Gemma 4 26B A4B IT is Google DeepMind’s instruction-tuned mixture-of-experts edition of the open Gemma 4 family. The official architecture table lists 25.2 billion total parameters and 3.8 billion active parameters; A4B names the rounded active-parameter scale. Its purpose covers reasoning, coding, agent workflows and multimodal understanding. It is separate from the pretrained edition and from the dense 31B model.
The mixture-of-experts design activates a subset of its parameters for each inference step: eight active experts from 128, plus one shared expert, in the published architecture table. Google positions this edition around inference efficiency relative to the dense model. It combines a 30-layer architecture and a 1,024-token sliding window with global attention, a 256K context and a visual encoder. These architecture figures describe the producer model, rather than a guaranteed latency or hardware configuration for a particular application.
Its 256K-token context supports a task that combines substantial text with visual evidence. Images and text can be interleaved in one prompt; the manufacturer recommends placing image content before the related text for best results. Visual tasks include object detection, document-page interpretation, screen and interface understanding, charts, multilingual OCR, handwriting and pointing. The output is text: extracted content, an explanation, code or a proposed tool action.
Video understanding processes a sequence of frames. The model card gives a maximum of 60 seconds when the images are processed at one frame per second. Prepare the relevant episode, preserve frame order and supply a question about the visible sequence. This 26B/31B visual profile does not accept audio; the speech capabilities of the smaller audio-enabled Gemma 4 variants do not apply here. If the task needs spoken information, add its transcript as text.
Configurable thinking lets the model reason before answering or give a direct answer. Gemma 4 adds a native system role alongside user and assistant roles. Google’s prompt format enables thinking with the <|think|> control token in the system prompt and separates the thought channel from the final answer; its documented processor can handle that template. For a multi-turn conversation, keep previous final answers in the history and exclude prior thoughts except in tool-call turns, where the manufacturer says they should be preserved.
Native Function Calling supports structured tool use, and Google’s launch description also documents structured JSON output. Define the task, the available function and the expected result; the application performs the requested operation and returns its output for the next step. A coding task can ask for generation, completion or correction, while a visual assistant can use the returned text to explain a chart or screen. A function-call instruction is distinct from a connected action that has actually executed.
Variable image resolution is controlled by the visual token budget. The documented options are 70, 140, 280, 560 and 1,120 tokens. Lower budgets suit classification, captions or frame sequences; higher budgets preserve more detail for OCR, document parsing and small text, with additional computation. Decide whether you need the overall scene or exact lettering before preparing the image. A clear crop of a label can be more useful to an extraction task than an unqualified request to inspect every detail of a full page.
Google recommends temperature 1.0, top-p 0.95 and top-k 64 as the family’s sampling configuration. The model card distinguishes training in more than 140 languages from out-of-the-box support in more than 35 languages. State the answer language and retain domain names, field names or formulas explicitly in the brief. The 256K figure is a context specification; the cited model card does not establish a separate universal maximum-output figure for this edition.
For a document-and-code assignment, supply the relevant page image, the written requirement and the source fragment: “Read the labels, explain how this screen differs from the specification and propose a focused correction to the provided code.” For a chart, ask the answer to separate visible values from an inferred trend. Gemma’s model card notes limits involving ambiguous language, very complex tasks and incorrect or outdated factual statements, so attach the authoritative facts that the requested result must use rather than expecting training knowledge to supply them.
