AR1
anthum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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 84952
AR2
anthum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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗AR3
anthum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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗orthrus-4-track
antichronology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。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-1.7B-GGUF
Antigma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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-14B-Base-GGUF
Antigma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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Flux-Klein-NSFW-Lora
AntiLeecher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。
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3.5-2B-Hermes-Meta-Ads
Antilimitless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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗deepseek-v4.1-flash-gguf
antirezCalibrated weights for the Metal implementation in DwarfStar, developed on the `ds4.1flash` branch. These are not interchangeable with DeepSeek V4 Flash weights or its DSpark drafter. The language GGUFs each include 188.83 GiB of native FP8 Engram tables. DwarfStar reads the needed rows directly from disk; it never makes the whole table resident. Keep the GGUF on a fast local SSD. The main-weight sizes above do not include context or runtime buffers. Use SSD streaming on a single 128 GB Mac. Q2 also fits resident tensor parallelism across two 128 GB Macs with RDMA, or full residency on a larger Mac. Q4 needs SSD streaming on smaller Macs; the resident target is a 512 GB Mac. It does not fit resident TP across two 128 GB Macs.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗deepseek-v4-gguf
antirezThis quants are specific for the DS4 inference engine. They may work with other inference engines or not (they should, but not the MTP model which requires a specific loader). Use **q2** on 128 GB Mac machines, **q4** on machines with ≥ 256 GB RAM, pair either with **MTP** for optional speculative decoding. For the **q4** file, only the three routed-expert classes change to `Q4_K`. Everything else is byte-for-byte identical to the q2 recipe.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗glm-5.2-gguf
antirez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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗glm-5.3-flash-gguf
antirez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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗glm-5.3-gguf
antirez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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗qwen3.8-flash-next-gguf
antirezSelf-contained GGUFs for DwarfStar, with original BF16 n-grams read directly from disk. Keep the file on a local SSD. No n-gram sidecar is needed. MTP weights are included; enable them with `--mtp`. Requires DwarfStar's native BF16 n-gram reader. Older versions expecting `--ple` cannot load these files. Each file contains the same 95.37 GiB BF16 n-gram table. It is not made resident or quantized. Context and runtime buffers require additional RAM. The main/MTP tensor bytes come unchanged from Ivan Fioravanti's Q2 and Q4 releases. Q2 has imatrix-calibrated IQ2_XXS gate/up and Q2_K down routed experts, with the down rows padded from 640 to 768 inputs. Q4 has calibrated Q4_K gate/up and MXFP4 down. Other tensor formats and MTP weights are unchanged.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗spam-detector-bert-MoE-v2.2
AntiSpamInstitute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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗belgpt2
antoinelouis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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗biencoder-camembert-base-mmarcoFR
antoinelouis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。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗colbert-xm
antoinelouisThis is a ColBERT model that can be used for semantic search in many languages. It encodes queries and passages into matrices of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. The model uses an XMOD backbone, which allows it to learn from monolingual fine-tuning in a high-resource language, like English, and perform zero-shot retrieval across multiple languages. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`: We use the English training samples from the MS MARCO passage ranking dataset, which contains 8.8M passages and 539K training queries. We do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the msmarco-hard-negatives distillation dataset. Our final training set consists of 6.4M (q, p+, p-) triples.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗crossencoder-camembert-base-mmarcoFR
antoinelouis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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗crossencoder-camembert-large-mmarcoFR
antoinelouis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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗crossencoder-distilcamembert-mmarcoFR
antoinelouisThis is a cross-encoder model for French. It performs cross-attention between a question-passage pair and outputs a relevance score. The model should be used as a reranker for semantic search: given a query and a set of potentially relevant passages retrieved by an efficient first-stage retrieval system (e.g., BM25 or a fine-tuned dense single-vector bi-encoder), encode each query-passage pair and sort the passages in a decreasing order of relevance according to the model's predicted scores. Here are some examples for using the model with Sentence-Transformers, FlagEmbedding, or Huggingface Transformers. The model is evaluated on the smaller development set of mMARCO-fr, which consists of 6,980 queries for which an ensemble of 1000 passages containing the positive(s) and ColBERTv2 hard negatives need to be reranked. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k). To see how it compares to other neural retrievers in French, check out the *DécouvrIR* leaderboard.
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗crossencoder-mdebertav3-base-mmarcoFR
antoinelouis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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗crossencoder-mMiniLMv2-L12-mmarcoFR
antoinelouis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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
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.