Qwen-VL Plus vs Yi-Large
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen-VL Plus by Alibaba and Yi-Large by 01.AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Qwen-VL Plus holds a notable pricing advantage for token ingestion. On output generation, Qwen-VL Plus is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen-VL Plus provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Qwen-VL Plus leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Qwen-VL Plus
フラッグシップ最先端モデルAlibaba が提供する Qwen-VL Plus は、最大 131K トークンのコンテキストウィンドウを備え、入力 $0.21/100万トークン・出力 $0.63/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.21 / 1M
$0.63 / 1M
131.1K tokens
N/A
Yi-Large
フラッグシップ最先端モデル01.AI が提供する Yi-Large は、最大 32K トークンのコンテキストウィンドウを備え、入力 $2.5/100万トークン・出力 $2.5/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$2.50 / 1M
$2.50 / 1M
32.8K tokens
N/A
ひと目でわかる主な違い
- Qwen-VL Plus is 7.0x cheaper for API usage (input + output tokens combined).
- Qwen-VL Plus has a larger context window (131K vs 33K tokens).
- Qwen-VL Plus is faster at 105 tok/s vs 75 tok/s.
| Feature | Qwen-VL Plus | Yi-Large |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 71 | 79 |
Provider | Alibaba | 01.AI |
Input Price Cost per million input tokens | $0.21 / 1M | $2.50 / 1M |
Output Price Cost per million output tokens | $0.63 / 1M | $2.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.12 / 1M | $0.93 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 32.8K tokens~49 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 105 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | N/A | 52% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.25 / 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.
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.
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.
Qwen-VL Plus · Strengths & Key Advantages
- Alibaba の高度な Efficient Multimodal Vision-Language Transformer アーキテクチャと 131K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.21/1M、出力 $0.63/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Yi-Large · Strengths & Key Advantages
- 01.AI の高度な Dense Bilingual Foundation Transformer アーキテクチャと 32K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $2.5/1M、出力 $2.5/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen-VL Plus | Yi-Large |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $0.15 |
Output Tokens / Task Tokens generated per task | 14K | 14K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen-VL Plus | Yi-Large |
|---|---|---|
| Official API Model ID (SDK) | qwen-vl-plus-instruct | yi-large |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | 01.AI REST API (OpenAI-Compatible) |
| Release Date | 2024-01 | 2024-05 |
| Knowledge Cutoff | December 2023 | March 2024 |
| Architecture | Efficient Multimodal Vision-Language Transformer | Dense Bilingual Foundation Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.21/1M in) across full 131K window | Flat rate ($2.5/1M in) across full 33K window |
| Prompt Cache Policy & TTL | Cached input at $0.021/1M tokens | Cached input at $0.25/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| 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 | 01.AI Production Safety & Alignment Verified |
| 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 |
| Output Modalities | text | text |
Qwen-VL Plus vs Yi-Large: よくある質問 (FAQ)
Which is more intelligent, Qwen-VL Plus or Yi-Large?
Yi-Large is more intelligent. It scores 79 compared with 71 on the composite Intelligence Index.
Which model is faster?
Qwen-VL Plus is faster, generating 105 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen-VL Plus is cheaper at $0.12 per 1M tokens blended, compared with $0.93 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen-VL Plus has lower latency, with a time to first token of 0.48s compared with 0.51s.
Which model has a larger context window?
Qwen-VL Plus has a larger context window, supporting 131.1K tokens compared with 32.8K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Qwen-VL Plus if your priority is E-commerce visual search, social image captioning, and everyday visual QA. Its performance in demanding tasks justifies its operational token cost. Choose Yi-Large if your workflow prioritizes Cross-border bilingual corporate communication, translation, and high-quality Chinese prose. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Qwen-VL Plus compare to Yi-Large in API token pricing and cost per token?+
Qwen-VL Plus costs $0.21 per 1M input tokens and $0.63 per 1M output tokens, whereas Yi-Large costs $2.50 input and $2.50 output. Overall, Qwen-VL Plus is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Qwen-VL Plus or Yi-Large?+
Qwen-VL Plus supports a context window of 131K tokens (~98k words), while Yi-Large supports 33K tokens (~25k words). Therefore, Qwen-VL Plus can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen-VL Plus or Yi-Large?+
Qwen-VL Plus scores at the frontier of coding benchmarks, while Yi-Large scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen-VL Plus provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen-VL Plus and Yi-Large?+
Qwen-VL Plus is optimized for low-latency direct generation. In contrast, Yi-Large focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen-VL Plus and Yi-Large?+
Qwen-VL Plus generates approximately 105 tokens per second, while Yi-Large delivers 75 tokens per second. In latency-sensitive production environments, Qwen-VL Plus provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen-VL Plus and Yi-Large to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen-VL Plus to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Yi-Large. This tiered routing pattern cuts overall API bills by up to 70%.
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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs