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Category/text-generation

Text generation AI models can generate coherent and natural-sounding human language text, making them useful for a variety of applications from language translation to content creation.

There are several types of text generation AI models, including rule-based, statistical, and neural models. Neural models, and in particular transformer-based models like GPT, have achieved state-of-the-art results in text generation tasks. These models use artificial neural networks to analyze large text corpora and learn the patterns and structures of language.

While text generation AI models offer many exciting possibilities, they also present some challenges. For example, it's essential to ensure that the generated text is ethical, unbiased, and accurate, to avoid potential harm or negative consequences.

deepseek-ai/DeepSeek-Prover-V2-671B cover image
featured
fp8
160k
$0.70/$2.18 in/out Mtoken
  • text-generation

DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by DeepSeek-V3. The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals. The proofs of resolved subgoals are synthesized into a chain-of-thought process, combined with DeepSeek-V3's step-by-step reasoning, to create an initial cold start for reinforcement learning.

Qwen/Qwen3-235B-A22B cover image
featured
fp8
40k
$0.10 / Mtoken
  • text-generation

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support

Qwen/Qwen3-30B-A3B cover image
featured
fp8
40k
$0.10/$0.30 in/out Mtoken
  • text-generation

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support

Qwen/Qwen3-32B cover image
featured
fp8
40k
$0.10/$0.30 in/out Mtoken
  • text-generation

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support

Qwen/Qwen3-14B cover image
featured
fp8
40k
$0.07/$0.24 in/out Mtoken
  • text-generation

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support.

meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 cover image
featured
fp8
1024k
$0.17/$0.60 in/out Mtoken
  • text-generation

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Maverick, a 17 billion parameter model with 128 experts

meta-llama/Llama-4-Scout-17B-16E-Instruct cover image
featured
bfloat16
320k
$0.08/$0.30 in/out Mtoken
  • text-generation

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Scout, a 17 billion parameter model with 16 experts

deepseek-ai/DeepSeek-R1-Turbo cover image
featured
fp4
32k
$1.00/$3.00 in/out Mtoken
  • text-generation

We introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.

deepseek-ai/DeepSeek-R1 cover image
featured
fp8
160k
$0.50/$2.18 in/out Mtoken
  • text-generation

We introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.

microsoft/phi-4-reasoning-plus cover image
featured
bfloat16
32k
$0.07/$0.35 in/out Mtoken
  • text-generation

Phi-4-reasoning-plus is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. The supervised fine-tuning dataset includes a blend of synthetic prompts and high-quality filtered data from public domain websites, focused on math, science, and coding skills as well as alignment data for safety and Responsible AI. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning. Phi-4-reasoning-plus has been trained additionally with Reinforcement Learning, hence, it has higher accuracy but generates on average 50% more tokens, thus having higher latency.

meta-llama/Llama-Guard-4-12B cover image
featured
bfloat16
160k
$0.05 / Mtoken
  • text-generation

Llama Guard 4 is a natively multimodal safety classifier with 12 billion parameters trained jointly on text and multiple images. Llama Guard 4 is a dense architecture pruned from the Llama 4 Scout pre-trained model and fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It itself acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Qwen/QwQ-32B cover image
featured
bfloat16
128k
$0.15/$0.20 in/out Mtoken
  • text-generation

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.

deepseek-ai/DeepSeek-V3-0324 cover image
featured
fp8
160k
$0.30/$0.88 in/out Mtoken
  • text-generation

DeepSeek-V3-0324, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token, an improved iteration over DeepSeek-V3.

google/gemma-3-27b-it cover image
featured
bfloat16
128k
$0.10/$0.20 in/out Mtoken
  • text-generation

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 27B is Google's latest open source model, successor to Gemma 2

google/gemma-3-12b-it cover image
featured
bfloat16
128k
$0.05/$0.10 in/out Mtoken
  • text-generation

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3-12B is Google's latest open source model, successor to Gemma 2

google/gemma-3-4b-it cover image
featured
bfloat16
128k
$0.02/$0.04 in/out Mtoken
  • text-generation

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3-12B is Google's latest open source model, successor to Gemma 2

microsoft/Phi-4-multimodal-instruct cover image
featured
bfloat16
128k
$0.05/$0.10 in/out Mtoken
  • text-generation

Phi-4-multimodal-instruct is a lightweight open multimodal foundation model that leverages the language, vision, and speech research and datasets used for Phi-3.5 and 4.0 models. The model processes text, image, and audio inputs, generating text outputs, and comes with 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning, direct preference optimization and RLHF (Reinforcement Learning from Human Feedback) to support precise instruction adherence and safety measures. The languages that each modal supports are the following: - Text: Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian - Vision: English - Audio: English, Chinese, German, French, Italian, Japanese, Spanish, Portuguese

deepseek-ai/DeepSeek-R1-Distill-Llama-70B cover image
featured
fp8
128k
$0.10/$0.40 in/out Mtoken
  • text-generation

DeepSeek-R1-Distill-Llama-70B is a highly efficient language model that leverages knowledge distillation to achieve state-of-the-art performance. This model distills the reasoning patterns of larger models into a smaller, more agile architecture, resulting in exceptional results on benchmarks like AIME 2024, MATH-500, and LiveCodeBench. With 70 billion parameters, DeepSeek-R1-Distill-Llama-70B offers a unique balance of accuracy and efficiency, making it an ideal choice for a wide range of natural language processing tasks.