model identifier to send to POST /v1/responses.
Sections on this page: At a glance (pricing table), Detailed model cards, and Model library by task.
At a glance
| Model | Input /1M | Output /1M | Cached input /1M | Task | Max tokens |
|---|---|---|---|---|---|
 deepseek-v4.1-flash | $0.30 | $1.20 | $0.003 | Text Generation | 1,048,576 |
 glm-5.3-flash | $0.10 | $0.35 | $0.015 | Text Generation | 1,048,576 |
 gpt-5.4-nano | $0.20 | $1.25 | $0.02 | Text Generation | 400,000 |
gpt-5.6-luna | $0.20 | $1.20 | $0.20 | Text Generation | 272,000 |
gpt-6-luna | $0.10 | $0.50 | $0.01 | Text Generation | 272,000 |
gpt-4.1-mini | $0.40 | $1.60 | $0.10 | Text Generation | 1,047,576 |
gpt-oss-120b | $0.15 | $0.60 | $0.03 | Text Generation | 131,072 |
 qwen3-30b-a3b-fp8 | $0.10 | $0.45 | $0.05 | Text Generation | 32,768 |
 llama-3.1-8b-instruct-fast | $0.15 | $0.28 | $0.025 | Text Generation | 131,072 |
llama-3.2-3b-instruct-fast | $0.05 | $0.30 | $0.025 | Text Generation | 131,072 |
llama-guard-4-12b | $0.18 | $0.18 | — | Text Generation | 163,840 |
 zlm-v2-iab-classify-edge-enriched | $0.05 | $0.15 | — | Text Classification | 800 |
 zlm-v1-iab-classify-edge | $0.02 | $0.05 | — | Text Classification | 400 |
 zlm-v1-iab-domain-classifier | $0.02 | $0.05 | — | Text Classification | 100 |
zlm-v1-moderation-edge | $0.10 | $0.15 | — | Text Moderation | 800 |
 gliner2.5-base-v1 | $0.02 | $0.05 | — | Text Classification | — |
gliner2.5-multi-v1 | $0.02 | $0.05 | — | Text Classification | — |
 deberta-v3-small | $0.02 | $0.05 | — | Text Classification | 400 |
 LFM2.5-1.2B-Thinking | $0.02 | $0.05 | — | Text Generation | 32,768 |
 LFM2.5-1.2B-Instruct | $0.02 | $0.05 | — | Text Generation | 32,768 |
 qwen3.6-35b-a3b | $0.20 | $1.50 | $0.20 | Text Generation | — |
zlm-v1-signal-extract | $0.04 | $0.10 | — | Text Classification | 400 |
gliner2.5-decide-v1 | $0.004 | Not billed | — | Text Classification | — |
 all-minilm-l6-v2 | $0.004 | Not billed | — | Text Embedding | 512 |
 bge-small-en-v1.5 | $0.004 | Not billed | — | Text Embedding | 512 |
POST /v1/audio/transcriptions and POST /v1/audio/speech rather than /v1/responses, and are billed per minute of audio instead of per token, rounded up to the next whole minute with a one-minute minimum.
| Model | Price /min | Task | Limits |
|---|---|---|---|
whisper-tiny | $0.02 | Speech-to-Text | 25 MB, 10 minutes of audio |
 chatterbox-nano | $0.01 | Text-to-Speech | 2,000 characters |
Detailed model cards
deepseek-v4.1-flash
1,048,576 context window$0.30 / 1M input$1.20 / 1M output$0.003 / 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$0.015 / 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…gpt-5.4-nano
400,000 context window$0.20 / 1M input$1.25 / 1M output$0.02 / 1M cached input
OpenAI’s GPT-5.4 nano is the most cost-efficient model in the GPT-5.4 family, served on ZeroGPU for high-volume and latency-sensitive workloads such as classification, extraction, routing, and…gpt-5.6-luna
272,000 context window$0.20 / 1M input$1.20 / 1M output$0.20 / 1M cached input
OpenAI’s GPT-5.6 Luna is the cost-optimized model of the GPT-5.6 family, served on ZeroGPU for cost-sensitive, high-volume workloads. It supports adjustable reasoning effort, function calling, and…gpt-6-luna
272,000 context window$0.10 / 1M input$0.50 / 1M output$0.01 / 1M cached input
OpenAI’s GPT-6 Luna is the most efficient model of the GPT-6 family, served on ZeroGPU for high-volume, cost-sensitive workloads. It reads both text and images, and supports adjustable reasoning…gpt-4.1-mini
1,047,576 context window$0.40 / 1M input$1.60 / 1M output$0.10 / 1M cached input
OpenAI’s GPT-4.1 mini is the fast, cost-efficient model of the GPT-4.1 family, served on ZeroGPU for general text generation. It excels at instruction following and tool calling, supports function…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…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…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-3.2-3b-instruct-fast
131,072 max tokens$0.05 / 1M input$0.30 / 1M output$0.025 / 1M cached input
Meta’s Llama 3.2 3B Instruct, packaged by ZeroGPU for fast, low-cost summarization on edge GPUs. It shares the 131,072-token context window and the summarization pipeline of llama-3.1-8b-instruct-fast, and…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…zlm-v2-iab-classify-edge-enriched
800 max tokens$0.05 / 1M input$0.15 / 1M output
The enriched variant of ZeroGPU’s IAB classifier turns a single inference call into a full content-intelligence profile not just a label, but everything a contextual pipeline needs to act on. Each…zlm-v1-iab-classify-edge
400 max tokens$0.02 / 1M input$0.05 / 1M output
ZeroGPU’s IAB classifier maps any text to the industry-standard IAB Content Taxonomy in a single, fast inference call. Each call returns categories across both the 1.0 and 2.2 taxonomies plus matched…zlm-v1-iab-domain-classifier
100 max tokens$0.02 / 1M input$0.05 / 1M output
