A curated, living guide to the best open-source models across capability domains. Updated from live research on public leaderboards, benchmarks, and Hugging Face releases. Focused on models that are free, production-ready, and genuinely useful for vertical domain projects.
精选各能力域最佳开源模型指南——动态维护，基于公开排行榜、benchmark 和 Hugging Face 发布实时研究。聚焦于免费、可生产部署、对垂直领域项目真正有用的模型。
Last verified / 最后核验: 2026-04-19 Methodology / 方法: Cross-referenced BenchLM.ai, Onyx open-weight leaderboard, HF Open ASR Leaderboard, MTEB, SWE-bench Verified / Pro, Seed-TTS Eval, Artificial Analysis, bentoml.com guides. See Sources at bottom.
1. Frontier Reasoning / 前沿推理
For agentic workflows, multi-step planning, and general-purpose high-quality reasoning.
| Model | Provider | Params | License | Best For | Highlights (2026-04) |
|---|---|---|---|---|---|
| GLM-5.1 | Zhipu AI | MoE (undisclosed active) | Open weight | Agentic engineering, long-horizon SWE | #1 on BenchLM.ai open-weight leaderboard (84 overall) — purpose-built for agent loops |
| GLM-5 (Reasoning) | Zhipu AI | MoE | Open weight | Multi-step reasoning | Leaderboard top at 85 overall; reasoning-tuned variant |
| Qwen3.5-397B-A17B | Alibaba | 397B MoE · 17B active | Apache 2.0 | Multimodal reasoning, ultra-long ctx | Flagship MoE, 81 on leaderboard; 119 languages |
| DeepSeek-V3.2 | DeepSeek | 685B MoE | MIT | General reasoning + tool use | Very cheap inference (~$0.28/M tokens on hosted APIs); 73.1% SWE-bench |
| DeepSeek-V3.2-Speciale | DeepSeek | 685B MoE | MIT | Hard math/competition reasoning | Surpasses GPT-5 and reaches Gemini-3.0-Pro-level on AIME / HMMT 2025 |
| Llama 4 Maverick | Meta | 400B MoE · 17B active | Llama 4 License | General purpose + 1M ctx | Strong all-rounder with cheap per-token cost (17B active) |
| MiniMax-M2.7 | MiniMax | Large MoE | Open weight | Agentic workflows | Actively refines its own agent system — agent-native design |
| Gemma 4 26B | 26B dense | Gemma License | Consumer-hardware reasoning | Only Western model in top 5; 85 t/s on M3 Max / RTX 4090 |
2. Coding / 代码
Bug fixing, feature implementation, SWE-bench. Ranked by SWE-bench Verified.
| Model | Provider | Params | License | SWE-bench | Highlights |
|---|---|---|---|---|---|
| Kimi K2.5 | Moonshot AI | MoE | MIT* | 76.8% | #1 open-source on SWE-bench Verified (Jan 2026 release). *Commercial restriction at 100M+ MAU |
| GLM-5.1 | Zhipu AI | MoE | Open weight | leads SWE-bench Pro | Best on SWE-bench Pro + Terminal Bench (full agentic SWE) |
| GLM-4.7 | Zhipu AI | — | Open weight | 73.8% | Runs on consumer hardware (single RTX 4090 quantized); 94.2 HumanEval |
| DeepSeek-V3.2 | DeepSeek | 685B MoE | MIT | 73.1% | More consistent than GLM-4.7 across direct comparisons |
| Qwen3-Coder-480B-A35B | Alibaba | 480B MoE · 35B active | Apache 2.0 | 70.6% | Qwen's flagship coder; strong on repo-scale edits |
| Qwen3-Coder-30B-A3B | Alibaba | 30B MoE · 3B active | Apache 2.0 | ~65% | Best self-hostable coder — runs on 24GB VRAM |
| Qwen2.5-Coder-32B | Alibaba | 32B dense | Apache 2.0 | ~56% | Mature option, largest LoRA ecosystem |
3. Math & Reasoning / 数学与推理
Competition math (AIME, HMMT), chain-of-thought, self-correction.
| Model | Provider | Params | License | Best For | Highlights |
|---|---|---|---|---|---|
| Step-3.5-Flash | StepFun | 196B | Open weight | AIME/HMMT | AIME 2025: 97.3 — tied for highest on full leaderboard at modest size |
| GLM-4.7 (Reasoning) | Zhipu AI | — | Open weight | Competition math | AIME 2025: 97.3; strong open-weight competitor |
| DeepSeek-V3.2-Speciale | DeepSeek | 685B MoE | MIT | Proof-style math | GPT-5-level on AIME/HMMT; HMMT-specialized variant |
| DeepSeek-R1 | DeepSeek | 671B MoE | MIT | o1-level reasoning | Classic R1 still competitive; long thinking chains |
| Qwen3 235B (Thinking) | Alibaba | 235B dense | Apache 2.0 | All-round reasoning | AIME 2025: 92.3; /think mode |
| QwQ-32B | Alibaba | 32B dense | Apache 2.0 | Self-hostable reasoning | Rivals DeepSeek-R1 / o1-mini at 32B; ~48GB for BF16 |
| DeepSeek-Math-V2 | DeepSeek | 671B MoE | MIT | Formal math, proofs | Proof generation, Lean integration |
| GLM-Z1-9B | Zhipu | 9B | Open weight | Lightweight math | Runs on single RTX 3090; punches well above weight |
4. Multimodal / Vision / 多模态与视觉
Vision-language models (VLMs): image understanding, document / chart parsing, visual agents.
| Model | Provider | Params | License | Best For | Highlights |
|---|---|---|---|---|---|
| Qwen3-VL-235B-A22B | Alibaba | 235B MoE · 22B active | Apache 2.0 | Frontier VLM | Rivals Gemini-2.5-Pro / GPT-5 on multimodal benchmarks |
| GLM-4.6V | Zhipu AI | — | Open weight | Visual agent, tool use | End-to-end visual tool use, 128K context |
| GLM-4.5V | Zhipu AI | — | Open weight | Everyday VLM | Smaller but strong agentic abilities |
| InternVL3-78B | Shanghai AI Lab | 78B | Apache 2.0 | Document, chart, 3D vision | MMMU: 72.2 — SOTA among open-source |
| Qwen2.5-VL-32B | Alibaba | 32B | Apache 2.0 | OCR, structured extraction | Fits single A100 80GB |
| Qwen2.5-VL-7B | Alibaba | 7B | Apache 2.0 | Self-host VLM | Runs on 16GB VRAM |
5. Long Context / 长上下文
When you need to ingest full books, codebases, or document archives in one shot.
| Model | Provider | Context | License | Notes |
|---|---|---|---|---|
| Llama 4 Scout | Meta | 10M tokens | Llama 4 License | 109B MoE · 17B active. Industry-leading context; handles entire codebases |
| MiniMax-Text-01 | MiniMax | 4M tokens | Open weight | Between standard and Scout extreme |
| Qwen2.5-1M | Alibaba | 1M tokens | Apache 2.0 | 7B/14B variants; proven stability at 1M |
| Llama 4 Maverick | Meta | 1M tokens | Llama 4 License | 400B MoE · 17B active |
| Qwen3.5-397B-A17B | Alibaba | ≥1M | Apache 2.0 | Flagship, multimodal + long ctx |
| Qwen3.5-0.8B | Alibaba | 262K | Apache 2.0 | Long ctx at edge device scale |
6. Agent / Tool Use / Agent 与工具调用
Optimized for function calling, multi-tool orchestration, browser use.
