Qwen-VL Plus vs Yi-Large
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Alibaba 的 Qwen-VL Plus 与 01.AI 的 Yi-Large 是当前备受关注的两款主力 AI 模型。Qwen-VL Plus 提供 131.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.21 / 输出 $0.63；而 Yi-Large 提供 32.8K Token 上下文窗口，定价为每百万 Token 输入 $2.50 / 输出 $2.50。以下为两者的基准测试、延迟与成本全面对比。
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Qwen-VL Plus
原生多模态视觉模型Alibaba 推出的 Qwen-VL Plus 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 131K 上下文窗口、105 Token/秒生成速度与 71/100 综合智能指数（每百万 Token $0.21 输入 / $0.63 输出）。
$0.21 / 1M
$0.63 / 1M
131.1K tokens
N/A
Yi-Large
经典世代基础模型01.AI 推出的 Yi-Large 是一款面向企业级对话与通用文本生成任务的高可靠性经典大模型，具备 33K 上下文窗口、75 Token/秒生成速度与 79/100 综合智能指数（每百万 Token $2.5 输入 / $2.5 输出）。
$2.50 / 1M
$2.50 / 1M
32.8K tokens
N/A
核心差异一览
- 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 |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen-VL Plus · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 8K 输出容量，核心基准表现优异（MMLU-Pro 70.5% · Arena Elo 1240）
- 高达 105 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 480 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.21 输入 / $0.63 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Yi-Large · Strengths & Key Advantages
- 提供 33K 上下文窗口与最高 4K 输出容量，核心基准表现优异（GPQA Diamond 52% · MMLU-Pro 76.5%）
- 高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 510 毫秒
- 极具竞争力的 API 定价（每百万 Token $2.5 输入 / $2.5 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
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 |
技术规格参数
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: 常见问题解答 (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.
综合结论
综合来看，Qwen-VL Plus（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Yi-Large（适合：面向企业级对话与通用文本生成任务的高可靠性经典任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Yi-Large 在综合智能与编程基准测试中表现更强，Qwen-VL Plus 拥有更快的 Token 生成速度，而 Qwen-VL Plus 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Qwen-VL Plus 和 Yi-Large 哪个更强？+
在综合智能指数与编程基准评测中，Yi-Large 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Qwen-VL Plus 与 Yi-Large 的 API 价格哪个更便宜？+
Qwen-VL Plus 的 API 价格为输入 $0.21 / 输出 $0.63（每百万 Token），Yi-Large 为输入 $2.50 / 输出 $2.50。综合混合成本来看，Qwen-VL Plus 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Qwen-VL Plus 支持 131.1K Token 上下文窗口，而 Yi-Large 支持 32.8K Token。在流式生成速度方面，Qwen-VL Plus 的吞吐表现更快。
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.
数据来源: Alibaba 与 01.AI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。