codex-auto-review
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探索 AI 模型 60
Cyber-Tiel-Coder-35B-A3B-GGUF-MTP
peculiar-ragdoll> GGUF without MTP here, MLX version here, and MLX MTP version here. > ⚠️ **WARNING - Read before use**: Abliterated models like CyberTiel are able to say and do things other models refuse, including potentially harmful behaviours. By using CyberTiel, you agree to take full personal responsibility and liability for your use of it, its behaviour and generated content, and to show caution: it is entirely up to you as the user to ensure your use of CyberTiel is legitimate, legal and harmless, and that the model is safely sandboxed and monitored when running. Much like a knife, abliterated models like CyberTiel can be classified and used as either a tool or a weapon, depending on the context and use case. We carry forward huihui's original usage warnings. *CyberTiel* outcodes every other 35B-A3B at Q4 quantization (and spring-of-2026 frontier models), while engaging with offensive security work without hesitation or refusal. As a sweet spot between speed and ability, CyberTiel delivers agentic coding solves about 3-4x faster than 3.8-27B dense. This is the first time the frontier coder in this size/speed class is an uncensored model. If you need a safer censored alternative, go for *TielCoder*.
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查看详情 ↗Tiel-Coder-35B-A3B-GGUF-MTP
peculiar-ragdoll*Tiel* is the fast coder of the arsenal. At 4-bit quantization and 23 GB it fixes real codebase issues at the rate (and speed, with the right GPU) of Opus 4.6 medium, while holding the best multi-turn conversation of any local model we have measured. It is also cheerfully bad at trivia. > **This is Ornith-1.5-35B-A3B re-quantized > dynamically with our own imatrix and carrying the Sharp chat template** > inside the GGUF. Find the non-MTP GGUFs here, and the non-MTP MLX > version here. > **The uncensored and improved CyberTiel is out now!** Get it here. **Where it stands.** On 25 SWE-bench-Live problems Tiel fixes 12 — the same as Opus 4.6 (medium), four more than Ornith-1.5 itself, three more than Nail, and four more than Sonnet 5 (medium). Among models of its own class it is first; the ones ahead are dense 27Bs and Opus 5. Its time per attempt is also steadier than Nail's: an 8.6 minute median against 7.2, but a 12.3 minute mean against 15.7, because it lacks Nail's tail of expensive attempts.
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查看详情 ↗VieNeu-TTS-v3-Turbo
pnnbao-ump**VieNeu-TTS v3 Turbo** is the next generation of Vietnamese TTS — **48 kHz** high-fidelity speech, **23 built-in preset voices** across three regions (North / Central / South), **instant voice cloning**, **real-time streaming** with an **OpenAI-compatible API** (16 concurrent streams on one RTX 3060), inline **emotion cues**, and seamless **bilingual (En–Vi) code-switching**. The reference implementation is the **`vieneu` Python SDK (v3.7.1)**. Its minimal install is **torch-free**: on CPU everything runs on **ONNX Runtime** (PyTorch is never imported), and on a CUDA machine it auto-switches to the PyTorch engine with **automatic batching** and a **continuous-batching stream scheduler** — same API, no code change. > [!IMPORTANT] > **What's new in SDK v3.7:** > - **GPU streaming with continuous batching** — `infer_stream` on CUDA runs on one CUDA graph shared by every caller: first audio in **~115 ms**, **16 concurrent streams under 200 ms** on a single RTX 3060 (32 max). > - **OpenAI-compatible streaming API + Docker** — `POST /v1/audio/speech` (`pcm`/`wav`, chunked or SSE) works with the OpenAI SDK, Pipecat, LiveKit; `docker compose --profile api-gpu` / `api-cpu`. Numbers, sizing and CPU-only notes: docs/streaming.md. > - **23 preset voices** covering North / Central / South, both genders and several reading characters (default: `Minh Quân`). > - **LoRA fine-tuning** of v3 Turbo on one consumer GPU. > - Since 3.3: torch-free voice cloning on CPU, optional **int8** CPU backbone (`Vieneu(precision="int8")`, ~2× faster; fp32 is the default), sliding-window repetition penalty.
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Qwen2.5-Coder-32B-Instruct-AWQ
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 AWQ-quantized 4-bit instruction-tuned 32B 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: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - 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. - Quantization: AWQ 4-bit
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查看详情 ↗Qwen2.5-Coder-7B-Instruct-AWQ
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 AWQ-quantized 4-bit 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. - Quantization: AWQ 4-bit
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查看详情 ↗Qwen3 Coder 480B A35B
阿里巴巴 · QwenQwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over...
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$0.3 / M 输入 TokenOpenRouter · qwen/qwen3-coder
查看详情 ↗Qwen3 Coder 30B A3B Instruct
阿里巴巴 · QwenQwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the...
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$0.07 / M 输入 TokenOpenRouter · qwen/qwen3-coder-30b-a3b-instruct
查看详情 ↗Qwen3-Coder-30B-A3B-Instruct-FP8
阿里巴巴 · Qwen**Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct-FP8**. 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-FP8** 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 ↗Qwen3 Coder Next
阿里巴巴 · QwenQwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...
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$0.12 / M 输入 TokenOpenRouter · qwen/qwen3-coder-next
查看详情 ↗Qwen3-Coder-Next-FP8
阿里巴巴 · QwenToday, we're announcing **Qwen3-Coder-Next-FP8**, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements: - **Super Efficient with Significant Performance**: With only 3B activated parameters (80B total parameters), it achieves performance comparable to models with 10–20x more active parameters, making it highly cost-effective for agent deployment. - **Advanced Agentic Capabilities**: Through an elaborate training recipe, it excels at long-horizon reasoning, complex tool usage, and recovery from execution failures, ensuring robust performance in dynamic coding tasks. - **Versatile Integration with Real-World IDE**: Its 256k context length, combined with adaptability to various scaffold templates, enables seamless integration with different CLI/IDE platforms (e.g., Claude Code, Qwen Code, Qoder, Kilo, Trae, Cline, etc.), supporting diverse development environments. > [!Note] > This repository contains the **FP8-quantized Qwen3-Coder-Next** model checkpoint for convenience and performance. > The quantization method is "fine-grained fp8" quantization with block size of 128. > You can find more details in the `quantization_config` field in `config.json`. > > In addition, the experimental results presented in this model card are obtained from the original bfloat16 model prior to FP8 quantization.
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Grok Code Fast 1 是由 github-copilot 提供的人工智能模型。
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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 ↗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。