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 | Task | Params | Max tokens |
|---|---|---|---|---|---|
 gpt-oss-120b | $0.03 | $0.10 | Text Generation | 117B | 131,072 |
 qwen3-30b-a3b-fp8 | $0.05 | $0.30 | Text Generation | 30.5B | 32,768 |
 llama-3.1-8b-instruct-fast | $0.02 | $0.05 | Summarization | 8B | 131,072 |
 zlm-v2-iab-classify-edge-enriched | $0.02 | $0.05 | Text Classification | 90M | 400 |
 zlm-v1-iab-classify-edge | $0.02 | $0.05 | Text Classification | 90M | 400 |
 zlm-v1-iab-domain-classifier | $0.02 | $0.05 | Text Classification | 149M | 100 |
 zlm-v1-followup-questions-edge | $0.02 | $0.05 | Text Generation | 120M | 400 |
 gliner-multi-pii-v1 | $0.02 | $0.05 | PII | 300M | 800 |
 gliner2-base-v1 | $0.02 | $0.05 | PII | 205M | 800 |
 deberta-v3-small | $0.02 | $0.05 | Text Classification | 142M | 400 |
 LFM2.5-1.2B-Thinking | $0.02 | $0.05 | Text Generation | 1.2B | 32,768 |
 LFM2.5-1.2B-Instruct | $0.02 | $0.05 | Text Generation | 1.2B | 32,768 |
Detailed model cards
gpt-oss-120b
Text GenerationReasoningFunction callingBatch tasks117B params131,072 context window$0.03 / 1M input$0.10 / 1M output
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
Text GenerationReasoningFunction callingStreamingBatch tasksMultilingual30.5B params32,768 context window$0.05 / 1M input$0.30 / 1M output
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
Summarization8B params131,072 max tokens$0.02 / 1M input$0.05 / 1M output
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…zlm-v2-iab-classify-edge-enriched
Text Classification90M params400 max tokens$0.02 / 1M input$0.05 / 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
Text Classification90M params400 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
Text Classification149M params100 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-followup-questions-edge
Text Generation120M params400 max tokens$0.02 / 1M input$0.05 / 1M output
ZeroGPU’s follow-up question generator takes any piece of content — an article, an answer, a chat turn and returns a short set of natural questions a reader would actually ask next. Where a…gliner-multi-pii-v1
PII300M params800 max tokens$0.02 / 1M input$0.05 / 1M output
GLiNER Multi PII is a multilingual PII detection and redaction model that supports on-prem deployments as well. It identifies 40+ personally identifiable entity types — identity, contact, government…gliner2-base-v1
PII205M params800 max tokens$0.02 / 1M input$0.05 / 1M output
gliner2-base-v1 is a versatile extraction-and-classification model for the structured tasks that fill most production pipelines. Point it at any text and, with a single API call, pull named entities…deberta-v3-small
Text Classification142M params400 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
Text Generation1.2B params32,768 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
Text Generation1.2B params32,768 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…Model library by task
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3 models:
zlm-v1-iab-classify-edge, zlm-v2-iab-classify-edge-enriched, zlm-v1-iab-domain-classifierText Classification
4 models:
zlm-v2-iab-classify-edge-enriched, zlm-v1-iab-classify-edge, zlm-v1-iab-domain-classifier, deberta-v3-smallText Generation
5 models:
gpt-oss-120b, qwen3-30b-a3b-fp8, zlm-v1-followup-questions-edge, LFM2.5-1.2B-Thinking, LFM2.5-1.2B-InstructPII
2 models:
gliner-multi-pii-v1, gliner2-base-v1Summarization
1 model:
llama-3.1-8b-instruct-fast