| Model | Provider | Params | License | Highlights |
|---|---|---|---|---|
| GLM-4.5-Air | Zhipu AI | — | Open weight | Purpose-built for agent workflows — optimized tool use + web browsing |
| Qwen3-30B-A3B-Thinking-2507 | Alibaba | 30B MoE · 3B active | Apache 2.0 | Complex reasoning agents with /think mode |
| Qwen3-Coder-30B-A3B | Alibaba | 30B MoE | Apache 2.0 | Agentic coding workflows (IDE-level) |
| MiniMax-M2.7 | MiniMax | MoE | Open weight | Self-refines its own agent system |
| Hermes-4 70B | Nous Research | 70B | Llama License | Best open tool-calling on Llama base |
| xLAM-2 (series) | Salesforce | 8B–70B | CC-BY-NC | Function-calling specialist; APIGen-trained |
7. Edge & Mobile (≤7B) / 边缘与移动端
Under 7B params, runs on phones / laptops / Jetson-class hardware.
| Model | Provider | Params | License | Best For | Highlights |
|---|---|---|---|---|---|
| Phi-4-mini | Microsoft | 3.8B | MIT | Tool calling, structured | Reasoning on par with 7–9B models |
| Gemma 3 4B | 4B | Gemma License | Apple M-series / Snapdragon | 20-30 t/s at 4-bit; audio + video input | |
| Gemma 3 2B | 2B | Gemma License | Ultra-lightweight | For truly tiny devices | |
| Qwen3.5-0.8B | Alibaba | 0.8B | Apache 2.0 | Multilingual micro-agent | 262K context + thinking at 0.8B |
| SmolLM3-3B | Hugging Face | 3B | Apache 2.0 | General edge | Beats Llama-3.2-3B and Qwen2.5-3B |
| Llama 3.2-3B | Meta | 3B | Llama License | Mobile | Proven, enormous ecosystem |
| Llama 3.2-1B | Meta | 1B | Llama License | Tiny fallback | 4GB RAM at 4-bit |
8. Embedding & Retrieval / 嵌入与检索
Semantic search, RAG retrieval. Ranked for production RAG.
| Model | Provider | Params | License | Best For | Highlights |
|---|---|---|---|---|---|
| BGE-M3 | BAAI | 568M | MIT | Multilingual RAG default | Dense + sparse + ColBERT in one model; 100+ languages |
| Qwen3-Embedding-8B | Alibaba | 8B | Apache 2.0 | Top MTEB | Leads MTEB-multilingual open-source category |
| Nomic Embed Text v2 | Nomic AI | MoE | Apache 2.0 | Multilingual retrieval | First MoE embedding model; 100 languages |
| gte-multilingual-base | Alibaba | 305M | Apache 2.0 | Balanced quality/size | Strong multilingual; efficient |
| Jina Embeddings v3 | Jina AI | 570M | CC-BY-NC 4.0 | Long documents | 8192 ctx + late chunking |
| mxbai-embed-large-v1 | Mixedbread | 335M | Apache 2.0 | English MRL | Matryoshka Representation Learning |
| nomic-embed-text-v1.5 | Nomic AI | 137M | Apache 2.0 | Tiny embedder | 8K context; efficient CPU inference |
9. Speech-to-Text (ASR) / 语音识别
Transcription, captioning, voice-interface STT.
