Apriel-1.6-15b-Thinker-GGUF
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View details ↗DISCOVER & COMPARE
Explore AI models 41
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View details ↗DeepSeek-R1-Distill-Qwen-7B-GGUF
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View details ↗Devstral-Small-2505-GGUF
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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 ↗Dolphin3.0-Mistral-24B-GGUF
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View details ↗Dolphin3.0-R1-Mistral-24B-GGUF
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View details ↗gemma-3-12b-it-GGUF
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View details ↗gemma-3-12b-it-pruned-GGUF
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View details ↗gemma-4-26B-A4B-it-GGUF
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View details ↗gemma-4-E2B-it-GGUF
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View details ↗gemma-4-E4B-it-GGUF
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View details ↗GLM-4.6V-Flash-GGUF
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View details ↗granite-4.1-8b-GGUF
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View details ↗Hammer2.1-7b-GGUF
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View details ↗IQ2_NL-IQ3_NL-Test-Models
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View details ↗JQ2_K-JQ6_K-Test-Models
eaddarioQuantization test results for **K2-Horizon-7B** (dense) and **LFM2.5-8B-A1B** (MoE), comparing standard llama.cpp quantization against a fork that adds five new non-linear super-block types: **JQ2_K** to **JQ6_K**. On both models, four of the five new types perform materially better than their counterpart on every measured metric (mean and 99% KLD, Δp RMS, top-1 agreement). `JQ2_K` is the exception. The size of the gain depends on the model: on the dense `K2-Horizon-7B`, `JQ3_K` and `JQ6_K` cut the mean KLD by more than half, while on the MoE `LFM2.5-8B-A1B` the improvements range from 13.8% to 23.2%. The five new types are non-linear (NL) super-block (K) types. Each new type spends exactly the same index payload as its K-quant counterpart:
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View details ↗K2-Horizon-7B-GGUF
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View details ↗K2-Horizon-MoVA-36B-A4B-GGUF
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View details ↗Llama-Guard-3-8B-GGUF
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View details ↗MiniCPM5-1B-GGUF
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View details ↗MiniCPM5-2B-GGUF
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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.
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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.
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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.