VibeVoice-Large
aoi-ot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 Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 85001
distill-neucodec
aoiandroid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 Audio→ Output Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗gemma-4-E2B-it-GGUF
aoiandroid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 · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗parakeet-tdt-0.6b-v3
aoiandroid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 ↗translategemma-4b-it-GGUF
aoiandroid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 Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗T-lite-it-1.0-Q4_K_M-GGUF
aovchinnikov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 ↗search_slm_demo
aozalevsky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 ↗eds-pseudo-public
AP-HP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 ↗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 ↗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 ↗ds4-kolibri-1-gguf
aparusel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 ↗Qwythos-9B-Claude-Mythos-5-1M-GGUF
ApertureQA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 ↗DeepSeek-V4.1-Flash-1017-Abliterated-MLX
apetersson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-MixedQ2-GGUF
apeterssonRouted-expert requantisation of `deepseek-ai/DeepSeek-V4.1-Flash` revision `2bc89ac599031fa673cab993f1df02fc4a98c673`, produced on a 32-vCPU Runpod CPU pod with the pinned llama.cpp ggml reference quantisers (PR `ggml-org/llama.cpp#28696` head `cd628010bc3fc0a787d156c969d52a0789451c96`). Tensor names follow that PR's `deepseek4` table. This is a **weights artifact**. No runtime has yet been shown to execute it end to end; the "Runtime status" and "Smoke-test findings" sections say exactly what has been measured on which hardware, and what is still blocked. `SHA256SUMS` covers the five shards. The pack is deterministic: two independent runs produced byte-identical shard digests.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗> **Experimental low-bit model-surgery derivative.** This release is designed > to run DeepSeek-V4-Flash-0731 with its real three-stage DSpark drafter on a > 128 GB Apple Silicon machine. Structural validation has passed, but direct > generation, quality, refusal-removal, DSpark-acceptance, throughput and peak > memory measurements for this exact artifact are still pending. This is a DS4-native mixed-precision GGUF conversion of `apetersson/DeepSeek-V4-Flash-0731-Abliterated-FP8`, which is itself a rank-1 abliterated derivative of `deepseek-ai/DeepSeek-V4-Flash-0731`. It remains a derivative of the official post-trained 0731 model: it is not a base model, was not gradient-finetuned, and is not claimed to be universally "uncensored." The two GGUF files total approximately 86.34 GiB. The profile deliberately spends precision on dense paths used for every token while aggressively compressing the much larger routed-expert bank, leaving practical unified- memory headroom for DS4 runtime state and KV cache on a 128 GB host.
Input Text→ 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 ↗DeepSeek-V4-Flash-0731-Abliterated-FP8
apetersson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 ↗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-Flash-Vision-Exp-Abliterated
apetersson> [!WARNING] > Experimental research release. Text and focused image smoke tests pass, but > broader quality, safety, and production-runtime evaluation remains pending. This catalog contains the native abliterated checkpoint and two DS4 GGUF profiles. Both GGUF receivers use tensor types accepted by `antirez/ds4` main; neither requires the legacy Python vision sidecar for current native vision. `Basic128-Routed-IQ2_M` uses the closest familiar BPW class for the complete bundle: 2.915 BPW overall, close to llama.cpp's documented 2.9294 BPW for IQ2_M. The routed-expert subset averages 2.715 BPW, close to IQ2_S. This is a custom mixed recipe rather than llama.cpp's exact IQ2_M preset: routed gate/up tensors use IQ2_XXS, routed down tensors use Q2_K, ten routed layers retain MXFP4, and non-routed tensors include F32, F16, and Q8_0. See the llama.cpp quantization table for the reference BPW classes.
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3.8-Flash-Next-Abliterated
apetersson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 · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3.8-Flash-Next-Abliterated-Adapter
apetersson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 ↗Qwen3.8-Flash-Next-GGUF
apetersson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 · 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.