Qwen3-1.7B-AutoRound-MXFP4-W4A4
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探索 AI 模型 84999
Qwen3-30B-A3B-12L-MXFP4-test
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查看详情 ↗Qwen3-30B-A3B-12L-MXFP8-test
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查看详情 ↗Qwen3-32B-MXFP8-CT-AutoRound
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查看详情 ↗Qwen3-32B-MXFP8-FP8ATTN-CT-AutoRound
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查看详情 ↗Flint-DFlash2-GGUF
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查看详情 ↗GLM-5.3-DFlash2
incoaiThis repository contains the DFlash 2 draft model for `zai-org/GLM-5.3`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. DFlash 2 is also supported by vLLM v0.28.0 and later; see `incoai/GLM-5.3-NVFP4` for a vLLM serving example with the NVFP4-quantized target. See the blog post for more details.
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查看详情 ↗GLM-5.3-Flash-DFlash2
incoaiThis repository contains the DFlash 2 draft model for `zai-org/GLM-5.3-Flash`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. Benchmark numbers for this checkpoint are being re-measured and will be published here once final.
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查看详情 ↗GLM-5.3-NVFP4
incoaiThis repository contains an NVFP4-quantized version of Z.ai's `zai-org/GLM-5.3`. The weights and activations of the routed-expert linear layers are in NVFP4, and the KV cache is in FP8. The checkpoint uses the NVIDIA Model Optimizer format (`quant_method: modelopt`) and is served directly by SGLang and vLLM on NVIDIA Blackwell GPUs (SM100+). This checkpoint was produced with our in-house post-training quantization toolchain and exported in the NVIDIA Model Optimizer format. Only the weights and activations of the linear operators within the routed MoE experts are quantized to NVFP4, with static per-tensor activation scales. The KV cache is quantized to FP8 with static unit scales (`kv_cache_quant_algo: FP8`). The commands below enable speculative decoding with the DFlash 2 draft model (lossless, 7 draft tokens per verification step); remove the `--speculative-*` flags to serve without it.
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查看详情 ↗Muse-Glimmer-30B-DFlash2
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查看详情 ↗Ornith-1.5-122B-DFlash2
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查看详情 ↗Qwen3.6-35B-A3B-DFlash2
incoaiThis repository contains the DFlash 2 draft model for `Qwen/Qwen3.6-35B-A3B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. We compare autoregressive decoding, Qwen3.6's built-in MTP with seven speculative steps, and DFlash 2. Both speculative methods propose seven draft tokens per verification step.
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查看详情 ↗Qwen3.8-27B-DFlash2
incoaiThis repository contains the DFlash 2 draft model for `Qwen/Qwen3.8-27B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. The checkpoint is also mirrored at `z-lab/Qwen3.8-27B-DFlash2`. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. See the blog post for other engines and more details.
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查看详情 ↗Qwen3.8-27B-DFlash2-GGUF
incoaiThis repository contains GGUF conversions of `incoai/Qwen3.8-27B-DFlash2`, the DFlash 2 draft model for `Qwen/Qwen3.8-27B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. The checkpoints are also mirrored at `z-lab/Qwen3.8-27B-DFlash2-GGUF`. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. Build llama.cpp with DFlash 2 support (PR #27342):
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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。