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All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (Qwen_Qwen3.5-397B-A17B-Q8_0) or download them all in place (./)
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
All quants made using imatrix option with dataset from here Run them directly with llama.cpp, or any other llama.cpp based project Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
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。
All quants made using imatrix option with dataset from here - llama.cpp - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
All quants made using imatrix option with dataset from here - llama.cpp - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (Qwen_Qwen3-Next-80B-A3B-Thinking-Q8_0) or download them all in place (./)
All quants made using imatrix option with dataset from here combined with a subset of combined_all_small.parquet from Ed Addario here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
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。
All quants made using imatrix option with dataset from here combined with a subset of combined_all_small.parquet from Ed Addario here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
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。
All quants made using imatrix option with dataset from here combined with a subset of combined_all_small.parquet from Ed Addario here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
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。
All quants made using imatrix option with dataset from here combined with a subset of combined_all_small.parquet from Ed Addario here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
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。
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All quants made using imatrix option with dataset from here combined with a subset of combined_all_small.parquet from Ed Addario here Run them directly with llama.cpp, or any other llama.cpp based project Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
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。
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Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.
