Text Generation

Text generation models produce new text from an input prompt. ZeroGPU offers OpenAI’s open-weight gpt-oss-120b and Alibaba’s qwen3-30b-a3b-fp8 for heavyweight reasoning and tool use, conversational and reasoning models from Liquid AI, plus an edge model that generates contextual follow-up questions. Each request is shown for both the Responses API and the OpenAI-compatible Chat Completions API — except qwen3-30b-a3b-fp8, which is Chat Completions only.

Model	Input /1M	Output /1M	Params	Max tokens
`gpt-oss-120b`	$0.03	$0.10	117B	131,072
`qwen3-30b-a3b-fp8`	$0.05	$0.30	30.5B	32,768
`LFM2.5-1.2B-Instruct`	$0.02	$0.05	1.2B	32,768
`LFM2.5-1.2B-Thinking`	$0.02	$0.05	1.2B	32,768
`zlm-v1-followup-questions-edge`	$0.02	$0.05	120M	400

gpt-oss-120b

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 before answering and returns that reasoning trace alongside the final answer, and it supports function calling, batch tasks, and a 131,072-token context window. When a task needs frontier-level reasoning or tool use beyond what the smaller edge models cover, this is the model.

References: Model docs • Terms • Privacy

curl https://api.zerogpu.ai/v1/responses \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "input": "Name my WiFi network something that will make my neighbors laugh. Give me your top 3 with a one-line reason each.",
  "model": "gpt-oss-120b",
  "max_tokens": 800,
  "instructions": "You are a witty naming consultant. Be brief."
}'

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "gpt-oss-120b",
  "messages": [
    {
      "role": "system",
      "content": "You are a witty naming consultant. Be brief."
    },
    {
      "role": "user",
      "content": "Name my WiFi network something that will make my neighbors laugh. Give me your top 3 with a one-line reason each."
    }
  ],
  "max_tokens": 800
}'

The model reasons before it answers and returns the trace alongside the final text — a reasoning output item in Responses, and a reasoning field on the message in Chat Completions.

{
  "id": "id-1784318124857",
  "object": "response",
  "created_at": 1784318124,
  "model": "gpt-oss-120b",
  "status": "completed",
  "instructions": "You are a witty naming consultant. Be brief.",
  "output": [
    {
      "id": "rs_8d4dc6f3f33d0d28",
      "type": "reasoning",
      "content": [
        {
          "type": "reasoning_text",
          "text": "We need to respond with a top 3 WiFi names that will make neighbors laugh, each with a one-line reason. Be witty. Keep brief."
        }
      ]
    },
    {
      "id": "msg_9c3dfbf63445d80a",
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "**1. “FBI Surveillance Van #42”** – Neighbors will double-check the street before they even think about streaming.\n\n**2. “Drop It Like It’s Hotspot”** – A pun that’s so cheesy it’ll have the whole block groaning and giggling.\n\n**3. “Tell My Wi-Fi I Love You”** – Because nothing says “I’m caring” like a reminder that even your router needs affection."
        }
      ]
    }
  ],
  "usage": {
    "input_tokens": 0,
    "output_tokens": 0,
    "total_tokens": 0
  }
}

{
  "id": "id-1784318142640",
  "object": "chat.completion",
  "created": 1784318142,
  "model": "gpt-oss-120b",
  "choices": [
    {
      "index": 0,
      "finish_reason": "stop",
      "message": {
        "role": "assistant",
        "content": "**1. “Drop It Like It’s Hotspot”** – A cheeky nod to the song that’ll make anyone pause and grin while they log in.\n\n**2. “FBI Surveillance Van #42”** – The classic prank that’ll have neighbors double-checking their curtains.\n\n**3. “Tell My Wi-Fi I’m Sorry”** – Because we all know the neighbor who borrows bandwidth—now they’ll feel the love (and the guilt).",
        "reasoning": "We need to give top 3 witty WiFi network names that will make neighbors laugh, with one-line reason each. Be brief.",
        "tool_calls": []
      }
    }
  ],
  "usage": {
    "prompt_tokens": 107,
    "completion_tokens": 160,
    "total_tokens": 267
  }
}

qwen3-30b-a3b-fp8

This model supports the Chat Completions API only — the Responses endpoint (/v1/responses) is not available for this model.

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 before answering and returns that reasoning alongside the final text, and it supports function calling, streaming, batch tasks, and 100+ languages with a 32,768-token context window. When you want frontier-style reasoning and tool use in a lighter, multilingual package, this is the model.

References: Model docs • Terms • Privacy

Chat Completions

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -d '{
  "model": "qwen3-30b-a3b-fp8",
  "messages": [
    {
      "role": "system",
      "content": "You are a pragmatic senior engineer. Be brief."
    },
    {
      "role": "user",
      "content": "My API is getting rate-limited by a third party. Give me 3 battle-tested strategies to handle it, one line each."
    }
  ],
  "max_tokens": 800
}'

The model reasons before it answers and returns the trace alongside the final text as a reasoning field on the message.

