T-lite-it-1.0
t-tech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 ↗DISCOVER & COMPARE
Explore AI models 13
T-lite-it-2.1
t-tech**🚨 Users are advised to exercise caution and are responsible for any additional training and oversight required to ensure the model's responses meet acceptable ethical and safety standards. The responsibility for incorporating this model into industrial or commercial solutions lies entirely with those who choose to deploy it.** T-lite-it-2.1 is an efficient Russian model built upon the Qwen 3 architecture, featuring significant improvements in instruction following and **adds support for tool-calling capabilities** — a key advancement over T-lite-it-1.0, which lacks tool-use support. Outperforms Qwen3-8B in tool calling scenarios, which is essential for agentic applications. Built for both general tasks and complex workflows, with higher Russian text generation throughput enabled by optimized tokenizer. **NOTE: This model supports only non-thinking mode and does not generate ` ` in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗T-lite-it-2.1-FP8
t-tech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 ↗T-lite-it-2.1-GGUF
t-tech**🚨 Users are advised to exercise caution and are responsible for any additional training and oversight required to ensure the model's responses meet acceptable ethical and safety standards. The responsibility for incorporating this model into industrial or commercial solutions lies entirely with those who choose to deploy it.** This repository contains **T-lite-it-2.1** converted to the **GGUF** format with llama.cpp. See the original BF16 model here: t-tech/T-lite-it-2.1. T-lite-it-2.1 is an efficient Russian model built upon the Qwen 3 architecture, featuring significant improvements in instruction following and **adds support for tool-calling capabilities** — a key advancement over T-lite-it-1.0, which lacks tool-use support. Outperforms Qwen3-8B in tool calling scenarios, which is essential for agentic applications. Built for both general tasks and complex workflows, with higher Russian text generation throughput enabled by optimized tokenizer.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗T-one
t-tech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 ↗T-pro-it-2.0
t-tech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 ↗T-pro-it-2.0-AWQ
t-tech> **Main BF16 model:** t-tech/T-pro-it-2.0 **🚨 Users are advised to exercise caution and are responsible for any additional training and oversight required to ensure the model's responses meet acceptable ethical and safety standards. The responsibility for incorporating this model into industrial or commercial solutions lies entirely with those who choose to deploy it.** T‑pro‑it‑2.0‑AWQ is a fine‑grained AWQ‑quantised version of **T‑pro‑it‑2.0** (built on the Qwen‑3 family). AWQ 4-bit (W4A16_ASYM) offers comparable performance with approximately one-quarter the memory footprint and significantly faster inference.
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗T-pro-it-2.0-GGUF
t-tech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 ↗T-pro-it-2.1
t-tech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 ↗T-pro-it-2.1-FP8
t-tech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 ↗T-pro-it-2.1-GGUF
t-tech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 ↗T-Search
t-tech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 ↗T-Search-GGUF
t-tech**🚨 T-Search-GGUF is designed for use with the official T-Search harness. The source T-Search checkpoint did not show noticeable differences relative to the Qwen3.6-35B-A3B base model on general-purpose benchmarks.** **🚨 Users are advised to exercise caution and are responsible for any additional training and oversight required to ensure the model's responses meet acceptable ethical and safety standards. The responsibility for incorporating this model into industrial or commercial solutions lies entirely with those who choose to deploy it.** We introduce **T-Search-GGUF**, a set of GGUF quantizations of the T-Search agentic retriever for difficult multi-step search in English and Russian. T-Search plans and executes searches across multiple rounds with a configurable search budget.
Input Text→ 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.