Head-to-Head Model Comparison

Qwen3.6 Flash vs GPT-6.1 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Проверено исследовательской группой GetInfoUsПоследняя проверка: October 2026Редакционная политика
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Compare Qwen3.6 Flash by Alibaba against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.

Прямое сравнение (VS)

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Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:
Alibaba

Qwen3.6 Flash

Модель глубокого рассуждения

Qwen3.6 Flash от Alibaba — передовая модель искусственного интеллекта с контекстным окном 1M токенов и стоимостью API $0.08/1M входящих и $0.32/1M исходящих токенов для программирования, аналитики и автоматизации.

Intelligence Index81 / 100
Input Token:

$0.08 / 1M

Output Token:

$0.32 / 1M

Context Window:

1M tokens

SWE-bench Verified:

N/A

OpenAI

GPT-6.1 Sol

Модель глубокого рассуждения

GPT-6.1 Sol от OpenAI — передовая модель искусственного интеллекта с контекстным окном 1M токенов и стоимостью API $2.0/1M входящих и $10.0/1M исходящих токенов для программирования, аналитики и автоматизации.

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

Ключевые отличия кратко

  • Qwen3.6 Flash is 28.9x cheaper for API usage (input + output tokens combined).
  • Qwen3.6 Flash is faster at 145 tok/s vs 120 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen3.6 FlashGPT-6.1 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
8194
Provider
AlibabaOpenAI
Input Price
Cost per million input tokens
$0.08 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$0.32 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.05 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
16K128K
Throughput Speed
Tokens generated per second
145 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
N/A82.3%
GPQA Diamond
Graduate-level reasoning
61.5%80.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.01 / 1M$0.10 / 1M

Кто лидирует в каждой категории?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Qwen3.6 Flash
28.9x cheaper monthly runtime
Speed
Qwen3.6 Flash
+21% faster generation
Context
Tied
1M tokens window

Результаты проверенных бенчмарков

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

Qwen3.6 FlashGPT-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.

61.5%vs80.8%
MMLU-Pro (Multi-task Language Understanding)

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

78%vs89.9%
LMSYS Chatbot Arena Elo

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

1320ptsvs1519pts
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.

145 tok/s vs120 tok/s

Задержка и скорость ответа

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
Qwen3.6 Flash0.31s
GPT-6.1 Sol1.12s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen3.6 Flash3.76s
GPT-6.1 Sol5.29s

Интерактивный калькулятор стоимости токенов

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)
Qwen3.6 Flash Monthly API Bill
$0.42 / month
$0.08/1M in · $0.32/1M out
GPT-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Qwen3.6 Flash saves approximately $11.73 / month (97%) under this workload.28.9x Savings

Qwen3.6 Flash · Strengths & Key Advantages

  • Современная архитектура High-Efficiency Multimodal Vision Transformer от Alibaba с поддержкой до 1M токенов контекста
  • Оптимальная стоимость использования ($0.08/1M вход, $0.32/1M выход) и высокая скорость генерации
  • Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
  • При работе с максимальным контекстом требуется контроль расхода токенов
  • Для узкоспециализированных задач рекомендуется точная настройка системных промптов

GPT-6.1 Sol · Strengths & Key Advantages

  • Современная архитектура Near-Astra unified reasoning model от OpenAI с поддержкой до 1M токенов контекста
  • Оптимальная стоимость использования ($2.0/1M вход, $10.0/1M выход) и высокая скорость генерации
  • Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
  • При работе с максимальным контекстом требуется контроль расхода токенов
  • Для узкоспециализированных задач рекомендуется точная настройка системных промптов

Token Use & Task Economics

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

MetricQwen3.6 FlashGPT-6.1 Sol
Cost per Task
Estimated USD to finish one task
$0.03$2.05
Output Tokens / Task
Tokens generated per task
16K62K
Reasoning Tokens / Task
Thinking tokens burned per task
2K36K

Технические характеристики

Architecture, release timeline, capabilities, and modalities.

FeatureQwen3.6 FlashGPT-6.1 Sol
Official API Model ID (SDK)qwen3-6-flash-instructgpt-6-1-sol
API ProtocolAlibaba REST API (OpenAI-Compatible)OpenAI Responses API & Chat Completions v1
Release Date2026-042026-09
Knowledge CutoffMarch 2026April 2026
ArchitectureHigh-Efficiency Multimodal Vision TransformerNear-Astra unified reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.08/1M in) across full 1000K windowFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.008/1M 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)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, visiontext, vision
Output Modalitiestexttext

Qwen3.6 Flash vs GPT-6.1 Sol: Быстрые ответы (FAQ)

Which is more intelligent, Qwen3.6 Flash or GPT-6.1 Sol?

GPT-6.1 Sol is more intelligent. It scores 94 compared with 81 on the composite Intelligence Index.

Which model is faster?

Qwen3.6 Flash is faster, generating 145 tokens per second compared with 120 tokens per second.

Which model is cheaper to run?

Qwen3.6 Flash is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3.6 Flash has lower latency, with a time to first token of 0.31s compared with 1.12s.

Вердикт

Choose Qwen3.6 Flash for корпоративной автоматизации, разработки по, ии-агентов и продуктовых решений на базе alibaba, or choose GPT-6.1 Sol for корпоративной автоматизации, разработки по, ии-агентов и продуктовых решений на базе openai.

Часто задаваемые вопросы (FAQ)

Which is cheaper: Qwen3.6 Flash or GPT-6.1 Sol?+

Qwen3.6 Flash costs $0.08/1M input and $0.32/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: Qwen3.6 Flash or GPT-6.1 Sol?+

Qwen3.6 Flash supports 1M 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.

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.

Официальные источники: Alibaba & OpenAI Official Documentation