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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 16
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价格待核验多模态与渠道费用须分别核对
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查看详情 ↗KAT ships **`mtp_num_hidden_layers: 0`** — no draft head. These builds graft Qwen3.6-35B-A3B's **original** MTP head onto KAT's trunk, quantized with an imatrix calibrated on KAT's own output. Includes the **bf16 master** so you can build any tier yourself without a 69 GB safetensors pull or a conversion. We fine-tuned this head twice on KAT's own rollouts. **Both fine-tunes made it worse.** Measured live on 79 configs, same tier, same flags, only the head differing:
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查看详情 ↗Laguna-XS-2.1-APEX-GGUF
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查看详情 ↗Five ICE 2.0 tiers of **openbmb's MiniCPM-V 4.7 35B-A3B** (35B total, 3B active, Qwen3.5-MoE text tower + SigLIP2 vision), **uncensored trunk** (openbmb's BF16 + Atomic Chat's refusal-direction LoRA, merged), quantized against **Sangreal** — a purpose-built 77-bucket calibration corpus — and shipping **Qwen-Sharp v22.5.0-arrow4.0-minicpm** — our agent-tuned chat template, a heavily modified derivative of Qwen-Sharp, fitted to MiniCPM-V's multimodal content format (spec below). Vision works through openbmb's own projector, shipped here unmodified. **Sangreal-UD** — Unsloth Dynamic 2.0 replays (with Sangreal10K and default chat-template) — on the same trunk are shipped as well. > ⚠️ **Uncensored.** Refusal behaviour has been suppressed in the > weights. Sandbox it at the OS level and control its network and code-execution access; with no refusal > backstop, a prompt injection from a hostile page or third-party code has nothing to stop it. All five tiers ship as **ICEv2**: `12G-ICEv2`, `15G-ICEv2`, `19G-ICEv2`, `23G-ICEv2` and `25G-ICEv2`.
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查看详情 ↗Five ICE 2.0 tiers of **nex-agi's Nex-N2.5-mini** (35B total, 3B active, Qwen3.5-MoE architecture), **abliterated trunk by OrcaRouter**, quantized against **Sangreal** — a purpose-built 77-bucket calibration corpus — with an importance matrix measured on Nex itself, and shipping **Qwen-Sharp v22.5.0-arrow4.0** — our agent-tuned chat template, a heavily modified derivative of peculiar-ragdoll's Qwen-Sharp (spec below). Vision works through this trunk's own projector, `mmproj-Nex-N2.5-mini-OrcaRouter-Q8_0.gguf`. > ⚠️ **Uncensored.** This is an abliterated checkpoint — refusal behaviour has been suppressed in the > weights. Sandbox it at the OS level and control its network and code-execution access; with no refusal > backstop, a prompt injection from a hostile page or third-party code has nothing to stop it. All five tiers now ship as **ICEv2**: `12G-ICEv2`, `15G-ICEv2`, `19G-ICEv2`, `23G-ICEv2` and `25G-ICEv2`.
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查看详情 ↗Four ICE 1.6 tiers of **nex-agi's Nex-N2.5-mini** (35B total, 3B active, Qwen3.5-MoE architecture), quantized against **Sangreal** — a purpose-built 77-bucket calibration corpus — with an importance matrix measured on Nex itself, and shipping **Qwen-Sharp v22.5.0-arrow4.0** — our agent-tuned chat template, a heavily modified derivative of peculiar-ragdoll's Qwen-Sharp (spec below). Vision works through Nex's own projector, `mmproj-Nex-N2.5-mini-Q8_0.gguf`. Mean KLD to BF16, one session. **Size-matched** = bartowski's imatrix ladder for the same model, interpolated (log-linear) at each ICE file's exact byte size; negative = ICE closer to BF16. **Paired** = the same 64 chunks scored for both files against the same reference. Code at 16K context (`code.test.raw`, n_ctx 16384 × 8), mean KLD to BF16 vs file size; lower-left is better. Raw numbers in Table 2 below and in `measurements/2026-10-03/`.
