2026年主流AI大模型哪个性能最强？智能、速度、成本全面对比

在当今快速演进的人工智能领域，选择合适的大语言模型（LLM）大型语言模型是驱动ChatGPT等AI系统的机器学习工具，能够理解和生成人类语言，但在不同问题表述下可能产生不一致答案。对于开发者、研究者和企业而言至关重要。面对市场上超过百款模型，决策者需要基于客观、多维度的数据来评估其性能、成本与适用性。本文基于 Artificial Analysis 的最新评测数据，对主流 LLM 在智能水平、推理速度、成本效益及上下文长度等关键维度进行横向对比与分析，旨在为技术选型提供一份清晰的参考。

In today's rapidly evolving AI landscape, selecting the right Large Language Model (LLM) is crucial for developers, researchers, and enterprises. Faced with over a hundred models on the market, decision-makers need objective, multi-dimensional data to evaluate their performance, cost, and suitability. This article, based on the latest evaluation data from Artificial Analysis, provides a horizontal comparison and analysis of mainstream LLMs across key dimensions such as intelligence level, inference speed, cost-effectiveness, and context length, aiming to offer a clear reference for technical selection.

核心发现概览

本次分析揭示了当前顶级模型在不同性能指标上的分布格局。值得注意的是，没有单一模型能在所有维度上均拔得头筹，这凸显了根据具体应用场景进行权衡选择的重要性。

This analysis reveals the distribution pattern of current top-tier models across different performance metrics. It is noteworthy that no single model leads in all dimensions, highlighting the importance of trade-offs based on specific application scenarios.

智能水平（Intelligence）领先者

在衡量模型综合理解与推理能力的“智能”指标上，顶尖模型竞争激烈。

In the "Intelligence" metric, which measures a model's comprehensive understanding and reasoning capabilities, the competition among top models is fierce.

• Gemini 3.1 Pro Preview 与 GPT-5.4 (xhigh) 并列榜首，展现了当前最高的智能水平。
  • Gemini 3.1 Pro Preview and GPT-5.4 (xhigh) are tied for the top spot, demonstrating the highest current level of intelligence.
• GPT-5.3 Codex (xhigh) 与 Claude Opus 4.6 (max) 紧随其后，同样处于第一梯队。
  • GPT-5.3 Codex (xhigh) and Claude Opus 4.6 (max) follow closely, also belonging to the top tier.

推理速度（Speed）与延迟（Latency）优胜者

对于需要快速响应的应用（如实时对话、流式输出），推理速度（Tokens per Second）和首次令牌延迟（TTFT）首次令牌时间，指从用户发送请求到模型开始生成第一个令牌所需的时间，衡量模型响应速度。是关键。

For applications requiring fast responses (such as real-time conversation, streaming output), inference speed (Tokens per Second) and Time To First Token (TTFT) are critical.

• 速度最快模型：Mercury 2 和 Granite 3.3 8B 在输出令牌速度上表现最佳。
  • Fastest Models: Mercury 2 and Granite 3.3 8B perform best in output token speed.
• 延迟最低模型：Qwen3.5 2B 和 Ministral 3 3B 在首次响应时间上领先，Qwen3.5 4B 也表现优异。
  • Lowest Latency Models: Qwen3.5 2B and Ministral 3 3B lead in first response time, with Qwen3.5 4B also performing excellently.

成本效益（Price）最佳选择

在成本敏感的场景下，以下模型提供了极具竞争力的每百万令牌输入价格。

In cost-sensitive scenarios, the following models offer highly competitive prices per million input tokens.

• 最经济模型：Qwen3.5 0.8B 成本最低，Gemma 3n E4B 和 Qwen3.5 2B 也是高性价比之选。
  • Most Economical Models: Qwen3.5 0.8B has the lowest cost, while Gemma 3n E4B and Qwen3.5 2B are also high-value choices.

上下文窗口LLM处理输入文本时的长度限制，超出部分可能被截断或忽略，影响模型对长内容的整体理解。（Context Window）容量之王

处理长文档、复杂代码库或多轮深度对话需要巨大的上下文容量。

Processing long documents, complex codebases, or multi-turn in-depth conversations requires massive context capacity.

• 最大上下文支持：Llama 4 Scout 和 Grok 4.20 0309 支持最大的上下文窗口LLM处理输入文本时的长度限制，超出部分可能被截断或忽略，影响模型对长内容的整体理解。，Grok 4.1 Fast 和 Grok 4.20 0309 v2 同样容量惊人。
  • Largest Context Support: Llama 4 Scout and Grok 4.20 0309 support the largest context windows, with Grok 4.1 Fast and Grok 4.20 0309 v2 also offering impressive capacity.

主流推理模型推理模型是指能够进行逻辑推理和思维链分析的人工智能模型，Klarity通过支持这类模型提供更深入的LLM行为洞察。深度对比

为了更直观地进行多维度比较，我们将排名靠前的“推理模型推理模型是指能够进行逻辑推理和思维链分析的人工智能模型，Klarity通过支持这类模型提供更深入的LLM行为洞察。”核心数据整理如下表。表格涵盖了模型智能分、价格、速度、延迟及上下文长度等关键指标，其中加粗数据代表在该列维度中的领先者或突出表现。

For a more intuitive multi-dimensional comparison, we have compiled the core data of the top-ranked "Reasoning Models" in the table below. The table covers key metrics such as model intelligence score, price, speed, latency, and context length, with bold data indicating leaders or outstanding performance in that column dimension.

