DomURLs_BERT
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View details ↗DISCOVER & COMPARE
Explore AI models 85001
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View details ↗⚡ Each donation funds the next large quant. I host free GGUF or MoE quants as independent research. Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro. Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell , typically $100 per quant. Released as part of the **NOESIS Professional Multilingual Dubbing Automation Platform** (framework: Deterministic Hybrid Control Framework for Frozen Neural Operators — DHCF-FNO). Repack (BF16 safetensors, shards ≤50 GB) and community GGUF quantizations of yandex/AliceAI-Foundation-80B-A3B-Base (`model_type: alice_ai`, 80B total / 3B active MoE with KDA layers).
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View details ↗DeepSeek-V4.1-Flash-1017-FP8-GGUF
AMAImedia⚡ Each donation funds the next large quant. I host free GGUF or MoE quants as independent research. Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro. Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell , typically $100 per quant. Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators) **DeepSeek-V4.1-Flash-1017** is a minor update to **DeepSeek-V4.1-Flash**, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.
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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 ↗GLM-5.3-Flash-FP8-GGUF
AMAImedia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 ↗Ling-3.1-Flash-1019-FP8-GGUF
AMAImedia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-BPW2.5-GGUF
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View details ↗MiniMax-M3.1-Flash-NVFP4-GGUF
AMAImedia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 ↗Naive-N0.5-Flash-Mixed-Quant-GGUF
AMAImedia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 ↗NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16
AMAImedia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-BF16-GGUF
AMAImedia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 ↗AMALIA-9B-0626-DPO
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View details ↗AMALIA-9B-0626-SFT
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View details ↗AMALIA-VL-DPO
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View details ↗ACE-Step-1.5-GGUF-fork
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View details ↗music-flamingo-hf-i1-GGUF-fork
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View details ↗parakeet-cpp-gguf-fork
amandabenson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
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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.