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qwen-coder-plus

阿里巴巴 · Qwen

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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。

qwen-coder-plus-1106

阿里巴巴 · Qwen

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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。

qwen-coder-plus-latest

阿里巴巴 · Qwen

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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。

qwen-coder-turbo

阿里巴巴 · Qwen

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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。

qwen-coder-turbo-0919

阿里巴巴 · Qwen

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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。

qwen-coder-turbo-latest

阿里巴巴 · Qwen

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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。

Qwen2.5-Coder-14B-Instruct

阿里巴巴 · Qwen

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Qwen2.5-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.

Qwen2.5-Coder-7B-Instruct

阿里巴巴 · Qwen

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Qwen2.5-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 7B 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: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 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.

qwen3-coder-480b-a35b-instruct

阿里巴巴 · Qwen

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Today, 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: **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**. **NOTE: This model supports only non-thinking mode and does not generate `` `` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**

qwen3-coder-flash

阿里巴巴 · Qwen

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Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling...

qwen3-coder-plus

阿里巴巴 · Qwen

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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。

qwen3-coder-plus-2025-07-22

阿里巴巴 · Qwen

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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。

qwen3-coder-plus-2025-09-23

阿里巴巴 · Qwen

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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。

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。

**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:

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。

Seed-2.0-Code

字节跳动

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Seed 2.0 Code is a model from ByteDance Seed optimized for agentic coding. It is suited for frontend development, multilingual programming tasks, and coding-agent workflows in tools such as Claude...

North Mini Code (free)

Cohere

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North 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...

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。

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.

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。

doubao-seed-2-0-code-preview-260215

字节跳动

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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。

**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.

> [!NOTE] > **Qwopus3.8-27B-Flash-V2** is a renewed post-training release of Qwopus3.8-27B-Flash, whose foundation is Qwen3.8-27B. > [!TIP] > **Revised Rewards and Reinforcement Learning · Improved Python Formatting** Qwopus3.8-27B-Flash-V2 is a renewed post-training release of Qwopus3.8-27B-Flash, which itself is built on Qwen3.8-27B. The goal remains the same: preserve enough capability for demanding work while reducing ineffective reasoning and making long-running agent workloads more practical.