| Model | Provider | Params | License | Best For | Highlights |
|---|---|---|---|---|---|
| NVIDIA Canary Qwen 2.5B | NVIDIA | 2.5B | CC-BY 4.0 | English + Q&A | #1 on HF Open ASR Leaderboard — 5.63% WER; dual transcribe/analyze |
| NVIDIA Parakeet RNNT 1.1B | NVIDIA | 1.1B | CC-BY 4.0 | English streaming | LibriSpeech 1.8% WER — lowest of any open model |
| Whisper Large V3 Turbo | OpenAI | 809M | MIT | 99 languages | Best multilingual; real-time capable |
| IBM Granite Speech 3.3 | IBM | 8B | Apache 2.0 | Enterprise | ~5.85% WER; commercial-friendly license |
| Qwen3-ASR | Alibaba | 1.7B / 0.6B | Apache 2.0 | Multilingual + dialects | 52 languages & dialects; Jan 2026 |
| Distil-Whisper | HF / Whisper | — | MIT | Streaming | Low-latency distilled Whisper |
10. Text-to-Speech (TTS) & Voice Cloning / 语音合成与克隆
| Model | Provider | Params | License | Best For | Highlights |
|---|---|---|---|---|---|
| Fish Audio S2 Pro | Fish Audio | — | Apache 2.0 | Multilingual + cloning | Beats Google/OpenAI on Seed-TTS Eval; 80+ languages zero-shot |
| Fish Speech V1.5 | Fish Audio | — | Apache 2.0 | Proven multilingual | Stable previous generation, huge ecosystem |
| CosyVoice2-0.5B | Alibaba | 0.5B | Apache 2.0 | Streaming | 150ms latency for real-time voice UX |
| IndexTTS-2 | Bilibili Index | — | Apache 2.0 | Video dubbing | SOTA zero-shot; precise duration & emotion control |
| Kokoro-82M | hexgrad | 82M | Apache 2.0 | Efficiency | MOS 4.2 at 82M — best quality/size ratio |
11. Image Generation / 图像生成
| Model | Provider | License | Best For | Highlights |
|---|---|---|---|---|
| FLUX.2 [dev] | Black Forest Labs | FLUX-1 Dev Non-Commercial | Photorealism + text rendering | Production-grade (Nov 2025); best text-in-image of open models |
| FLUX.1 [dev] | Black Forest Labs | FLUX-1 Dev | Photorealism | Still the go-to for photoreal single images |
| Stable Diffusion 3.5 Large | Stability AI | Stability Non-Commercial | Quality improvement over SDXL | Quality leap over SDXL |
| SDXL | Stability AI | CreativeML Open RAIL++-M | LoRA ecosystem, custom fine-tunes | Battle-tested since 2023; huge community |
| HiDream | HiDream.ai | Open weight | High-res output | Strong on anime / illustration |
12. Video Generation / 视频生成
| Model | Provider | Params | License | Best For | Highlights |
|---|---|---|---|---|---|
| Wan 2.2 T2V-A14B | Alibaba | 14B active MoE | Apache 2.0 | T2V 480P/720P 5s | First open-source MoE video model; cinematic quality |
| HunyuanVideo | Tencent | 13B | Tencent License | Professional T2V | Beats Runway Gen-3 on benchmarks; mature ecosystem |
| Mochi 1 | Genmo | — | Apache 2.0 | Best T2V quality/license | Apache 2.0 gives full commercial freedom |
| CogVideoX-5B | Zhipu | 5B | Apache 2.0 | Image-to-Video | Best I2V; runs on 1× 3090 |
| LTX-Video | Lightricks | — | OpenRAIL | Real-time | First DiT-based real-time video model |
选型速查 / Quick Selection
按场景推荐 / By Scenario
| 场景 Scenario | 推荐方案 Recommendation | 为什么 Why |
|---|---|---|
| 智能客服 Customer Support | Qwen3.5 + BGE-M3 | 多语言 + 成熟的嵌入检索 |