ZeroGPU’s Domain IAB Classifier maps a raw domain name straight to the IAB Content Taxonomy, returning content categories, topics, keywords, and user-intent signals. It needs only the domain as input, cutting payload size by up to 10x versus page-level…zlm-v1-moderation-edge
800 max tokens$0.10 / 1M input$0.15 / 1M output
ZeroGPU’s moderation model returns the complete OpenAI 13-category taxonomy — a flagged verdict, category booleans, and calibrated scores — as a drop-in for omni-moderation-latest. In head-to-head benchmarks it wins the binary safe/unsafe decision (0.899 vs 0.853 F1) and 9 of 13 harm categories, and returns verdicts 1.2–1.8× faster on production-range…gliner2.5-base-v1
194M params$0.02 / 1M input$0.05 / 1M output
Fastino’s GLiNER2.5 Base is an English schema-based information extraction model built on DeBERTa-v3-base. A single model covers entity extraction, text classification, structured record extraction…gliner2.5-multi-v1
287M params$0.02 / 1M input$0.05 / 1M output
Fastino’s GLiNER2.5 Multi is a multilingual information extraction model built on mDeBERTa-v3-base. It supports zero-shot named entity recognition, text classification, and structured JSON extraction…deberta-v3-small
400 max tokens$0.02 / 1M input$0.05 / 1M output
Microsoft’s DeBERTa-v3-small is a fast, lightweight zero-shot text classifier for high-volume routing, filtering, and tagging. Hand it any text alongside your own candidate labels and it returns a…LFM2.5-1.2B-Thinking
32,768 max tokens$0.02 / 1M input$0.05 / 1M output
Liquid AI’s LFM2.5-1.2B-Thinking is a compact reasoning model that works through a problem step by step before it answers. Built by Liquid AI, it generates an explicit chain-of-thought trace so for…LFM2.5-1.2B-Instruct
32,768 max tokens$0.02 / 1M input$0.05 / 1M output
Liquid AI’s LFM2.5-1.2B-Instruct is a hybrid architecture model purpose-built for on-device deployment, trained on 28 trillion tokens with multi-stage reinforcement learning. It delivers…qwen3.6-35b-a3b
35B params$0.20 / 1M input$1.50 / 1M output$0.20 / 1M cached input
Alibaba’s Qwen3.6-35B-A3B is a fast, open-weight multimodal model for applications that need to understand text, images, and video, served on ZeroGPU as an FP8 build. It suits visual question answering, document and screenshot analysis…zlm-v1-signal-extract
Text ClassificationSignal extractionAd TechChat80M params400 context window$0.04 / 1M input$0.10 / 1M output
ZeroGPU’s signal extractor turns unstructured text into structured signals that downstream systems can act on. One inference call returns topics, keywords, intent…gliner2.5-decide-v1
486M params$0.004 / 1M input
Fastino’s GLiNER2.5 Decide is a 486M-parameter zero-shot decision and classification model built for operational AI workflows. You supply the labels or decision options at request time to classify intent, route agents and tools, triage support requests, make moderation decisions…all-minilm-l6-v2
512 max tokens$0.004 / 1M input
Sentence-Transformers’ all-MiniLM-L6-v2 is the default workhorse of semantic search. It maps a sentence or short paragraph to a 384-dimensional vector, trained with contrastive learning on more than a billion sentence pairs…bge-small-en-v1.5
512 max tokens$0.004 / 1M input
BAAI’s BGE-small-en-v1.5 is a retrieval-first English embedding model and one of the strongest performers on the MTEB benchmark for its size. It produces the same 384-dimensional vectors, with a 512-token window…whisper-tiny
Speech-to-Text39M params$0.02 / minute
OpenAI’s Whisper Tiny is the smallest model in the Whisper family of speech recognition models, at 39M parameters. It is the multilingual checkpoint: it transcribes speech in the language it was spoken…chatterbox-nano
Text-to-SpeechVoice cloning110M params$0.01 / minute
Resemble AI’s Chatterbox Nano is a 110M-parameter English text-to-speech model built for fast, low-cost speech generation. It speaks your text in a built-in voice, or clones a speaker…Model library by task
Open Weight
8 models:
deepseek-v4.1-flash, glm-5.3-flash, qwen3-30b-a3b-fp8, qwen3.6-35b-a3b, gpt-oss-120b, llama-3.1-8b-instruct-fast, llama-3.2-3b-instruct-fast, llama-guard-4-12bText Generation
14 models:
deepseek-v4.1-flash, glm-5.3-flash, gpt-5.4-nano, gpt-5.6-luna, gpt-6-luna, gpt-4.1-mini, gpt-oss-120b, qwen3-30b-a3b-fp8, qwen3.6-35b-a3b, llama-3.1-8b-instruct-fast, llama-3.2-3b-instruct-fast, llama-guard-4-12b, LFM2.5-1.2B-Thinking, LFM2.5-1.2B-InstructAudio Models
2 models:
whisper-tiny, chatterbox-nanoText Classification
8 models:
zlm-v2-iab-classify-edge-enriched, zlm-v1-iab-classify-edge, zlm-v1-iab-domain-classifier, deberta-v3-small, zlm-v1-signal-extract, gliner2.5-base-v1, gliner2.5-multi-v1, gliner2.5-decide-v1Moderation
1 model:
zlm-v1-moderation-edgeText Embedding
2 models:
all-minilm-l6-v2, bge-small-en-v1.5Ad Tech
4 models:
zlm-v1-iab-classify-edge, zlm-v2-iab-classify-edge-enriched, zlm-v1-iab-domain-classifier, zlm-v1-signal-extract