gemma-3-1b-it-quantized.w8a8
RedHatAI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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 271
gemma-3-27b-it-FP8-dynamic
RedHatAI- **Model Architecture:** gemma-3-27b-it - **Input:** Vision-Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 2/24/2025 - **Version:** 1.0 - **Model Developers:** Neural Magic Quantized version of google/gemma-3-27b-it. This model was obtained by quantizing the weights of google/gemma-3-27b-it to FP8 data type, ready for inference with vLLM >= 0.5.2.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-3-27b-it-quantized.w4a16
RedHatAI--- tags: - vllm - vision - w4a16 license: gemma base_model: google/gemma-3-27b-it library_name: transformers --- - **Model Architecture:** google/gemma-3-27b-it - **Input:** Vision-Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Activation quantization:** FP16 - **Release Date:** 6/4/2025 - **Version:** 1.0 - **Model Developers:** RedHatAI Quantized version of google/gemma-3-27b-it.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-3-4b-it-FP8-dynamic
RedHatAI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。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-3-4b-it-quantized.w4a16
RedHatAI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。
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-3-4b-it-quantized.w8a8
RedHatAI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。
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-12B-it
RedHatAI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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-12B-it-FP8-Dynamic
RedHatAIThis is a preliminary version (and subject to change) of FP8_Dynamic quantized google/gemma-4-12B-it model. The model has both weights and activations quantized to FP8_Dynamic format with vllm-project/llm-compressor. Run this script with this LLM Compressor PR and latest transformers to quantize the model using iMatrix quantization Evals: | model_name | flexible-extract | strict-match | bits_per_byte | byte_ppl | |---------------|------------------|--------------|---------------|----------| | baseline-bf16 | 0.9082 | 0.8958 | 1.9125 | 3.7645 | | FP8-RTN | 0.9115 | 0.8999 | 1.9368 | 3.8285 | | *FP8-iMatrix* | 0.9198 | 0.9032 | 1.9056 | 3.7465 | | FP8-GPTQ | 0.9098 | 0.8950 | 1.9357 | 3.8257 |
译文排队中 · 当前显示来源原文
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-12B-it-NVFP4
RedHatAIThis is a preliminary version (and subject to change) of NVFP4 quantized google/gemma-4-12B-it model. The model has both weights and activations quantized to NVFP4 format with vllm-project/llm-compressor. Run this script with this LLM Compressor PR and latest transformers to quantize the model using GPTQ
译文排队中 · 当前显示来源原文
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-26B-A4B-it
RedHatAI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。
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-26B-A4B-it-FP8-dynamic
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 2026-04-04 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of google/gemma-4-26B-A4B-it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model. This model was obtained by quantizing the weights and activations of google/gemma-4-26B-A4B-it to FP8 data type using dynamic per-token quantization, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-26B-A4B-it-NVFP4
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **Release Date:** 2026-04-04 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of google/gemma-4-26B-A4B-it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model. This model was obtained by quantizing the weights and activations of google/gemma-4-26B-A4B-it to NVFP4 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-31B-it
RedHatAI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。
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-31B-it-FP8-block
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 2026-04-04 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of google/gemma-4-31B-it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model. This model was obtained by quantizing the weights and activations of google/gemma-4-31B-it to FP8 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-31B-it-FP8-dynamic
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 2026-04-04 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of google/gemma-4-31B-it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model. This model was obtained by quantizing the weights and activations of google/gemma-4-31B-it to FP8 data type using dynamic per-token quantization, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-31B-it-NVFP4
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **Release Date:** 2026-04-04 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of google/gemma-4-31B-it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model. This model was obtained by quantizing the weights and activations of google/gemma-4-31B-it to FP4 data type using the NVFP4 format, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-31B-it-speculator.dflash
RedHatAI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。
输入 待核验→ 输出 待核验
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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-31B-it-speculator.dspark
RedHatAI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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-31B-it-speculator.eagle3
RedHatAIThis is a preliminary model release, we will continue to train the model and improve the acceptance rates in the next few days. This is a speculator model designed for use with google/gemma-4-31b-it, based on the EAGLE-3 speculative decoding algorithm. It was trained using the Speculators library on a combination of the Magpie-Align/Magpie-Llama-3.1-Pro-300K-Filtered dataset and the `train_sft` split of the HuggingFaceH4/ultrachat_200k dataset. Training data used **Magpie + UltraChat with responses from the gemma-4-31B-it model (no reasoning)**. This model should be used with the google/gemma-4-31b-it chat template, specifically through the `/chat/completions` endpoint.
译文排队中 · 当前显示来源原文
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-E4B-it
RedHatAI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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗GLM-5.2-FP8
RedHatAI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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗GLM-5.2-NVFP4-FP8
RedHatAI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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
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。