| 代码助手 (云端) Coding (Cloud) | GLM-5.1 或 Kimi K2.5 | SWE-bench 顶级开源模型 |
| 代码助手 (自建) Coding (Self-host) | GLM-4.7 或 Qwen3-Coder-30B-A3B | 可在消费级显卡跑 |
| 文档/图表 Document & Chart | InternVL3-78B 或 Qwen3-VL-235B | MMMU 顶级，表格/图表强 |
| 移动端 App Mobile | Phi-4-mini 或 Gemma 3 4B | 4GB RAM 可跑；M-系列友好 |
| RAG 系统 RAG | DeepSeek-V3.2 + BGE-M3 | 推理 + 检索双强，价格极低 |
| 数学教育 Math Tutor | QwQ-32B 或 Step-3.5-Flash | 32B 自建 / 196B 顶尖 |
| 语音交互 Voice UX | Whisper-V3-Turbo + CosyVoice2 | 99 语言识别 + 150ms 合成 |
| 语音转录企业 Enterprise ASR | Canary Qwen 2.5B (英文) 或 IBM Granite Speech 3.3 | 低 WER + 商用许可 |
| Agent 系统 Agent | GLM-4.5-Air 或 MiniMax-M2.7 | 工具调用 + 长任务 |
| 超长上下文 Long Context | Llama 4 Scout (10M) 或 Qwen2.5-1M | 整个代码库一次喂 |
| 文生图 T2I | FLUX.2 [dev] (质量) 或 SDXL (生态) | 需要二次微调选 SDXL |
| 文生视频 T2V | Wan 2.2 (质量) 或 Mochi 1 (许可宽松) | 商用选 Mochi |
硬件要求速查 / Hardware Quick Reference
| 模型规模 Scale | 最低显存 (4-bit 量化) | 适合硬件 Hardware |
|---|---|---|
| 0.8B – 3B | 2 – 4 GB | 手机 · Raspberry Pi · Mac Mini · 树莓派 |
| 7B – 8B | 6 – 8 GB | RTX 3060 · M1/M2/M3 Mac · GTX 1080 |
| 14B – 32B | 16 – 24 GB | RTX 4090 · A5000 · M3/M4 Max (64GB) |
| 70B dense | 40 – 48 GB | A6000 · 2×4090 · M3 Ultra (128GB) |
| MoE 30B-A3B | 16 – 24 GB (总权重 ~60GB 但活跃 3B) | RTX 4090 (权重需快速存储) |
| MoE 400B-A17B | 80 – 160 GB 显存或 512 GB 统一内存 | 多卡 A100/H100 · M3 Ultra (512GB) |
| MoE 600B+ | 8× H100 80GB 或等价 | 企业级部署 |
重要变更记录 / Changelog
- 2026-04-19 — Full rewrite: added GLM-5/5.1, Kimi K2.5, Step-3.5-Flash, Qwen3-VL-235B, Wan 2.2, FLUX.2, Canary Qwen 2.5B, Qwen3-ASR, Fish Audio S2 Pro, Hermes-4. Added two new categories: Agent/Tool Use and Video Generation. Removed stale entries (Qwen-QwQ-32B renamed to QwQ-32B, etc.).
- 2026-03 — Initial version with 7 categories.
How to Add a Model / 如何添加模型
See CONTRIBUTING.md for the full template.
Hard criteria:
- Must be open-source or open-weight with a clear license (commercial-use status noted if restricted)
- Must have verifiable benchmark or well-documented real-world capability
- Must be practically usable (weights on HF, runs on consumer or affordable cloud hardware)
- Must be not vaporware — released and downloadable at time of listing
- Include a Last verified date in the row; readers must know the nutrient freshness
Sources / 参考来源
Cross-referenced at the 2026-04-19 verification pass:
- BenchLM.ai Open-Weight Leaderboard — real-time rankings
- Onyx Open-Source LLM Leaderboard 2026
- HF Open ASR Leaderboard
- Artificial Analysis open-weight comparison
- bentoml.com — Best Open-Source LLMs in 2026
- bentoml.com — Best Open-Source Embedding Models
- bentoml.com — Best Open-Source Small Language Models
- bentoml.com — Multimodal VLMs in 2026
- bentoml.com — Open-Source TTS Models
- SiliconFlow — LLM for Agent Workflow
- SiliconFlow — Best Open-Source Coding LLMs
- Northflank — Best STT 2026
- Wavespeed — Best Open Source Image Models 2026
- Hyperstack — Best Open-Source Video Generation Models
- SWE-rebench Leaderboard
- MTEB Leaderboard
Last tool-verified at 2026-04-19 (Hong Kong / Beijing timezone). Model benchmarks and releases move weekly — re-verify before any critical production selection.