qwen-coder-plus
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 60
qwen-coder-plus-1106
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗qwen-coder-plus-latest
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗qwen-coder-turbo
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗qwen-coder-turbo-0919
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗qwen-coder-turbo-latest
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗qwen2.5-coder-14b-instruct
阿里巴巴 · QwenQwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. - **Long-context Support** up to 128K tokens. **This repo contains the instruction-tuned 14B Qwen2.5-Coder model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 14.7B - Number of Paramaters (Non-Embedding): 13.1B - Number of Layers: 48 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens - Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts.
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查看详情 ↗Qwen2.5 Coder 32B Instruct
阿里巴巴 · QwenQwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...
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$0.66 / M 输入 TokenOpenRouter · qwen/qwen-2.5-coder-32b-instruct
查看详情 ↗qwen2.5-coder-7b-instruct
阿里巴巴 · Qwen-
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查看详情 ↗qwen3-coder-480b-a35b-instruct
阿里巴巴 · QwenToday, we're announcing **Qwen3-Coder**, our most agentic code model to date. **Qwen3-Coder** is available in multiple sizes, but we're excited to introduce its most powerful variant first: **Qwen3-Coder-480B-A35B-Instruct**. featuring the following key enhancements: - **Significant Performance** among open models on **Agentic Coding**, **Agentic Browser-Use**, and other foundational coding tasks, achieving results comparable to Claude Sonnet. - **Long-context Capabilities** with native support for **256K** tokens, extendable up to **1M** tokens using Yarn, optimized for repository-scale understanding. - **Agentic Coding** supporting for most platform such as **Qwen Code**, **CLINE**, featuring a specially designed function call format. **Qwen3-480B-A35B-Instruct** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 480B in total and 35B activated - Number of Layers: 62 - Number of Attention Heads (GQA): 96 for Q and 8 for KV - Number of Experts: 160 - Number of Activated Experts: 8 - Context Length: **262,144 natively**.
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查看详情 ↗Qwen3 Coder Flash
阿里巴巴 · QwenQwen3 Coder Flash 是阿里巴巴专有 Qwen3 Coder Plus 的快速且经济高效的版本。它是一款强大的编码代理模型，专门通过工具调用来实现自主编程……
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$0.195 / M 输入 TokenOpenRouter · qwen/qwen3-coder-flash
查看详情 ↗qwen3-coder-plus
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗qwen3-coder-plus-2025-07-22
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗qwen3-coder-plus-2025-09-23
阿里巴巴 · Qwen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗MioCodec-25Hz-44.1kHz-v2
Aratako**MioCodec-25Hz-44.1kHz-v2** is an upsampled, high-fidelity version of the MioCodec-25Hz-24kHz model. By integrating an **UpsamplerBlock** inspired by Inworld TTS-1 into the decoder, this model reconstructs 44.1 kHz audio from the standard 25 Hz token stream. This model is a fine-tuned version of `MioCodec-25Hz-24kHz` with the following architectural enhancements:
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查看详情 ↗MiMo-V2.6-Distill-Qwen-9B-GGUF
bartowski**Model details:** - Parameter count: 9B - Input support: text, image (with mmproj file) - details - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (5.84GB) - usually a good mix of size and performance. Download instructions available here These quants run with llama.cpp - installable in one line via llama.app:
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查看详情 ↗bloomz-560m
bigscience1. Model Summary 2. Use 3. Limitations 4. Training 5. Evaluation 7. Citation > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. - **Repository:** bigscience-workshop/xmtf - **Paper:** Crosslingual Generalization through Multitask Finetuning - **Point of Contact:** Niklas Muennighoff - **Languages:** Refer to bloom for pretraining & xP3 for finetuning language proportions. It understands both pretraining & finetuning languages. - **BLOOMZ & mT0 Model Family:**
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Seed-2.0-Code
字节跳动Seed 2.0 Code 是字节跳动推出的一款针对智能体驱动编码进行优化的模型。它适用于前端开发、多语言编程任务，以及在 Claude 等工具中的编码智能体工作流……
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$0.5 / M 输入 TokenOpenRouter · bytedance-seed/seed-2.0-code
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North Mini Code (free)
CohereNorth Mini Code is Cohere's first agentic coding model and the debut of its North family. A sparse mixture-of-experts model with 30B total parameters and 3B active, it is optimized...
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$0 / M 输入 TokenOpenRouter · cohere/north-mini-code:free
查看详情 ↗**Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements: - **Significant Performance** among open models on **Agentic Coding**, **Agentic Browser-Use**, and other foundational coding tasks. - **Long-context Capabilities** with native support for **256K** tokens, extendable up to **1M** tokens using Yarn, optimized for repository-scale understanding. - **Agentic Coding** supporting for most platform such as **Qwen Code**, **CLINE**, featuring a specially designed function call format. **Qwen3-Coder-30B-A3B-Instruct** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**.
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查看详情 ↗ABSOLUTE FIRE: 640 ARC-C for both 8bit and 4bit. Model exceeds 7 of 7 benchmarks for Qwen 3.5 9B, Qwen3.5 27B, Qwen3.6 35B-A3B, and meets Qwen 3.6 27B in some cases... and it does so in 4bit and 8bit. Regular and MTP (fast) NEO IMATRIX GGUFs provided. (this model is part of the Qwen 3.6 27B Fable Fusion 711 pipelines: 2200+ likes, 3 million + downloads) NEW - Qwen 3.8 Reasoning Modes: 2 MTP quants (Q6/Q8) Now with 5 reasoning modes (2 new - Spoon / Einstein), and 5 instruct modes (2 new - Spoon / Einstein, all use ZERO REASONING TOKENS) all switchable on the fly via API, direct and "in chat" (yes - model ctrl at the chat/message level). Model name has "plusIQ" in the name. A 12+12 (12 reasoning and 12 instruct) model with interactive optimization/help system will be releasing shortly too. BF16/16-bit MTP GGUF also avail. (there is also a extra robust "tools" version too - Q6 and Q8.) Extreme intelligence in a small package. Jaw dropping performance. Superior instruction following.
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查看详情 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF
ISTA-DASLab**A 512-expert mixture-of-experts model with half of its experts removed**, targeted at code and retaining multimodal capability. **58.4 GB** against 354 GB at BF16, an effective **1.89 bits per parameter of the original transformer**. > **Update, October 17, 2026.** The model has been improved: looping is reduced and coding performance is higher, with SWE-bench Verified rising from 75.60 to 79.20 and LiveCodeBench v6 from 86.28 to 87.43. Only the first shard changed; if you downloaded the earlier build, re-download that shard. This release is a capability-targeted compression of Qwen3.8-Flash-Next. Half of the routed experts are removed from the model rather than quantized, and the remaining weights are stored at 3.5 bpw.
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。