At a glance
| Model | Input /1M | Output /1M | Cached input /1M | Max tokens |
|---|---|---|---|---|
 deepseek-v4.1-flash | $0.15 | $0.60 | $0.0025 | 1,048,576 |
 glm-5.3-flash | $0.10 | $0.35 | $0.02 | 1,048,576 |
deepseek-v4-flash-0731 | $0.16 | $0.38 | $0.006 | 1,048,576 |
 glm-5.2 | $1.10 | $3.50 | $0.26 | 262,144 |
 qwen3-30b-a3b-fp8 | $0.10 | $0.45 | $0.05 | 32,768 |
 gpt-oss-120b | $0.15 | $0.60 | $0.03 | 131,072 |
 llama-3.1-8b-instruct-fast | $0.15 | $0.28 | $0.025 | 131,072 |
llama-guard-4-12b | $0.18 | $0.18 | — | 163,840 |
Model cards
deepseek-v4.1-flash
1,048,576 context window$0.15 / 1M input$0.60 / 1M output$0.0025 / 1M cached input
DeepSeek’s DeepSeek-V4.1-Flash is an open-weight sparse Mixture-of-Experts model and the first built on DeepSeek’s Causal Encoder-Decoder (CED) architecture, activating 8B parameters on input and 16B on output. It…glm-5.3-flash
1,048,576 context window$0.10 / 1M input$0.35 / 1M output$0.02 / 1M cached input
Z.ai’s GLM-5.3-Flash is an efficient open-weight model for coding and long-horizon agent tasks, served on ZeroGPU for general text generation. Its hybrid sparse and linear attention keeps…deepseek-v4-flash-0731
1,048,576 context window$0.16 / 1M input$0.38 / 1M output$0.006 / 1M cached input
DeepSeek’s DeepSeek-V4-Flash is an open-weight Mixture-of-Experts model built for efficient reasoning, coding, and agentic workflows, with 284B total parameters activating only 13B per token. Its…glm-5.2
262,144 context window$1.10 / 1M input$3.50 / 1M output$0.26 / 1M cached input
Z.ai’s GLM-5.2 is an open-weight Mixture-of-Experts flagship built for long-horizon tasks, with 753B total parameters activating 8 of 256 experts per token. It sustains a 262,144-token (256K)…qwen3-30b-a3b-fp8
32,768 context window$0.10 / 1M input$0.45 / 1M output$0.05 / 1M cached input
Alibaba’s Qwen3-30B-A3B is an open-weight Mixture-of-Experts model with 30.5B total parameters (3.3B active per token), served on ZeroGPU as an FP8 build for efficient inference. It thinks through a problem…gpt-oss-120b
131,072 context window$0.15 / 1M input$0.60 / 1M output$0.03 / 1M cached input
OpenAI’s gpt-oss-120b is an open-weight Mixture-of-Experts model with 117B total parameters (5.1B active per token), served on ZeroGPU for general text generation. It reasons through a problem…llama-3.1-8b-instruct-fast
131,072 max tokens$0.15 / 1M input$0.28 / 1M output$0.025 / 1M cached input
Meta’s Llama 3.1 Instruct, tuned for fast, low-cost summarization at scale on the ZeroGPU edge network. Its 128K-token context window takes in entire documents, long transcripts, and full email or…llama-guard-4-12b
Text GenerationSafety classificationText ModerationBrand Safety12B params163,840 context window$0.18 / 1M input$0.18 / 1M output
Meta’s Llama Guard 4 12B is a multimodal safety classification model for moderating text, images, and mixed text-image inputs. It evaluates both incoming prompts…