Response

{
  "id": "id-1784324156320",
  "object": "chat.completion",
  "created": 1784324156,
  "model": "qwen3-30b-a3b-fp8",
  "choices": [
    {
      "index": 0,
      "finish_reason": "stop",
      "message": {
        "role": "assistant",
        "content": "**1. Exponential backoff with jitter** – Retry on 429s with doubling delays plus randomness so your retries don't stampede in sync.\n\n**2. Client-side token bucket** – Throttle your own outbound rate just below the provider's limit so you never hit the wall in the first place.\n\n**3. Cache and coalesce** – Cache hot responses and merge duplicate in-flight requests so repeat calls never reach the third party at all.",
        "reasoning": "The user's API is being rate-limited by a third party and wants 3 battle-tested strategies, one line each. The classic answers: exponential backoff with jitter, client-side throttling below the limit, and caching plus request coalescing. Keep each to one line.",
        "tool_calls": []
      }
    }
  ],
  "usage": {
    "prompt_tokens": 52,
    "completion_tokens": 143,
    "total_tokens": 195
  }
}

LFM2.5-1.2B-Instruct

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 best-in-class instruction following and native tool-calling at the 1B scale — something very few models this size can do reliably. Its hybrid design gives it significantly faster inference with lower memory usage compared to transformer-only models of the same size. If you need a conversational model that can actually run on real devices without a GPU, this is it. Supports 8 languages including English, Chinese, Japanese, and Spanish.

References: Model docs • Terms • Privacy

curl https://api.zerogpu.ai/v1/responses \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "input": "I just completed a 5K run in 28 minutes. Give me a short motivational follow-up message.",
  "model": "LFM2.5-1.2B-Instruct",
  "instructions": "You are a friendly in-app assistant for a fitness app."
}'

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "LFM2.5-1.2B-Instruct",
  "messages": [
    {
      "role": "system",
      "content": "You are a friendly in-app assistant for a fitness app."
    },
    {
      "role": "user",
      "content": "I just completed a 5K run in 28 minutes. Give me a short motivational follow-up message."
    }
  ]
}'

Response

That's an amazing accomplishment! Keep up the great work, each step brings you closer to your goals. You're stronger than you think!

Would you like tips to help you keep improving?

LFM2.5-1.2B-Thinking

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 multi-step math, planning, data extraction, and agentic tool-calling, you get verifiable intermediate reasoning, not just a final guess. At 1.2B parameters it fits under a gigabyte and runs fast on CPU at the edge, rivaling models several times its size on reasoning benchmarks. Reach for a frontier model when the task is knowledge-heavy or code-intensive, and keep this one as the planning brain that decides and orchestrates. When the task needs reasoning, this is the model.

References: Model docs • Terms • Privacy

curl https://api.zerogpu.ai/v1/responses \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "input": "Our whole team got locked out of the dashboard this morning after the payment failed. We have a product demo with a customer in 2 hours and cannot log in. Card on file should be valid.",
  "model": "LFM2.5-1.2B-Thinking",
  "instructions": "You are a support triage assistant. Read the ticket, briefly reason about the signals, then assign one team (Billing, Technical, Account, General) and one priority (Low, Medium, High). Decide once and commit. End with a single line: TEAM | PRIORITY."
}'

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "LFM2.5-1.2B-Thinking",
  "messages": [
    {
      "role": "system",
      "content": "You are a support triage assistant. Read the ticket, briefly reason about the signals, then assign one team (Billing, Technical, Account, General) and one priority (Low, Medium, High). Decide once and commit. End with a single line: TEAM | PRIORITY."
    },
    {
      "role": "user",
      "content": "Our whole team got locked out of the dashboard this morning after the payment failed. We have a product demo with a customer in 2 hours and cannot log in. Card on file should be valid."
    }
  ]
}'

The model returns its answer alongside an explicit reasoning trace.

Response

The capital of Japan is **Tokyo**.

**Reasoning:**
1. Japan's political, cultural, and economic center is located in Tokyo.
2. Historically, Tokyo has served as Japan's...

zlm-v1-followup-questions-edge

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 general-purpose model has to be coaxed into this with prompting, this one is purpose built: every output is a ready-to-render question, no parsing or cleanup. Small enough to run inline at the edge, it adds a “what to ask next” layer to any surface for a fraction of a cent per call. When you want to turn a dead end into the next click, this is the model.

References: Model docs • Terms • Privacy

curl https://api.zerogpu.ai/v1/responses \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "input": "Electric vehicles are gaining popularity worldwide as battery costs drop and charging infrastructure expands. Most modern EVs offer a range of 250 to 350 miles on a single charge, and fast-charging stations can replenish 80% of battery capacity in under 30 minutes. However, concerns around battery disposal, rare earth mineral sourcing, and grid capacity continue to shape the debate around large-scale EV adoption.",
  "model": "zlm-v1-followup-questions-edge"
}'

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "zlm-v1-followup-questions-edge",
  "messages": [
    {
      "role": "user",
      "content": "Electric vehicles are gaining popularity worldwide as battery costs drop and charging infrastructure expands. Most modern EVs offer a range of 250 to 350 miles on a single charge, and fast-charging stations can replenish 80% of battery capacity in under 30 minutes. However, concerns around battery disposal, rare earth mineral sourcing, and grid capacity continue to shape the debate around large-scale EV adoption."
    }
  ]
}'

The model returns follow-up questions a reader might naturally ask next.

Response

How long does an EV battery typically last before it needs to be replaced?
What happens to electric vehicle batteries at the end of their life, and how recyclable are they?
Is the current power grid ready to handle widespread EV charging demand?

Get Started

Models

Guides

Platform

Text Generation

gpt-oss-120b

qwen3-30b-a3b-fp8

LFM2.5-1.2B-Instruct

LFM2.5-1.2B-Thinking

zlm-v1-followup-questions-edge

​gpt-oss-120b

​qwen3-30b-a3b-fp8

​LFM2.5-1.2B-Instruct

​LFM2.5-1.2B-Thinking

​zlm-v1-followup-questions-edge

gpt-oss-120b

qwen3-30b-a3b-fp8

LFM2.5-1.2B-Instruct

LFM2.5-1.2B-Thinking

zlm-v1-followup-questions-edge