Native multimodal GLM model for efficient coding and long-horizon agent tasks
Models
Every model in the catalog with source-linked pricing, context limits, provider availability, and published benchmark results.
Dense 27B vision-language model for coding, agent tasks, and image and video understanding
Kimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is particularly strong at...
MiniMax multimodal model for long-context coding, perception, and agent planning
Step 3.7 Flash is StepFun's latest high-efficiency multimodal Mixture-of-Experts model. It pairs a 196B-parameter language backbone with a vision encoder for native image and video understanding, activating roughly 11B parameters...
MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding...
Qwen vision-language model for visual reasoning, documents, and agent tasks
Open multimodal Qwen MoE for local agents that need vision, audio, and code
Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...
Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...
Qwen vision-language model for visual reasoning, documents, and agent tasks
Qwen vision-language model for visual reasoning, documents, and agent tasks
Qwen vision-language model for visual reasoning, documents, and agent tasks
Qwen instruction model for multilingual chat, reasoning, and tool use
Large open Qwen multimodal MoE for visual agents and long technical tasks
GLM vision model for visual reasoning, documents, and multimodal agents
GLM vision model for visual reasoning, documents, and multimodal agents
| Model | Creator | Inputs | Context | Input | Output | Released | Compare |
|---|---|---|---|---|---|---|---|
| GLM-5.3-Flashzhipuai/glm-5.3-flash | 1M | $0.075 | $0.25 | 2026-08-26 | |||
| Qwen3.8 27Balibaba/qwen3.8-27b | 262.144K | $0.1 | $0.4 | 2026-08-14 | |||
| MoonshotAI: Kimi K3moonshotai/kimi-k3 | 1.04858M | $2.34 | $11.7 | 2026-07-16 | |||
| MiniMax-M3minimax/MiniMax-M3 | 1.04858M | $0.3 | $1.2 | 2026-06-01 | |||
| StepFun: Step 3.7 Flashstepfun/step-3.7-flash | 256K | $0.2 | $1.15 | 2026-05-29 | |||
| Xiaomi: MiMo-V2.5xiaomi/mimo-v2.5 | 1.04858M | $0.14 | $0.28 | 2026-04-22 | |||
| Qwen3.6 27Balibaba/qwen3.6-27b | 262.144K | $0.6 | $3.6 | 2026-04-22 | |||
| Qwen3.6 35B-A3Balibaba/qwen3.6-35b-a3b | 262.144K | $0.248 | $1.485 | 2026-04-17 | |||
| Google: Gemma 4 26B A4B google/gemma-4-26b-a4b-it | 131.072K | $0.042 | $0.22 | 2026-04-02 | |||
| Google: Gemma 4 31Bgoogle/gemma-4-31b-it | 262.144K | $0.09 | $0.34 | 2026-04-02 | |||
| Qwen3.5 122B-A10Balibaba/qwen3.5-122b-a10b | 262.144K | $0.4 | $3.2 | 2026-02-23 | |||
| Qwen3.5 27Balibaba/qwen3.5-27b | 262.144K | $0.3 | $2.4 | 2026-02-23 | |||
| Qwen3.5 35B-A3Balibaba/qwen3.5-35b-a3b | 262.144K | $0.25 | $2 | 2026-02-23 | |||
| Qwen3.5 9Balibaba/qwen3.5-9b | 262.144K | $0.04 | $0.15 | 2026-02-23 | |||
| Qwen3.5 397B-A17Balibaba/qwen3.5-397b-a17b | 262.144K | $0.6 | $3.6 | 2026-02-15 | |||
| GLM-4.6Vzhipuai/glm-4.6v | 128K | $0.3 | $0.9 | 2025-12-08 | |||
| GLM-4.5Vzhipuai/glm-4.5v | 64K | $0.6 | $1.8 | 2025-08-11 |