表格分析要点:

• 智能与成本的权衡：Gemini 3.1 Pro 在顶级智能中提供了相对更优的成本（$4.5 vs GPT-5.4的$5.63），而 Claude Opus 虽然智能分略低，但成本显著更高。
• 速度与延迟的差异：Grok 4.20 v2 在输出速度（166 tokens/s）上表现突出，同时保持了极低的 P90 延迟（19.18ms）。Claude Opus 和 Gemini 3.1 Pro 的 P50 延迟极低，适合对首次响应时间要求苛刻的应用。
• 上下文窗口LLM处理输入文本时的长度限制，超出部分可能被截断或忽略，影响模型对长内容的整体理解。优势：Grok 4.20 v2 以 2M 的上下文长度独树一帜，是处理超长文本任务的理想选择。
• 性价比亮点：Qwen3.6 Plus 以 50 的智能分和仅 $1.13 的输入价格，展现了出色的性价比，其 P50 延迟也极低。

Table Analysis Key Points:

• Intelligence vs. Cost Trade-off: Gemini 3.1 Pro offers a relatively better cost ($4.5 vs. GPT-5.4's $5.63) among top-tier intelligence models, while Claude Opus, despite a slightly lower intelligence score, has a significantly higher cost.
• Speed vs. Latency Differences: Grok 4.20 v2 stands out in output speed (166 tokens/s) while maintaining very low P90 latency (19.18ms). Claude Opus and Gemini 3.1 Pro have extremely low P50 latency, suitable for applications with stringent first-response time requirements.
• Context Window Advantage: Grok 4.20 v2 is unique with its 2M context length, making it an ideal choice for processing ultra-long text tasks.
• Cost-Effectiveness Highlights: Qwen3.6 Plus, with an intelligence score of 50 and an input price of only $1.13, demonstrates excellent cost-effectiveness, and its P50 latency is also very low.

选型建议与总结

选择 LLM 并非寻找“全能冠军”，而是寻找最适合特定任务的“专家”。基于以上数据，我们可以给出初步的选型指引：

Choosing an LLM is not about finding a "jack of all trades," but rather the "specialist" most suited to a specific task. Based on the above data, we can provide preliminary selection guidance:

• 追求极致智能与综合能力：优先考虑 Gemini 3.1 Pro Preview 或 GPT-5.4 (xhigh)。前者在成本上略有优势，后者在生态和工具链上可能更成熟。
  • For Pursuing Ultimate Intelligence and Comprehensive Capabilities: Prioritize Gemini 3.1 Pro Preview or GPT-5.4 (xhigh). The former has a slight cost advantage, while the latter may have a more mature ecosystem and toolchain.
• 需要低延迟与实时交互：关注 Claude Opus 4.6、Gemini 3.1 Pro 或 Qwen3.6 Plus，它们在首次令牌延迟上表现卓越。
  • Requiring Low Latency and Real-time Interaction: Consider Claude Opus 4.6, Gemini 3.1 Pro, or Qwen3.6 Plus, which excel in Time to First Token latency.
• 处理超长文本或深度对话：Grok 4.20 0309 v2 和 Llama 4 Scout 提供的超大上下文窗口LLM处理输入文本时的长度限制，超出部分可能被截断或忽略，影响模型对长内容的整体理解。是决定性优势。
  • Processing Ultra-Long Text or Deep Conversations: The massive context windows provided by Grok 4.20 0309 v2 and Llama 4 Scout are decisive advantages.
• 严格受限于预算：Qwen3.5 系列（特别是 0.8B, 2B）和 Gemma 3n E4B 提供了极高的成本效益，适合大规模或实验性部署。
  • Under Strict Budget Constraints: The Qwen3.5 series (especially 0.8B, 2B) and Gemma 3n E4B offer extremely high cost-effectiveness, suitable for large-scale or experimental deployment.
• 平衡性能、速度与成本：Qwen3.6 Plus 和 Grok 4.20 v2 在各自的智能分段提供了非常具有竞争力的综合表现，值得深入评估。
  • Balancing Performance, Speed, and Cost: Qwen3.6 Plus and Grok 4.20 v2 offer highly competitive overall performance within their respective intelligence segments and deserve in-depth evaluation.

需要注意的是，模型性能会持续更新，价格也可能变动，且实际表现受具体提示词、API配置和网络条件影响。建议在最终决策前，结合官方文档和针对自身用例的基准测试进行验证。

It is important to note that model performance is continuously updated, prices may change, and actual performance is influenced by specific prompts, API configurations, and network conditions. It is recommended to validate decisions with official documentation and benchmarks tailored to your own use case before finalizing.

（本文分析基于 Artificial Analysis 公开数据，详细方法论可参考其 FAQ。）

(This analysis is based on public data from Artificial Analysis. For detailed methodology, please refer to their FAQ.)

常见问题（FAQ）

哪款AI大模型在智能水平上表现最好？

根据评测，Gemini 3.1 Pro Preview和GPT-5.4 (xhigh)在智能指标上并列榜首，GPT-5.3 Codex (xhigh)和Claude Opus 4.6 (max)紧随其后。

如果追求推理速度，应该选择哪个模型？

Mercury 2和Granite 3.3 8B在输出令牌速度上表现最佳；Qwen3.5 2B和Ministral 3 3B则在首次响应延迟上领先。

哪款AI模型性价比最高，适合成本敏感的场景？

Qwen3.5 0.8B是成本最低的模型，Gemma 3n E4B和Qwen3.5 2B也是高性价比选择，适合预算有限的应用。