمجيات وتطبيقات الإنتاج واسعة النطاق وأنظمة rag المؤسسية وربط واجهات البرمجة (api) القابلة للتوسع..

الأسئلة الشائعة (FAQ)

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.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

المصادر الرسمية: Alibaba & OpenAI Official Documentation