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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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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 - Atomic Chat Note: if it's a newly supported model, you may need to wait for an update from the developers.

Visual QA
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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: The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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: The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. 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:

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. 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:

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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 (zai-org_GLM-4.5-Air-Q8_0) or download them all in place (./)

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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:

Visual QA
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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。

Basic profile indexed · More details to follow

Visual QA
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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.

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
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Cost analysis

Adjust monthly token usage to compare estimated API costs.

1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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How should API prices be compared?+

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.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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