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Models

Models

CompareDiscover Models
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CompareDiscover Models
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  • Favicon for qwen
    Qwen: Qwen3.8 27BQwen3.8 27B
    31.5B tokens

    Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled.

    by qwenAug 14, 2026262K context$0.40/M input tokens$3/M output tokens
  • Favicon for dots-studio
    Dots Studio: Dots3-Note Preview (free)Dots3-Note Preview (free)Free variant
    39.7B tokens

    Dots3-Note Preview is an open-weight mixture-of-experts model from Dots Studio, with 16B active parameters out of 280B total. It is the lightest model in the Dots 3 family and is suited for reasoning, coding, multimodal understanding, long-context processing, and multi-step agent workflows.

    by dots-studioAug 14, 2026512K context$0/M input tokens$0/M output tokens
  • Favicon for nvidia
    NVIDIA: Nemotron 3.5 ASR Streaming Multilingual 0.6BNemotron 3.5 ASR Streaming Multilingual 0.6B
    5.76M characters

    Nemotron 3.5 ASR Streaming Multilingual 0.6B is a speech recognition model from NVIDIA. Its prompt-conditioned, cache-aware FastConformer-RNNT design targets low-latency transcription across more than 40 languages for real-time captioning, voice agents, and multilingual transcription pipelines.

    by nvidiaAug 13, 2026$0.000003/second
  • Favicon for mistralai
    Mistral: Voxtral Small 24B 2507 STTVoxtral Small 24B 2507 STT
    2.91M characters

    Voxtral Small 24B 2507 STT is a speech transcription model from Mistral AI. It is suited for transcription, translation, and audio understanding workloads that benefit from its larger model capacity.

    by mistralaiAug 13, 2026$0.00005/second
  • Favicon for mistralai
    Mistral: Voxtral Mini 3B 2507Voxtral Mini 3B 2507
    2.58M characters

    Voxtral Mini 3B 2507 is a speech and audio understanding model from Mistral AI. It is suited for transcription, translation, and compact audio processing workloads.

    by mistralaiAug 13, 2026$0.000017/second
  • Favicon for bytedance-seed
    ByteDance Seed: Seedream 5.0 LiteSeedream 5.0 Lite
    160M tokens

    Seedream 5.0 Lite is an image generation model from ByteDance Seed. It is suited for professional visual creation that benefits from web-connected retrieval, complex-prompt comprehension, visual references, and broad knowledge coverage.

    by bytedance-seedAug 13, 2026$0.035/image
  • Favicon for google
    Google: Gemini 3.7 Flash (batch)Gemini 3.7 Flash (batch)
    50% off
    Batch variant
    2.6B tokens

    Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.

    by googleAug 13, 20261.05M context$0.1875/M input tokens$0.9375/M output tokens
  • Favicon for google
    Google: Gemini 3.7 FlashGemini 3.7 Flash
    50% off
    670B tokens

    Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.

    by googleAug 13, 20261.05M context$0.375/M input tokens$1.875/M output tokens
  • Favicon for voyageai
    VoyageAI by MongoDB: voyage-code-4voyage-code-4
    69.8M tokens

    voyage-code-4 is a code embedding model from Voyage AI, a MongoDB company. It is designed for coding agents and code retrieval, with Matryoshka embeddings at 2048, 1024, 512, and 256 dimensions and multiple quantization options. Learn more about voyage-code-4 here: blog.voyageai.com/2026/08/13/voyage-code-4

    by voyageaiAug 13, 202632K context$0.12/M tokens
  • Favicon for qwen
    Qwen3 Reranker 8BQwen3 Reranker 8B
    579M tokens

    Qwen3 Reranker 8B is a text reranking model from Alibaba Cloud built on the Qwen3 architecture. It evaluates query-document pairs to produce relevance scores for use in retrieval and RAG pipelines. Supports 100+ languages and programming languages, with instruction-aware reranking that allows customizing scoring criteria per task. Offers strong performance on multilingual benchmarks including MTEB, CMTEB, and MMTEB.

