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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 And the model will output the answer. Very simple stuff, similar to other llava models, just make sure you use the correct binary! 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.

Visual QA
Input Text · Image→ Output Text
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And the model will output the answer. Very simple stuff, similar to other llava models, just make sure you use the correct binary! 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 ↗

**Model details:** - Parameter count: 28B - Input support: text, image (with mmproj file) - details - Speculative decoding: yes (MTP) - details - imatrix: yes - details - Perplexity/KLD measured: yes - details **Don't know which to choose?** Grab Q4_K_M (17.44GB) - usually a good mix of size and performance. Download instructions available here Here's some graphs showing the relative performance between the previous files and the new ones:

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

**Model details:** - Parameter count: 180B - Input support: text, image (with mmproj file) - details - Speculative decoding: yes (MTP draft model) - details - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (119.60GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

Visual QA
Input Text · Image→ 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: Click to view Q4_0_X_X information These are *NOT* for Metal (Apple) or GPU (nvidia/AMD/intel) offloading, only ARM chips (and certain AVX2/AVX512 CPUs).

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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