Text Generation

Text generation models produce new text from an input prompt. ZeroGPU offers open-weight heavyweights — DeepSeek’s deepseek-v4.1-flash and deepseek-v4-flash-0731, OpenAI’s gpt-oss-120b, Alibaba’s qwen3-30b-a3b-fp8, and Z.ai’s glm-5.2 and glm-5.3-flash — for reasoning and tool use, OpenAI’s gpt-5.4-nano, gpt-5.6-luna, and gpt-4.1-mini for cost-efficient high-volume work, plus conversational and reasoning models from Liquid AI, Meta’s llama-3.1-8b-instruct-fast for high-volume summarization, and Meta’s llama-guard-4-12b for safety classification. Each request is shown for both the Responses API and the OpenAI-compatible Chat Completions API — except qwen3-30b-a3b-fp8, glm-5.2, and deepseek-v4-flash-0731, which are Chat Completions only.

Model	Input /1M	Output /1M	Cached input /1M	Max tokens
`gpt-oss-120b`	$0.15	$0.60	$0.03	131,072
`qwen3-30b-a3b-fp8`	$0.10	$0.45	$0.05	32,768
`glm-5.2`	$1.10	$3.50	$0.26	262,144
`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
`gpt-5.4-nano`	$0.20	$1.25	$0.02	400,000
`gpt-5.6-luna`	$0.20	$1.20	$0.20	272,000
`gpt-4.1-mini`	$0.40	$1.60	$0.10	1,047,576
`deepseek-v4-flash-0731`	$0.16	$0.38	$0.006	1,048,576
`llama-3.1-8b-instruct-fast`	$0.15	$0.28	$0.025	131,072
`llama-guard-4-12b`	$0.18	$0.18	—	163,840
`LFM2.5-1.2B-Instruct`	$0.02	$0.05	—	32,768
`LFM2.5-1.2B-Thinking`	$0.02	$0.05	—	32,768

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
  }
}

glm-5.2

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

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, served on ZeroGPU for general text generation. It sustains a 262,144-token (256K) context, reasons through a problem with flexible thinking effort before answering, and supports function calling and batch tasks. MIT-licensed with no usage restrictions. When the work spans large codebases, day-long agent sessions, or long documents, 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": "glm-5.2",
  "messages": [
    {
      "role": "system",
      "content": "You are a precise code-review assistant. Be brief."
    },
    {
      "role": "user",
      "content": "Review this function name: getUserDataAndSendEmailAndLog(). What does it tell you about the function, and how would you refactor it? 3 bullets max."
    }
  ],
  "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-1784840133815",
  "object": "chat.completion",
  "created": 1784840133,
  "model": "glm-5.2",
  "choices": [
    {
      "index": 0,
      "finish_reason": "stop",
      "message": {
        "role": "assistant",
        "content": "**1. The name confesses the crime** – \"And\" in a function name means it violates single responsibility: fetching, emailing, and logging are three separate concerns.\n\n**2. Split it** – Extract `getUserData()`, `sendEmail()`, and `logActivity()`, each independently testable and reusable.\n\n**3. Orchestrate at the edge** – If the sequence matters, compose them in a thin workflow function named for the *why* (e.g. `onUserSignup()`), not the *how*.",
        "reasoning": "The name reveals the function does three unrelated things — fetch data, send email, log — a single-responsibility violation. Recommend splitting into three functions plus a thin orchestrator. Keep to 3 bullets.",
        "tool_calls": []
      }
    }
  ],
  "usage": {
    "prompt_tokens": 58,
    "completion_tokens": 151,
    "total_tokens": 209
  }
}

deepseek-v4.1-flash

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, served on ZeroGPU for general text generation. It keeps the 1,048,576-token (1M) context window of the V4 Flash line, which suits large codebases, long documents, extended conversations, and multi-step agent tasks, and supports function calling alongside both fast non-thinking replies and higher-effort reasoning.

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": "Hello!",
  "model": "deepseek-v4.1-flash"
}'

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": "deepseek-v4.1-flash",
  "messages": [
    {
      "role": "system",
      "content": "You are a support triage analyst. Be concise."
    },
    {
      "role": "user",
      "content": "Classify this ticket by urgency (low/medium/high) and category, then summarize it in one line: '"'"'Our checkout page has been throwing intermittent 502 errors since this morning'"'"'s deploy. Roughly 1 in 5 customers can'"'"'t complete payment, and abandoned carts are spiking. Rolling back didn'"'"'t help."
    }
  ],
  "max_tokens": 800
}'