    by qwenAug 13, 202641K context$0.20/M tokens
  • Favicon for qwen
    Qwen: Qwen3 ASR 1.7BQwen3 ASR 1.7B
    26.1M characters

    Qwen3 ASR 1.7B is an automatic speech recognition model from Qwen. It supports multilingual language identification and transcription across 30 languages and 22 Chinese dialects, with streaming and offline inference plus segment-level and word-level timestamps.

    by qwenAug 13, 2026$0.000008/second
  • Favicon for qwen
    Qwen: Qwen3 ASR 0.6BQwen3 ASR 0.6B
    3M characters

    Qwen3 ASR 0.6B is a compact automatic speech recognition model from Qwen. It supports multilingual language identification and transcription across 30 languages and 22 Chinese dialects, with streaming and offline inference plus segment-level and word-level timestamps.

    by qwenAug 13, 2026$0.000003/second
  • Favicon for bytedance-seed
    ByteDance Seed: Seedream 5.0 ProSeedream 5.0 Pro
    206M tokens

    Seedream 5.0 Pro is an image generation and editing model from ByteDance Seed. It is suited for commercial visual-production workflows that require precise editing control, lifelike scenes, and natural rendering.

    by bytedance-seedAug 12, 2026from $0.045/image
  • Favicon for deepgram
    Deepgram: Flux TTS (free)Flux TTS (free)Free variant
    1.64M tokens

    Flux TTS is a text-to-speech model from Deepgram. It is suited for natural, expressive English speech synthesis across Deepgram's Flux voice catalog.

    by deepgramAug 12, 2026$0/M input tokens$0/M output tokens
  • Favicon for bytedance
    ByteDance: Seedance 2.0 MiniSeedance 2.0 Mini
    60% off
    26 hours

    Seedance 2.0 Mini is a video generation model from ByteDance. It supports text-to-video, image-to-video with first and last frame control, and multimodal reference-to-video with image, video, and audio inputs. It supports 480p and 720p output for 4-15 second videos. The number of tokens is given by (height of output video * width of output video * duration * 24) / 1024

    by bytedanceAug 12, 2026from $0.01345/second
  • Favicon for bytedance-seed
    ByteDance Seed: Seed 2.1 TurboSeed 2.1 Turbo
    1B tokens

    Seed 2.1 Turbo is a multimodal model from ByteDance Seed for coding and long-horizon agent workflows. It is suited for end-to-end software delivery, multi-step task execution, and understanding visual and video content, with capabilities for planning, debugging, and self-correction.

    by bytedance-seedAug 12, 2026262K context$0.50/M input tokens$2.50/M output tokens
  • Favicon for qwen
    Qwen: Qwen3.8 2.4T A95BQwen3.8 2.4T A95B
    27.2B tokens

    Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of Qwen3.8 Max, with 95 billion active parameters out of 2.4 trillion total. It is suited for coding, research, complex reasoning, and agentic workflows.

    by qwenAug 12, 20261.05M context$2/M input tokens$6/M output tokens
  • Favicon for bytedance-seed
    ByteDance Seed: Seed-2.0-CodeSeed-2.0-Code
    1.2B tokens

    Seed 2.0 Code is a model from ByteDance Seed optimized for agentic coding. It is suited for frontend development, multilingual programming tasks, and coding-agent workflows in tools such as Claude Code, Kilo, and OpenCode.

    by bytedance-seedAug 12, 2026262K context$0.50/M input tokens$3/M output tokens
  • Favicon for deepseek
    DeepSeek: DeepSeek V4 Pro 0813DeepSeek V4 Pro 0813
    30% off
    853B tokens

    DeepSeek V4 Pro 0813 is a large-scale mixture-of-experts model from DeepSeek. This is the GA release of DeepSeek V4 Pro.

    by deepseekAug 12, 20261.05M context$1.218/M input tokens$2.436/M output tokens
  • Favicon for x-ai
    SpaceXAI: Grok 4.6Grok 4.6
    385B tokens

    Grok 4.6 is SpaceXAI's smartest model with frontier performance on coding, knowledge work, and STEM.

    by x-aiAug 12, 2026500K context$2/M input tokens$6/M output tokens