Fine-tuning

Cosmos 3 Nano Policy DROID is a 16B-parameter robotics policy model fine-tuned from Cosmos 3 Nano on the DROID dataset. Given natural language instructions and visual observations from a robot camera (image or video), it generates robot action trajectories (JSON 1D list) for manipulation and control tasks. Compatible with multiple robot embodiments including Franka Panda (single/dual), UR, Google robot, WidowX 250, UMI, and Agibot. Supports 16-400 frame action sequences in various DoF configurations (9D-57D). Intended as a reference implementation for post-training Cosmos 3 Nano on specific robot platforms. The action output modality is represented in prose because the current model schema only has text, vision, video, audio, and related capability flags.

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Cosmos 3 Super Image2Video is a 64B-parameter fine-tuned variant of Cosmos 3 Super specialized for temporally coherent image-to-video generation. Takes a single image (jpg/png/webp at 256p-720p) plus an optional text prompt (up to 4096 tokens) and outputs MP4 video with 5-400 frames (default 189) at up to 720p, with optional muxed AAC stereo audio at 48kHz. Ranked #1 on Artificial Analysis image-to-video leaderboard (open models). Available via Hugging Face Diffusers and vLLM-Omni.

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Cosmos 3 Super Text2Image is a 64B-parameter fine-tuned variant of Cosmos 3 Super specialized for high-fidelity text-to-image generation. Takes text prompts up to 4096 tokens and outputs JPEG images at 256p, 480p, or 720p in aspect ratios 16:9, 4:3, 1:1, 3:4, or 9:16. Ranked #1 on Artificial Analysis text-to-image leaderboard (open models). Available via Hugging Face Diffusers (DiffusionPipeline) and vLLM-Omni.

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OpenAI GPT-4o: Flagship multimodal model with vision, function calling, and broad capability. $2.50/M input, $10/M output.

$2.50 in / $10.00 out / 1M tokens

5 routes · 1 batch · 2 cache

OpenAI: GPT-4o-mini available via OpenRouter. Pricing: $0.15/1M input, $0.6/1M output.

$0.150 in / $0.600 out / 1M tokens

4 routes · 2 cache

Tencent Hunyuan Hy4 preview is a Mixture-of-Experts flagship language model released and open-sourced August 28, 2026. First-party Tencent release: 770B total parameters with 49B activated per token, context window exceeding 1M tokens, aimed at coding, office productivity, game development, and scientific research. Hugging Face model card (tencent/Hy4-preview) documents a 78-layer MoE backbone (256 routed experts, top-8 plus 1 shared expert), Gated DeepSeek Sparse Attention with IndexCache, iHC residual streams, native MTP speculative decoding, Apache License 2.0 weights (also Hy4-preview-FP8), and OpenAI-compatible serving via vLLM/SGLang with tool-call and reasoning parsers. Public access: Hugging Face/ModelScope/GitCode/CNB weights; API via Tencent Cloud TokenHub and OpenRouter (model id tencent/hy4-preview); also WorkBuddy, CodeBuddy, Yuanbao, and ima. Official API list pricing USD 0.834 / 2.501 per 1M input/output tokens and USD 0.042 per 1M cache hits. Sibling of seeded hy3-preview under family hunyuan. Compare it for Agents and Coding.

$0.834 in / $2.50 out / 1M tokens

2 routes · 2 cache

Inkling is Thinking Machines Lab's Apache-2.0 open-weight general-purpose multimodal model. It accepts text, image, and audio inputs and generates text with a 1M-token context window. Compare it for Coding, RAG, Agents, Long context, Vision, and JSON / Tool use.

$1.00 in / $4.05 out / 1M tokens

2 routes · 2 cache

Spark-X2.5-1.7B is the smaller SparkLLM / XHToken (iFlytek) post-trained on-device general language model in the Spark-X2.5 series. Hugging Face card XHToken/Spark-X2.5-1.7B (Apache License 2.0 LICENSE blob, Copyright 2026 XHToken) documents Spark2_5ForCausalLM with the same hybrid 3:1 sliding-window / full-attention pattern, native 1,048,576-token context, safetensors 1,707,657,216 parameters (card title 1.7B; HF size widget rounds to 2B), thinking mode, tool-call parser spark25, and Llama-Factory fine-tuning. Same first-party GitHub README as the 4B: ~20T pretrain, SFT plus RL / MOPD from base XHToken/Spark-X2.5-1.7B-Base; 200+ languages; agent/coding/reasoning on-device. Serving: SGLang, vLLM, MLX, llama.cpp, Ollama, LM Studio. Xingchen MaaS listed on SparkLLM DEV as limited-time free; first-party Token Plan docs on 2026-09-01 list Spark-X2 / X2-Agent / X2-Flash only, not X2.5 IDs or USD rates. Sibling Spark-X2.5-4B. Do not packet unreleased Spark X2.5 293B. Compare it for Agents, Coding, and Long context.

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Spark-X2.5-4B is SparkLLM / XHToken (iFlytek) post-trained on-device general language model in the Spark-X2.5 series. Hugging Face card XHToken/Spark-X2.5-4B (Apache License 2.0 LICENSE blob, Copyright 2026 XHToken) documents a compact dense decoder (Spark2_5ForCausalLM) with hybrid attention: repeating 3 sliding-window layers plus 1 full-attention layer, native context up to 1,048,576 tokens (config max_position_embeddings), safetensors 4,112,079,360 parameters, thinking-mode chat template, tool-call parser spark25, and Llama-Factory fine-tuning. First-party GitHub README and HF card: pretrained ~20T tokens then SFT plus large-scale RL / MOPD from base XHToken/Spark-X2.5-4B-Base; 200+ languages; conversation, writing, translation, reasoning, coding, tool use, and agent harnesses (Codex, Claude Code, OpenClaw, Hermes). Serving recipes for SGLang, vLLM, MLX, llama.cpp, Ollama, LM Studio. Weights also listed on ModelScope, Modelers, SCNet. Xingchen MaaS API mentioned on SparkLLM DEV as limited-time free without published USD list prices or X2.5 providerModelId on first-party Token Plan as of 2026-09-01. Sibling Spark-X2.5-1.7B. Do not packet unreleased Spark X2.5 293B. Compare it for Agents, Coding, and Long context.

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Cohere Transcribe Arabic is Cohere's named 2B Arabic/English automatic speech-recognition model: an audio-in, text-out fine-tune of Cohere Transcribe for Arabic dialects and Arabic-English code-switching. It is not a general text-generation model.

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NVIDIA Nemotron-Labs-Diffusion 14B is the largest text model in NVIDIA Research's diffusion language model family, released May 23, 2026. Uses diffusion-based parallel decoding enabling up to 6× higher throughput versus autoregressive baselines, with three decoding modes: autoregressive, diffusion, and self-speculation. Training code released through NVIDIA Megatron Bridge framework alongside the weights, enabling fine-tuning. Released under NVIDIA Nemotron Open Model License (commercially usable open weights). Available on Hugging Face at nvidia/Nemotron-Labs-Diffusion-14B.

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