本站推荐榜单（站务编辑精选，会随时间更新）

以下是本站目前推荐的各领域最佳 AI 模型。榜单代表编辑观点，不是客观评测；下方点评为观点与分析，不含基准测试数据。欢迎在回复里交流你的使用体验，或推荐你认为更好的模型。

分类点评（观点，欢迎反驳）

最好的闭源模型：Claude Opus 5.5
闭源旗舰通常面向复杂推理、长文档分析、代码与 Agent 类任务，代价是按量付费、数据需出站。常见争论点：其他厂商的旗舰是否在某些任务上更强、价格是否值得。

最好的开源模型：DeepSeek V4.1 Flash 1017
开源模型的魅力在于可自托管、可微调、可私有化部署。Flash 类命名通常偏向速度与成本，但具体取舍请以你的实测为准。常见争论点：是否有更大或更擅长某类任务的开源权重。

最好的闭源视频生成模型：Seedance 2.5
适合短视频创作、广告素材、分镜预演。争论点通常集中在画面一致性、动作自然度、时长与价格。

最好的开源视频生成模型：Minimax H3
开源视频模型适合想本地跑、可控、可二次开发的玩家，但对显存和算力要求通常很高。争论点：本地能不能跑得动、效果与闭源差多少。

最好的生图模型：GPT Image 2.5
常见用途包括海报、插画、产品图、文字排版和图像编辑。争论点：文字渲染、风格多样性、可控性，以及开源生图模型能否替代。

最好的决策模型：KnowLine-31B-Gen1
决策类模型通常指偏重分析权衡、规划与判断的场景。这个名字相对小众，欢迎用过的朋友分享真实体验，也欢迎提出你认为更合适的候选。

最好的 TTS 模型：ElevenLabs Eleven v4 Turbo
文字转语音常用于有声书、配音、客服与视频旁白。“Turbo”类命名通常偏向低延迟。争论点：自然度、多语言（尤其中文）表现、克隆音色，以及开源 TTS 能否替代。

最好的 ASR 模型：ElevenLabs Scribe v2
语音转文字用于会议纪要、字幕、播客转写。争论点：多语言与口音、噪声环境、说话人区分，以及本地可跑的开源方案。

最好的嵌入模型：Voyage 4 Large
嵌入模型是 RAG、语义搜索、推荐系统的基础。争论点：多语言检索效果、向量维度与成本、以及可自托管的开源嵌入模型。

最好的音乐生成模型：Suno v6
常用于配乐、demo 创作、灵感草图。争论点：人声质感、结构完整度、可编辑性，以及版权与商用授权。

一起讨论

1. 你心中各领域的第一是谁？哪一项榜单你不同意，为什么？
2. 开源与闭源的差距现在还有多大？哪些场景开源已经够用？
3. 本地能跑的最佳选择是什么？你的显卡和配置是什么？
4. 你有没有遇到过榜单模型表现不如预期的场景？
5. 还有哪些类别你希望加入榜单（例如代码、OCR、翻译、重排序）？

直接在下方回复，说说你的看法吧。

Site editor's picks (curated by the site team, updated over time)

Here are the models we currently recommend in each category. This list reflects editorial opinion, not an objective benchmark; the commentary below is analysis and opinion, with no benchmark numbers. Share your own experience in the replies, or suggest models you think should be on the list.

Commentary by category (opinion, push back welcome)

Best closed-source model: Claude Opus 5.5
Closed flagships typically target complex reasoning, long-document analysis, coding and agent workflows, at the cost of usage-based pricing and sending data off-site. Typical debate: whether another vendor's flagship beats it on your tasks, and whether the price is worth it.

Best open-source model: DeepSeek V4.1 Flash 1017
Open weights mean you can self-host, fine-tune and keep data private. “Flash”-style names usually lean toward speed and cost, but judge by your own testing. Typical debate: is there a larger or task-specific open-weight model you'd rather use?

Best closed-source video generation model: Seedance 2.5
Used for short-form video, ad creatives and storyboard previews. Debates usually center on consistency, motion quality, clip length and price.

Best open-source video generation model: Minimax H3
Open video models suit people who want local control and customization, though they usually demand serious VRAM and compute. Debate: can you actually run it locally, and how far behind closed models is it?

Best image generation model: GPT Image 2.5
Common uses: posters, illustrations, product shots, text-in-image and editing. Debates: text rendering, style variety, controllability, and whether open-source image models can replace it.

Best decision-making model: KnowLine-31B-Gen1
Decision-oriented models focus on weighing trade-offs, planning and judgment. This one is less widely known, so we'd especially love to hear real experience from anyone who has used it, and your alternative nominations.

Best TTS model: ElevenLabs Eleven v4 Turbo
TTS is used for audiobooks, dubbing, support bots and narration. “Turbo” naming usually points to lower latency. Debates: naturalness, multilingual quality (especially Chinese), voice cloning, and whether open-source TTS is good enough.

Best ASR model: ElevenLabs Scribe v2
Speech-to-text powers meeting notes, subtitles and podcast transcripts. Debates: accents and languages, noisy audio, speaker separation, and local open-source options.

Best embedding model: Voyage 4 Large
Embeddings underpin RAG, semantic search and recommendation. Debates: multilingual retrieval quality, vector size and cost, and self-hostable open-source embedders.

Best music generation model: Suno v6
Used for soundtracks, demos and idea sketches. Debates: vocal quality, song structure, editability, and copyright / commercial licensing.

Let's discuss

1. Who is your #1 in each category? Which pick do you disagree with, and why?
2. How big is the open vs. closed gap today? Where is open-source already good enough?
3. What's the best model you can run locally, and on what hardware?
4. Have you seen a listed model underperform in your real use case?
5. Which categories should we add (e.g. coding, OCR, translation, reranking)?

Reply below and tell us what you think.