ERNIE 5.0 vs Qwen3.6 Max Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Baidu 的 ERNIE 5.0 与 Alibaba 的 Qwen3.6 Max Preview 是当前备受关注的两款主力 AI 模型。ERNIE 5.0 提供 128K Token 上下文窗口，API 定价为每百万 Token 输入 $1.50 / 输出 $6.00；而 Qwen3.6 Max Preview 提供 262.1K Token 上下文窗口，定价为每百万 Token 输入 $1.50 / 输出 $6.00。以下为两者的基准测试、延迟与成本全面对比。
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ERNIE 5.0
旗舰前沿大模型Baidu 推出的 ERNIE 5.0 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型，具备 128K 上下文窗口、70 Token/秒生成速度与 90/100 综合智能指数（每百万 Token $1.5 输入 / $6 输出）。
$1.50 / 1M
$6.00 / 1M
128K tokens
60%
Qwen3.6 Max Preview
旗舰前沿大模型Alibaba 推出的 Qwen3.6 Max Preview 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型，具备 262K 上下文窗口、75 Token/秒生成速度与 90/100 综合智能指数（每百万 Token $1.5 输入 / $6 输出）。
$1.50 / 1M
$6.00 / 1M
262.1K tokens
70%
核心差异一览
- Both models cost the same under this workload — choose on capability, not price.
- Qwen3.6 Max Preview has a larger context window (262K vs 128K tokens).
- Qwen3.6 Max Preview is faster at 75 tok/s vs 70 tok/s.
- Qwen3.6 Max Preview leads on SWE-bench coding (70.0% vs 60.0%).
| Feature | ERNIE 5.0 | Qwen3.6 Max Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 90 | 90 |
Provider | Baidu | Alibaba |
Input Price Cost per million input tokens | $1.50 / 1M | $1.50 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $6.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.01 / 1M | $1.01 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 16K |
Throughput Speed Tokens generated per second | 70 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 60% | 70% |
GPQA Diamond Graduate-level reasoning | 74.5% | 76% |
Cached Input Price Prompt caching discount per 1M tokens | $0.15 / 1M | $0.15 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
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.
ERNIE 5.0 · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 60% · GPQA Diamond 74.5%）
- 高达 70 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 580 毫秒
- 极具竞争力的 API 定价（每百万 Token $1.5 输入 / $6 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
Qwen3.6 Max Preview · Strengths & Key Advantages
- 提供 262K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 70% · GPQA Diamond 76%）
- 高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 540 毫秒
- 极具竞争力的 API 定价（每百万 Token $1.5 输入 / $6 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | ERNIE 5.0 | Qwen3.6 Max Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.32 | $0.32 |
Output Tokens / Task Tokens generated per task | 25K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 9K | 11K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | ERNIE 5.0 | Qwen3.6 Max Preview |
|---|---|---|
| Official API Model ID (SDK) | ernie-5-0 | qwen3-6-max-preview-instruct |
| API Protocol | Baidu REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2026-05 | 2026-04 |
| Knowledge Cutoff | February 2026 | March 2026 |
| Architecture | Colossus-Scale Omnimodal MoE Transformer | High-Scale Frontier Agentic Transformer |
| Parameters | 800B total (45B active MoE) | 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 ($1.5/1M in) across full 128K window | Flat rate ($1.5/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Cached input at $0.15/1M tokens | Cached input at $0.15/1M 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | 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 | Baidu Production Safety & Alignment Verified | Alibaba 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, audio, video | text |
| Output Modalities | text | text |
ERNIE 5.0 vs Qwen3.6 Max Preview: 常见问题解答 (FAQ)
Which model is faster?
Qwen3.6 Max Preview is faster, generating 75 tokens per second compared with 70 tokens per second.
Which model has lower latency?
Qwen3.6 Max Preview has lower latency, with a time to first token of 0.54s compared with 0.58s.
Which model has a larger context window?
Qwen3.6 Max Preview has a larger context window, supporting 262.1K tokens compared with 128K tokens.
综合结论
综合来看，ERNIE 5.0（适合：面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、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 Qwen3.6 Max Preview（适合：面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、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：ERNIE 5.0 在综合智能与编程基准测试中表现更强，Qwen3.6 Max Preview 拥有更快的 Token 生成速度，而 ERNIE 5.0 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，ERNIE 5.0 和 Qwen3.6 Max Preview 哪个更强？+
在综合智能指数与编程基准评测中，ERNIE 5.0 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。
ERNIE 5.0 与 Qwen3.6 Max Preview 的 API 价格哪个更便宜？+
ERNIE 5.0 的 API 价格为输入 $1.50 / 输出 $6.00（每百万 Token），Qwen3.6 Max Preview 为输入 $1.50 / 输出 $6.00。综合混合成本来看，ERNIE 5.0 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？+
ERNIE 5.0 支持 128K Token 上下文窗口，而 Qwen3.6 Max Preview 支持 262.1K Token。在流式生成速度方面，Qwen3.6 Max Preview 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.
Related reading
- ERNIE 5.0 vs ERNIE 4.5：基准测试、API 价格与速度全面对比 (2026)对比
- Qwen3.6 Max Preview vs Qwen3.6 Plus：基准测试、API 价格与速度全面对比 (2026)对比
- Qwen3.7 Max vs Qwen3.6 Max Preview：基准测试、API 价格与速度全面对比 (2026)对比
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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: Baidu 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。