Qwen2-0.5B-Instruct
litert-community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 85001
Qwen2.5-0.5B-Instruct
litert-communityThis model provides a few variants of Qwen/Qwen2.5-0.5B-Instruct that are ready for deployment on Android using the LiteRT (fka TFLite) stack and MediaPipe LLM Inference API (in maintenance-only mode). *Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device.* The source of this demo app has been removed from the MediaPipe samples repository. This repository has no `.litertlm` file for LiteRT-LM.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen2.5-1.5B-Instruct
litert-communityThis model provides a few variants of Qwen/Qwen2.5-1.5B-Instruct that are ready for deployment on Android using the LiteRT (fka TFLite) stack, MediaPipe LLM Inference API (in maintenance-only mode) and LiteRT-LM. *Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device.* * Download or build the app from GitHub.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen2.5-Coder-1.5B-Instruct
litert-community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 ↗Qwen2.5-Coder-3B-Instruct
litert-community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 ↗Qwen2-VL-2B
litert-communityLiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). Qwen/Qwen2-VL-2B-Instruct converted to the **LiteRT-LM** (`.litertlm`) format for **on-device image+text** inference with Google's LiteRT-LM runtime — the first Qwen2-VL-family VLM in this format. Qwen2-VL is Alibaba's widely-used general-purpose vision-language model: a dynamic-resolution ViT vision encoder feeds the **Qwen2-1.5B** language decoder, giving strong general VQA, description, and document/OCR reading in a phone-sized package. This bundle runs the whole thing through LiteRT-LM's `fast_vlm` multimodal path — give it an image and a question, get a grounded answer, fully on-device.
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-0.6B
litert-communityThis repository contains LiteRT-LM variants of Qwen3-0.6B for Android and desktop deployment. The mixed INT4 `.litertlm` artifact was produced with a TorchAO-based quantize-first recipe from the original Hugging Face checkpoint. This is a mixed quantization layout rather than a uniform all-INT4 model: eligible linear projection weights are stored as blockwise INT4 with group size 32 and floating-point scales, token embedding weights use weight-only INT8 quantization, and normalization/reduction paths plus KV cache tensors remain floating point. The mixed INT4 bundle also uses LiteRT-LM StableHLO composite ops for attention/cache execution, including `odml.runtime_bmm` and `odml.cache_update`.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-0.6B-int4
litert-communityOn-device **INT4** builds of Qwen/Qwen3-0.6B in the **`.litertlm`** format for Google's LiteRT-LM runtime (Android / iOS / desktop / embedded). At ~332 MB each (≈7× smaller than the fp32 source), these load fast and keep a small memory footprint on phones. Qwen3 is a hybrid reasoning model. Because LiteRT-LM bakes the prompt format into the file, the thinking mode is fixed per file, so this repo ships **both**:
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-1.7B
litert-communityThis repository contains LiteRT-LM variants of Qwen/Qwen3-1.7B optimized for on-device text generation. `Qwen3-1.7B_dynamic_wi4b32_afp32.litertlm` is a dynamic INT4 variant (block-32 weights, FP32 activations). It was converted through the LiteRT Torch (`litert-torch`) path and quantized with AI Edge Quantizer. This artifact incorporates LiteRT-LM GPU graph optimizations, including composite ops for RoPE, fused QKV, and fused Gate/Up projections, and is configured with static prefill memory allocation. Both files: Need litert-lm 0.18.0 or newer (tested on 0.18.0) and carry LiteRT-LM's official qwen3 configuration, so thinking is on unless `enable_thinking` is false, the reasoning arrives on the thought channel, and tool calls are parsed by the runtime.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-14B
litert-community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 ↗Qwen3-4B
litert-communityThis repository contains LiteRT-LM variants of Qwen/Qwen3-4B optimized for on-device text generation. The mixed INT4 `.litertlm` artifact was produced with a TorchAO-based quantize-first recipe from the original Hugging Face checkpoint. This is a mixed quantization layout rather than a uniform all-INT4 model: eligible linear projection weights are stored as blockwise INT4 with group size 32 and floating-point scales, token embedding weights use weight-only INT8 quantization, and normalization/reduction paths plus KV cache tensors remain floating point. The mixed INT4 bundle also uses LiteRT-LM StableHLO composite ops for attention/cache execution, including `odml.runtime_bmm` and `odml.cache_update`.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-4B-Instruct-2507
litert-community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 ↗Qwen3-4B-Thinking-2507
litert-community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 ↗Qwen3.5-0.8B
litert-community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 ↗Qwen3.5-2B
litert-community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 ↗Qwen3.5-4B
litert-community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 ↗Qwen3.5-9B
litert-community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 ↗Qwen3-8B
litert-community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-ASR-0.6B
litert-community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 Audio→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-ASR-1.7B
litert-community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 Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-TTS-12Hz-0.6B-Base
litert-community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 Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗resnet18
litert-community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 Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗resnet50
litert-community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 Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗RF-DETR-Nano-LiteRT
litert-community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 Image→ 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.