Qwen3.6-27Bcmf
infosave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 Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 84999
Qwen3.6-35B-A3B-Escha-W2cmf
infosave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 Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3.6-35B-A3Bcmf
infosave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 Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3.8-27B-cmf
infosaveQwen3.8-27B (48 GatedDeltaNet linear-attention layers + 16 full-attention layers, 262k context, thinking mode) packaged as a single CMF file. It runs with `cortiq`, a Rust inference engine with no Python and no ML framework: NVIDIA, AMD and Intel GPUs through Vulkan/DX12, Apple silicon through Metal, and the CPU. Use cortiq 0.8.15 or later for fast prompt processing; prebuilt binaries for Linux, macOS and Windows are on the releases page. The engine picks the GPU and its settings itself. `--no-think` asks for a direct answer; without it the model reasons first, so raise `--max-tokens` (default 256). The 4-bit files are quantized directly from the bf16 checkpoint. Perplexity: twelve 512-token windows of wikitext-2.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3.8-Flash-Next-cmf
infosaveQwen3.8-Flash-Next — a 125B hybrid mixture-of-experts model, 6B active per token (36 Gated DeltaNet + 12 sparse-attention layers, 512 experts per layer, 51B n-gram table, 262k context, thinking mode) — as CMF files, text only. It runs with `cortiq`, a Rust engine with no Python. Experts stream from the memory-mapped file as tokens need them, so the default 77 GB file runs on one GPU (measured from a 12 GB VRAM budget up to a whole 32 GB card) with the rest in system RAM. **RTX 3090 (24 GB), cortiq 0.8.15: 49.1 tok/s decode; first token after a 1,000-token prompt in 9.9 s.** RTX 5090 (cortiq 0.8.8): 81.7 tok/s, 108.6 with speculative decoding. Use cortiq 0.8.15 or later (prebuilt binaries). Without `--no-think` the model reasons first; raise `--max-tokens` (default 256). Each `cortiq run` loads the model again (10–30 s to the first token); for chat, keep it loaded with `cortiq serve`.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Spark-X2.5-cmf
infosaveSpark-X2.5-4B and Spark-X2.5-1.7B (hybrid attention: three 512-token sliding-window layers per full layer, 1M-token context, thinking mode, tool calling, 200+ languages) packaged as single CMF files. They run with `cortiq`, a Rust inference engine with no Python and no ML framework: NVIDIA, AMD and Intel GPUs through Vulkan/DX12, Apple silicon through Metal, and the CPU. Use cortiq 0.8.12 or later; prebuilt binaries for Linux, macOS and Windows are on the releases page. The engine picks the GPU and its settings itself. `--no-think` asks for a direct answer; without it the model reasons in a ` ` block first, so raise `--max-tokens` (default 256). All files are quantized from the bf16 checkpoints. Perplexity: the first 4096 tokens of wikitext-2 test with the BOS token in front; the unquantized models score 10.61 (4B) and 14.49 (1.7B; transformers in float32 gives the same 14.49). Spark is sensitive to 4-bit attention and down projections, so only gate/up are 4-bit in the compact files.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Spark-X2.6-4B-cmf
infosave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 ↗Ternary-Bonsai-2-27B-cmf
infosave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 Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗soc-triage-nemotron-bolt-e4b-lora-gguf
infosecn1nja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 ↗NT-Java-1.1B-GGUF
InfosysEnterpriseGGUF, introduced by the llama.cpp team on August 21st, 2023, is a new format designed to replace the outdated GGML, which is no longer maintained by llama.cpp. GGUF boasts several improvements over GGML, such as enhanced tokenization, support for special tokens, and metadata capabilities. It is also designed with extensibility in mind. The NT-Java-1.1B GGUFs are supported by llama.cpp and are compatible with a range of third-party user interfaces and libraries. For a detailed list, please refer to the beginning of this README. **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗InfoVis
InfoVis**InfoVis** is a unified 9B model for slide image generation and editing, fine-tuned from FLUX.2-klein-base-9B. One model handles text-to-slide generation, template-conditioned generation, multi-image grounded generation, and instruction-based slide editing. This repository contains the InfoVis transformer in diffusers format. The text encoder (Qwen3) and the VAE are loaded from the base model. Try InfoVis in the browser in the demo Space, or run it locally with the inference script from our code repository:
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗modelo-ajustado-sena
inghggw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 ↗vit-frijol
inghggw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 Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗qwen-image-2512-fp8-scaled-comfyui
ingtellect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 Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Swift1.5-Qwen3.8-Flash-Next-Sushi-2.6bpw
initialed85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 · Image→ Output Text
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 Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗f5-shan-tts-model
innkl24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 ↗InnoSpark3.0-397B-q8
innospark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。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗eedgym-ckpts
inq-android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 ↗LongCat-Flash-Lite-GGUF
InquiringMinds-AI> **Custom fork required.** This model uses a novel architecture (MLA + MoE with identity experts + N-gram embeddings) that is not supported by upstream llama.cpp. You must build from the `longcat-flash-ngram` branch of the linked fork. LongCat-Flash-Lite is a 68.5B parameter Mixture-of-Experts language model from Meituan, with only 3–4.5B parameters activated per token. It combines three architectural innovations that make it unusually efficient: Two upstream llama.cpp PRs attempted to add this architecture: - PR #19167 (ngxson) — N-gram embedding support, blocked by base model not yet being supported - PR #19182 (ngxson) — LongCat-Flash base architecture, abandoned after maintainers deemed identity experts too complex
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗BgGPT-7B-Instruct-v0.2
INSAIT-Institute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 ↗BgGPT-Gemma-2-9B-IT-v1.0-GGUF
INSAIT-Institute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 ↗BgGPT-Gemma-3-12B-IT-GGUF
INSAIT-Institute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 · Image→ Output Text
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.