--- title: Gemma 4 31B on SIE | Superlinked description: SIE, an open-source inference engine, serves Gemma 4 31B, an open vision model that reads documents and app screens into the JSON your schema asks for. canonical_url: https://superlinked.com/models/google-gemma-4-31b-it last_updated: 2026-11-01 --- # Gemma 4 31B An open vision model that reads documents and app screens into the JSON your schema asks for. Model id: `google/gemma-4-31B-it` · Endpoint: `generate` · [Hugging Face](https://huggingface.co/google/gemma-4-31B-it) Listed under [Chat, structured output and reading images](/models#chat) on [the model index](/models.md). | Accuracy | Time per request | Serverless price | |---|---|---| | – (MMLU-Pro, answers right · not measured) | – (Not timed one request at a time) | $0.45 in · $1.50 out per 1M tokens | ## On the task pages - [Extract more exact field values than Claude Sonnet 5.5, 88% cheaper](/doc-field-extraction): 94.3% Exact field values · 546 documents; $2.12 Cost per 1,000 documents - [Read app screenshots 69% cheaper than GPT-6.1 Sol](/screenshot-mining): 94.72% Field agreement · 100 screenshots; $1.88 Cost · per 1,000 screenshots ## Call it On SIE Serverless: ```python from pathlib import Path import base64 import json from sie_sdk import SIEClient client = SIEClient(api_key="sk-sie-…") schema = json.loads(""" { "type": "object", "properties": { "items": { "anyOf": [ { "type": "array", "items": { "type": "object", "properties": { "sn": { "anyOf": [ { "type": "number" }, { "type": "null" } ] }, "type": { "anyOf": [ { "type": "string", "enum": [ "LED Luminaire", "Power Supply", "Control System", "Mounting Bracket", "LED Driver", "Cable Assembly", "Junction Box", "Sensor Module" ] }, { "type": "null" } ] }, "unit": { "anyOf": [ { "type": "string", "enum": [ "pcs", "set", "unit" ] }, { "type": "null" } ] }, "brand": { "anyOf": [ { "type": "string", "enum": [ "Philips", "Osram", "Schneider Electric", "Siemens", "ABB", "Lutron", "Legrand", "Mean Well" ] }, { "type": "null" } ] }, "quantity": { "anyOf": [ { "type": "number" }, { "type": "null" } ] }, "reference": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "productSpecsJson": { "type": "object", "properties": { "power": { "type": "object", "properties": { "watts": { "anyOf": [ { "type": "number" }, { "type": "null" } ] }, "efficiency": { "anyOf": [ { "description": "Efficiency in percentage, 0-100", "type": "number" }, { "type": "null" } ] }, "powerFactor": { "anyOf": [ { "type": "number" }, { "type": "null" } ] }, "inputVoltage": { "anyOf": [ { "type": "string", "enum": [ "100-240v", "100-277v", "220-240v" ] }, { "type": "null" } ] } }, "required": [ "watts", "efficiency", "powerFactor", "inputVoltage" ], "additionalProperties": false }, "ratings": { "type": "object", "properties": { "protection": { "anyOf": [ { "type": "string", "enum": [ "ip65", "ip66", "ip67" ] }, { "type": "null" } ] } }, "required": [ "protection" ], "additionalProperties": false }, "physical": { "type": "object", "properties": { "weight": { "anyOf": [ { "type": "number" }, { "type": "null" } ] }, "material": { "anyOf": [ { "type": "string", "enum": [ "aluminum", "steel", "plastic" ] }, { "type": "null" } ] }, "mountingType": { "anyOf": [ { "type": "string", "enum": [ "surface", "recessed", "track" ] }, { "type": "null" } ] } }, "required": [ "weight", "material", "mountingType" ], "additionalProperties": false } }, "required": [ "power", "ratings", "physical" ], "additionalProperties": false } }, "required": [ "sn", "type", "unit", "brand", "quantity", "reference", "productSpecsJson" ], "additionalProperties": false } }, { "type": "null" } ] }, "header": { "type": "object", "properties": { "to": { "type": "object", "properties": { "tel": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "email": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "location": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "attention": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "reference": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "vatNumber": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "companyName": { "anyOf": [ { "type": "string" }, { "type": "null" } ] } }, "required": [ "tel", "email", "location", "attention", "reference", "vatNumber", "companyName" ], "additionalProperties": false }, "date": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "from": { "type": "object", "properties": { "tel": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "email": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "location": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "reference": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "vatNumber": { "anyOf": [ { "type": "string" }, { "type": "null" } ] }, "companyName": { "anyOf": [ { "type": "string" }, { "type": "null" } ] } }, "required": [ "tel", "email", "location", "reference", "vatNumber", "companyName" ], "additionalProperties": false }, "projectName": { "anyOf": [ { "type": "string" }, { "type": "null" } ] } }, "required": [ "to", "date", "from", "projectName" ], "additionalProperties": false } }, "required": [ "items", "header" ], "additionalProperties": false } """) instruction = "Extract data from the document image into the JSON schema. Copy values as they appear on the document. Use null for a field the document does not contain. Return only the JSON.\n\nJSON schema:\n{\n \"type\": \"object\",\n \"required\": [\n \"header\",\n \"items\"\n ],\n \"properties\": {\n \"items\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"required\": [\n \"sn\",\n \"type\",\n \"brand\",\n \"reference\",\n \"productSpecsJson\",\n \"unit\",\n \"quantity\"\n ],\n \"properties\": {\n \"sn\": {\n \"type\": \"number\"\n },\n \"type\": {\n \"enum\": [\n \"LED Luminaire\",\n \"Power Supply\",\n \"Control System\",\n \"Mounting Bracket\",\n \"LED Driver\",\n \"Cable Assembly\",\n \"Junction Box\",\n \"Sensor Module\"\n ],\n \"type\": \"string\"\n },\n \"unit\": {\n \"enum\": [\n \"pcs\",\n \"set\",\n \"unit\"\n ],\n \"type\": \"string\"\n },\n \"brand\": {\n \"enum\": [\n \"Philips\",\n \"Osram\",\n \"Schneider Electric\",\n \"Siemens\",\n \"ABB\",\n \"Lutron\",\n \"Legrand\",\n \"Mean Well\"\n ],\n \"type\": \"string\"\n },\n \"quantity\": {\n \"type\": \"number\"\n },\n \"reference\": {\n \"type\": \"string\"\n },\n \"productSpecsJson\": {\n \"type\": \"object\",\n \"required\": [\n \"power\",\n \"physical\",\n \"ratings\"\n ],\n \"properties\": {\n \"power\": {\n \"type\": \"object\",\n \"required\": [\n \"watts\",\n \"inputVoltage\"\n ],\n \"properties\": {\n \"watts\": {\n \"type\": \"number\"\n },\n \"efficiency\": {\n \"type\": \"number\",\n \"description\": \"Efficiency in percentage, 0-100\"\n },\n \"powerFactor\": {\n \"type\": \"number\"\n },\n \"inputVoltage\": {\n \"enum\": [\n \"100-240v\",\n \"100-277v\",\n \"220-240v\"\n ],\n \"type\": \"string\"\n }\n }\n },\n \"ratings\": {\n \"type\": \"object\",\n \"required\": [\n \"protection\"\n ],\n \"properties\": {\n \"protection\": {\n \"enum\": [\n \"ip65\",\n \"ip66\",\n \"ip67\"\n ],\n \"type\": \"string\"\n }\n }\n },\n \"physical\": {\n \"type\": \"object\",\n \"required\": [\n \"material\",\n \"mountingType\"\n ],\n \"properties\": {\n \"weight\": {\n \"type\": \"number\"\n },\n \"material\": {\n \"enum\": [\n \"aluminum\",\n \"steel\",\n \"plastic\"\n ],\n \"type\": \"string\"\n },\n \"mountingType\": {\n \"enum\": [\n \"surface\",\n \"recessed\",\n \"track\"\n ],\n \"type\": \"string\"\n }\n }\n }\n }\n }\n }\n }\n },\n \"header\": {\n \"type\": \"object\",\n \"required\": [\n \"from\",\n \"to\",\n \"date\",\n \"projectName\"\n ],\n \"properties\": {\n \"to\": {\n \"type\": \"object\",\n \"required\": [\n \"companyName\",\n \"reference\",\n \"location\",\n \"attention\",\n \"tel\",\n \"email\",\n \"vatNumber\"\n ],\n \"properties\": {\n \"tel\": {\n \"type\": \"string\"\n },\n \"email\": {\n \"type\": \"string\"\n },\n \"location\": {\n \"type\": \"string\"\n },\n \"attention\": {\n \"type\": \"string\"\n },\n \"reference\": {\n \"type\": \"string\"\n },\n \"vatNumber\": {\n \"type\": \"string\"\n },\n \"companyName\": {\n \"type\": \"string\"\n }\n }\n },\n \"date\": {\n \"type\": \"string\"\n },\n \"from\": {\n \"type\": \"object\",\n \"required\": [\n \"companyName\",\n \"reference\",\n \"location\",\n \"tel\",\n \"email\",\n \"vatNumber\"\n ],\n \"properties\": {\n \"tel\": {\n \"type\": \"string\"\n },\n \"email\": {\n \"type\": \"string\"\n },\n \"location\": {\n \"type\": \"string\"\n },\n \"reference\": {\n \"type\": \"string\"\n },\n \"vatNumber\": {\n \"type\": \"string\"\n },\n \"companyName\": {\n \"type\": \"string\"\n }\n }\n },\n \"projectName\": {\n \"type\": \"string\"\n }\n }\n }\n }\n}" prompt = instruction image = base64.b64encode(Path("delivery-note.jpg").read_bytes()).decode("ascii") messages = [ {"role": "system", "content": prompt}, { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image}"}}, ], }, ] result = client.chat_completions( "google/gemma-4-31B-it:h100-96k-hires-out8k-no-spec", messages, max_completion_tokens=8192, extra_body={"temperature":0,"presence_penalty":0}, response_format={ "type": "json_schema", "json_schema": {"name": "fields", "strict": True, "schema": schema}, }, ) choices = result["choices"] content = choices[0]["message"]["content"] if choices else None if not isinstance(content, str): raise ValueError("The model returned no JSON content.") print(json.loads(content)) ``` ## Next to the other language models | Model | MMLU-Pro, answers right | Time per request | Serverless price | Size | |---|---|---|---|---| | [Gemma 4 31B](/models/google-gemma-4-31b-it.md) `google/gemma-4-31B-it` | – | – | $0.45 in · $1.50 out per 1M tokens | 31.3B | | [Qwen3.8 27B](/models/qwen-qwen3-8-27b-fp8.md) `Qwen/Qwen3.8-27B-FP8` | – | 0.74 s per complete reply | $0.25 in · $2.75 out per 1M tokens | 27.8B | | [GLM-5.3-Flash](/models/zai-org-glm-5-3-flash.md) `zai-org/GLM-5.3-Flash` | – | – | $0.15 in · $0.50 out per 1M tokens | 321.3B | | [Qwen3.5 4B](/models/qwen-qwen3-5-4b.md) `Qwen/Qwen3.5-4B` | 59.3% | – | $0.305 in · $1.53 out per 1M tokens | 4.7B | | [Qwen3 4B Instruct 2507](/models/qwen-qwen3-4b-instruct-2507.md) `Qwen/Qwen3-4B-Instruct-2507` | 53.3% | – | Not on Serverless (run in your cloud) | 4.0B | – = not measured / not timed / not listed. [All 10 chat and document models](/models#chat) ## Facts - Size: 31.3B parameters - License: Apache-2.0 - Reads: Text (up to 8,192 tokens), image - Endpoint: generate ## Evaluation - Accuracy: Run on SIE against the public dataset named in each figure - Time: SIE timed on a GPU server with the client alongside, one request at a time - Prices: Serverless list prices - Sources: [Task page runs and their inputs](https://huggingface.co/datasets/superlinked/sie-task-evidence)