canvasAIEdit
MHM0321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
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 85003
cosmos-humanizer-gguf
Mhmadhassnz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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗flux-hyp8
mhnakif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
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗flux-schnell-dev-merge-gguf
mhnakif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
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗fluxunchained-dev
mhnakif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
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-1.7B-Base-opd
mhseo2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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-4B-Base-opd
mhseo2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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-4B-OT3-2ep-opd
mhseo2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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-8B-Base-opd
mhseo2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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗MiniMax-H3
MHWJJ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
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗EAQ v1 (EXL3 format) quantization of deepseek-ai/DeepSeek-V4.1-Flash-1017 (revision `1bae0e10`, the refreshed V4.1-Flash weights), converted with exllamav3 `v1.6.1` plus the EAQ patch set (converter version 1.6.1) and a port of the DeepSeek-V4.1 architecture (see Conversion). Teacher-forced next-token fidelity to the original source weights was measured in a shared evaluation port; see **Quality vs the original model**. This is **not** a drop-in replacement for the original FP8/FP4 checkpoint. The Engram embedding tables are **not** in this repo: you still need shards 47 and 48 from the base model (about 95 GiB each). **Format: plain EXL3 (EAQ v1).** EAQ v1 currently uses the standard EXL3 format unchanged: `quant_method` is `exl3`, codebook `mul1`, `out_scales=always`, and the tensor keys and the main-model per-tensor bit widths are identical to the standard EXL3 2.9 bpw builds of this model (checked against LibertAIDAI/DeepSeek-V4.1-Flash-1017-EXL3-2.9bpw: 192,495 tensor keys; the widths of all 46,702 main-model quantized tensors match; tokenizer files byte-identical). The 46 MTP / DSpark dense tensors differ: 4 bit here, 6 bit in the LibertAI build. What EAQ v1 changes is only how the weights were produced (the EAQ conversion; see Conversion). Any runtime that loads EXL3 DeepSeek-V4.1 checkpoints should load this one without EAQ-specific code, including the TensorFold DeepSeek-V4.1-Flash-1017 2x DGX Spark recipe (set its `MODEL_ID` to this repo).
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DeepSeek-V4.1-Flash-EXL3-2.9bpw
Mia-AiLab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。
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DeepSeek-V4.1-Flash-EXL3-3.0bpw
Mia-AiLab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
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Gemmable-4-12B-MTP-GGUF
Mia-AiLabGemmable 4 12B is a GGUF export of Gemma 4 12B fine-tuned on Fable-5 style reasoning and assistant traces. - Base model: `google/gemma-4-12B` - Format: GGUF - Training style: Fable-5 style reasoning and assistant traces - Distribution: fp16 GGUF plus matching assistant GGUFs for each quant - Intended use: local inference, coding, reasoning, and assistant workflows 1. Search this repo, download target + mtp file. 2. Load target. 3. Load settings → Speculative Decoding → select mtp file file.
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Gemmable-4-31B-MTP-GGUF
Mia-AiLab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
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗GLM-5.3-EXL3-2.75bpw-TensorFold
Mia-AiLab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
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗GLM-5.3-Flash-EXL3-4bpw-TensorFold
Mia-AiLabMia's AI Lab's own EXL3 quantization of zai-org/GLM-5.3-Flash, built for TensorFold and served by the GLM-5.3-Flash EXL3 TensorFold recipe for DGX Sparks. **Lower KL divergence than the widely used TR3-4bpw quant on all six test sets, at the same size, speed and memory, measured against Z.AI's official FP8 release.** Same format and bit width; only the calibration is new. KL divergence measures how far a quant's next-token probabilities drift from the reference's, Z.AI's FP8 release (lower is better). This quant lowers it on all six sets: by 11-27% with only the routed experts quantized, and by 2-18% as TensorFold actually serves it, with its own 4-bit dense weights on top.
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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。
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
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.
MODEL HANDBOOK
Understand before you choose
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.