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查看详情 ↗Four ICE tiers of **huihui-ai/Huihui-Ornith-1.5-35B-A3B-abliterated**, quantized against a **trunk-matched** importance matrix, with the model's own trained **MTPv2** head and the **Qwen-Sharp v22.4.1** chat template embedded. The BF16 master they were all built from is published here too. > ⚠️ **Uncensored model.** This is an abliterated checkpoint: refusal behaviour has been > suppressed in the weights. It will comply with requests a stock model declines. You are > responsible for how you use it. Sandbox it at the OS level and control its network and > code-execution access — with no refusal backstop, a prompt injection from a hostile page > or third-party code has nothing to stop it. If you want the guardrailed equivalent, use > the non-abliterated TIEL-Calibrated ICE tiers. Rule of thumb: take the largest tier that leaves you the context you actually need at `q8_0` KV. If you already run a `UD` build of this model, pick the ICE tier at the same size and keep everything else the same.
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查看详情 ↗Nine Pareto-optimal tiers of the abliterated model, each with an **MTP head grafted in** from the original Ornith-1.5 (the abliterated source ships none). Measured on wikitext-2-raw, 16 chunks x 2048 ctx, against **two** references, so quantization damage and abliteration damage can be told apart. For scale, the *best* quantization in this ladder costs ~0.022 KLD. **Abliteration is a smaller perturbation than Q6_K quantization.**
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查看详情 ↗GGUF quantizations of huihui-ai's abliterated Ornith-1.5-35B-A3B, made with a Sangreal 36,025-chunk importance matrix, ICE 1.6 per-tensor recipes and ICE 2.0 post-quantization expert repair. Every file keeps the MTP draft layer. The ICE files carry peculiar-ragdoll's v22.5.0 Qwen-Sharp chat template with gbuzhf's arrow4.0 harness operator wrapper. > ⚠️ **Uncensored.** This is an abliterated checkpoint — refusal behaviour has been > suppressed in the weights. Sandbox it at the OS level and control its network and > code-execution access; with no refusal backstop, a prompt injection from a hostile > page or third-party code has nothing to stop it. Score: F16 = 100; 70 % full-distribution fidelity `1/(1+KLD)` + 30 % same-top-1, averaged over the three sets.
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查看详情 ↗Sage-A1.5-Sangreal-ICE
gbuzhfGGUF quantizations of InternScience/Sage-A1.5, made with a Sangreal 10k importance matrix, ICE 1.6 per-tensor recipes and ICE 2.0 post-quantization expert repair. Every file keeps the MTP draft layer; the vision projector ships separately (InternScience's F16, plus a Q8_0). The ICE files carry the Arrow 4.0-sage chat template. Score: F16 = 100; 70 % full-distribution fidelity `1/(1+KLD)` + 30 % same-top-1, averaged over the three sets. The MTP head is in every file; on a llama.cpp build with MTP support, enable it for speculative decoding (draft depth 1). A Q8_0 of the projector (`mmproj-Sage-A1.5-Q8_0.gguf`) can be used in place of the F16 one.
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查看详情 ↗SNEX-V1
gbuzhf**SNEX-V1 is Sage-A1.5 with a small, sparse transplant of Nex-N2.5-mini's training.** Sage is the body; from Nex only the largest changes its own training made are carried over, at a quarter of their strength. Everything else is Sage's: the vision projector, the MTP draft head, the router, the norms, the embeddings. Score: BF16 = 100; 70 % full-distribution fidelity `1/(1+KLD)` + 30 % same-top-1, averaged over the three sets. **1. Lineage first.** Weight comparisons placed the three models: Sage-A1.5 is a fine-tune of Qwen3.6-35B-A3B; Nex-N2.5-mini is a fine-tune of Qwen3.5-35B-A3B; Qwen3.6 continues Qwen3.5. So Nex's own training is `τ = Nex − Qwen3.5`. Taking the difference against Qwen3.6 instead would also subtract the 3.5 → 3.6 upgrade that Sage already carries.
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。