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qwen2.5-coder-14b-instruct

阿里巴巴 · Qwen

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.

Code generationOpen weights
Input Text→ Output Text
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Qwen2.5 Coder 32B Instruct

阿里巴巴 · Qwen

Qwen2.5-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**...

Code generationOpen weights
Input Text→ Output Text
$0.66 / M input tokensOpenRouter · qwen/qwen-2.5-coder-32b-instruct
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qwen3-coder-480b-a35b-instruct

阿里巴巴 · Qwen

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: - **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**.

Code generation
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Qwen3 Coder Flash

阿里巴巴 · Qwen

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...

Code generation
Input Text→ Output Text
$0.195 / M input tokensOpenRouter · qwen/qwen3-coder-flash
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**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:

Music generationCode generationOpen weights
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**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:

Code generationOpen weights
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bloomz-560m

bigscience

1. 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:**

Code generationOpen weights
Input Text→ Output Text
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Seed-2.0-Code

字节跳动

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...

Code generation
Input Text · Image · Video→ Output Text
$0.5 / M input tokensOpenRouter · bytedance-seed/seed-2.0-code
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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...

Code generationOpen weights
Input Text→ Output Text
$0 / M input tokensOpenRouter · cohere/north-mini-code:free
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**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**.

Code generationOpen weights
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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.

Code generationOpen weights
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**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.

Code generationOpen weights
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