Trellis2-GGUF
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View details ↗DISCOVER & COMPARE
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Qwen-Image-2.1-Uncensored-GGUF
Aero2026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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View details ↗Atlas-2.1-Small
AeroAIAviation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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View details ↗detr-finetuned-chess
aesat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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View details ↗GLM-5.3-Flash-GGUF
AesSedai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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View details ↗GLM-5.3-GGUF
AesSedai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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View details ↗Kimi-K2.7-Code-GGUF
AesSedai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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View details ↗MiMo-V2.6-Flash-MOPD-GGUF
AesSedai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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View details ↗MiMo-V2.6-Pro-MOPD-GGUF
AesSedaiThis repo contains specialized MoE-quants for XiaomiMiMo/MiMo-V2.6-Pro-MOPD. The idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization. To that end, the quantization type default is kept in high quality and the FFN UP + FFN GATE tensors are quanted down along with the FFN DOWN tensors. The BPW quants were produced using ed's bpw-size PR. The FFNs are the primarily quantized feature, rest of the model remains in Q8_0 / Q6_K
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View details ↗MiniMax-M3-GGUF
AesSedai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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View details ↗Qwen3.5-122B-A10B-GGUF
AesSedai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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View details ↗Qwen3.5-35B-A3B-GGUF
AesSedai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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View details ↗Qwen3.5-397B-A17B-GGUF
AesSedai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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View details ↗Qwen3.6-35B-A3B-GGUF
AesSedai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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View details ↗Qwen3.8-Flash-Next-GGUF
AesSedaiThis repo contains specialized MoE-quants for Qwen/Qwen3.8-Flash-Next. The idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization. To that end, the quantization type default is kept in high quality and the FFN UP + FFN GATE tensors are quanted down along with the FFN DOWN tensors.
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View details ↗Qwen3.8-Flash-Next-GGUF
AetherKelly00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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View details ↗PicoLM-80M-Instruct
aethertp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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View details ↗PicoLM-V2.1-81M-Instruct
aethertp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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View details ↗PicoLM-V2-81M-Instruct
aethertp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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View details ↗PicoLM-V3-82M-Instruct
aethertp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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View details ↗PicoLM-V3-Pro-82M-Instruct
aethertp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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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。
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View details ↗sdxl-unet-q8-gguf
AEUPH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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View details ↗bert-base-swedish-uncased
af-ai-center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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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
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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.