Krea-2-Raw
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View details ↗DISCOVER & COMPARE
Explore AI models 84999
3D-PLOT-LLM-7B
jintangx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 ↗Jinx-gpt-oss-20b-GGUF
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View details ↗flux3-agibot-looped
Jinyan0924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 ↗R-Quest-Qwen3-4B-Validity-RL
jinyuan222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 ↗qwen3-0.6b-bnc
Jinyuan6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 ↗MinerU2.5-Pro-2605-1.2B-GGUF
jinzhenj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 ↗SparkVSR
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View details ↗tiny_random_llama2
Jiqing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 ↗tiny-random-qwen2
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View details ↗tiny-random-tvp
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View details ↗TEOChat
jirvin16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 ↗emotion-english
jitesh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 ↗- a model that feels less censored than stock chat releases - a model that is more capable than the raw base on practical text workloads - a compact local GGUF that still serves quickly on Apple Silicon - Uncensored chat behavior without forcing every prompt into coding mode - Tuned from the strongest `fast` line instead of the raw base - Neutral chat template baked into the GGUF to reduce prompt-routing bugs - Verified on Apple Silicon with clean general-chat and coding responses - Base model: `google/gemma-4-26B-A4B-it` - Format: `GGUF Q4_K_M` - General Korean prompt speed: `222.0 tok/s` - Generation speed: `89.4 tok/s` - Derived from the verified `SuperGemma Fast` MLX line
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View details ↗Do not pass `--chat-template /path/to/chat_template.jinja` as a literal path string on launch paths that expect the template body. That can corrupt responses.
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View details ↗SuperGemma4-31b-abliterated-GGUF
Jiunsong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 ↗supergemma4-e4b-abliterated-mlx
Jiunsong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
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.