Qwen3.6 Flash vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3.6 Flash by Alibaba against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
任意のAIモデルを切り替えて比較
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
Qwen3.6 Flash
ネイティブ・マルチモーダルAIAlibaba の Qwen3.6 Flash は、テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析向けに設計されたAIモデルです。1M のコンテキストウィンドウ、毎秒 145 トークンの生成速度、知能指数 81/100（1Mトークンあたり入力$0.08・出力$0.32）を実現しています。
$0.08 / 1M
$0.32 / 1M
1M tokens
N/A
GPT-6.1 Sol
フラッグシップ最先端モデルOpenAI の GPT-6.1 Sol は、最先端のエンタープライズ推論、ソフトウェア工学、ミッションクリティカルな本番運用向けに設計されたAIモデルです。1M のコンテキストウィンドウ、毎秒 120 トークンの生成速度、知能指数 94/100（1Mトークンあたり入力$2・出力$10）を実現しています。
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
ひと目でわかる主な違い
- 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.
| Feature | Qwen3.6 Flash | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 94 |
Provider | Alibaba | OpenAI |
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 text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 145 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | 61.5% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.6 Flash · Strengths & Key Advantages
- 1M のコンテキストウィンドウと最大 16K トークンの出力容量（GPQA Diamond 61.5% · MMLU-Pro 78%）
- 毎秒 145 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 310ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$0.08・出力$0.32）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
GPT-6.1 Sol · Strengths & Key Advantages
- 1M のコンテキストウィンドウと最大 128K トークンの出力容量（SWE-bench Verified 82.3% · GPQA Diamond 80.8%）
- 毎秒 120 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 1120ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$2・出力$10）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 大量のトークンを処理する本番環境では、プロンプトキャッシュや Batch API の併用によるコスト最適化が推奨されます
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.6 Flash | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.05 |
Output Tokens / Task Tokens generated per task | 16K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 36K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.6 Flash | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | qwen3-6-flash-instruct | gpt-6-1-sol |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | March 2026 | April 2026 |
| Architecture | High-Efficiency Multimodal Vision Transformer | Near-Astra unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.08/1M in) across full 1000K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.008/1M 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) | 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 | text, vision |
| Output Modalities | text | text |
Qwen3.6 Flash vs GPT-6.1 Sol: よくある質問 (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.
総合評価
Choose Qwen3.6 Flash for テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析、エンタープライズrag基盤、スケーラブルな本番api統合に最適。, or choose GPT-6.1 Sol for 最先端のエンタープライズ推論、ソフトウェア工学、ミッションクリティカルな本番運用、エンタープライズrag基盤、スケーラブルな本番api統合に最適。.
よくある質問 (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.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Aion-3.0 vs Aion-Mini：ベンチマーク・API料金・速度の徹底比較 (2026)比較
- Aion-3.0 vs Gemini 3.6 Flash：ベンチマーク・API料金・速度の徹底比較 (2026)比較
- Aion-Mini vs Laguna XS 2.1：ベンチマーク・API料金・速度の徹底比較 (2026)比較
- Aya Expanse 32B vs Llama 3.2 90B Vision：ベンチマーク・API料金・速度の徹底比較 (2026)比較
- Aya Expanse 8B vs Command R+：ベンチマーク・API料金・速度の徹底比較 (2026)比較
- Aya Expanse 8B vs Qwen 2.5 7B Instruct：ベンチマーク・API料金・速度の徹底比較 (2026)比較
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.
公的ソース: Alibaba & OpenAI Official Documentation