", "aboutText": "Gemma 4 26B A4B IT is Google DeepMind’s instruction-tuned mixture-of-experts edition of the open Gemma 4 family. The official architecture table lists 25.2 billion total parameters and 3.8 billion active parameters; A4B names the rounded active-parameter scale. Its purpose covers reasoning, coding, agent workflows and multimodal understanding. It is separate from the pretrained edition and from the dense 31B model.\n\nThe mixture-of-experts design activates a subset of its parameters for each inference step: eight active experts from 128, plus one shared expert, in the published architecture table. Google positions this edition around inference efficiency relative to the dense model. It combines a 30-layer architecture and a 1,024-token sliding window with global attention, a 256K context and a visual encoder. These architecture figures describe the producer model, rather than a guaranteed latency or hardware configuration for a particular application.\n\nIts 256K-token context supports a task that combines substantial text with visual evidence. Images and text can be interleaved in one prompt; the manufacturer recommends placing image content before the related text for best results. Visual tasks include object detection, document-page interpretation, screen and interface understanding, charts, multilingual OCR, handwriting and pointing. The output is text: extracted content, an explanation, code or a proposed tool action.\n\nVideo understanding processes a sequence of frames. The model card gives a maximum of 60 seconds when the images are processed at one frame per second. Prepare the relevant episode, preserve frame order and supply a question about the visible sequence. This 26B/31B visual profile does not accept audio; the speech capabilities of the smaller audio-enabled Gemma 4 variants do not apply here. If the task needs spoken information, add its transcript as text.\n\nConfigurable thinking lets the model reason before answering or give a direct answer. Gemma 4 adds a native system role alongside user and assistant roles. Google’s prompt format enables thinking with the <|think|> control token in the system prompt and separates the thought channel from the final answer; its documented processor can handle that template. For a multi-turn conversation, keep previous final answers in the history and exclude prior thoughts except in tool-call turns, where the manufacturer says they should be preserved.\n\nNative Function Calling supports structured tool use, and Google’s launch description also documents structured JSON output. Define the task, the available function and the expected result; the application performs the requested operation and returns its output for the next step. A coding task can ask for generation, completion or correction, while a visual assistant can use the returned text to explain a chart or screen. A function-call instruction is distinct from a connected action that has actually executed.\n\nVariable image resolution is controlled by the visual token budget. The documented options are 70, 140, 280, 560 and 1,120 tokens. Lower budgets suit classification, captions or frame sequences; higher budgets preserve more detail for OCR, document parsing and small text, with additional computation. Decide whether you need the overall scene or exact lettering before preparing the image. A clear crop of a label can be more useful to an extraction task than an unqualified request to inspect every detail of a full page.\n\nGoogle recommends temperature 1.0, top-p 0.95 and top-k 64 as the family’s sampling configuration. The model card distinguishes training in more than 140 languages from out-of-the-box support in more than 35 languages. State the answer language and retain domain names, field names or formulas explicitly in the brief. The 256K figure is a context specification; the cited model card does not establish a separate universal maximum-output figure for this edition.\n\nFor a document-and-code assignment, supply the relevant page image, the written requirement and the source fragment: “Read the labels, explain how this screen differs from the specification and propose a focused correction to the provided code.” For a chart, ask the answer to separate visible values from an inferred trend. Gemma’s model card notes limits involving ambiguous language, very complex tasks and incorrect or outdated factual statements, so attach the authoritative facts that the requested result must use rather than expecting training knowledge to supply them.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Compare readings and inferred trends\n\nAnalyze this transcribed chart data: week 1, 20 returns from 1,000 orders; week 2, 24 from 1,200; week 3, 18 from 600. Calculate each return rate, distinguish counts from rates and write a two-sentence operations note. Do not infer the cause of any change from these values alone.A three-row rate table and an operations note separating observed values from causal interpretation.Check percentages use each week's own order count and the note does not equate fewer returns with a lower rate.google/gemma-4-26b-a4b-itGemma 4 26B A4B IT is Google’s instruction-tuned mixture-of-experts model for text and visual reasoning. It combines a 256K context, image and frame-based video understanding, configurable thinking and native function calling with text output.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Gemma 4 26B A4B IT is Google DeepMind’s instruction-tuned mixture-of-experts edition of the open Gemma 4 family. The official architecture table lists 25.2 billion total parameters and 3.8 billion active parameters; A4B names the rounded active-parameter scale. Its purpose covers reasoning, coding, agent workflows and multimodal understanding. It is separate from the pretrained edition and from the dense 31B model.
The mixture-of-experts design activates a subset of its parameters for each inference step: eight active experts from 128, plus one shared expert, in the published architecture table. Google positions this edition around inference efficiency relative to the dense model. It combines a 30-layer architecture and a 1,024-token sliding window with global attention, a 256K context and a visual encoder. These architecture figures describe the producer model, rather than a guaranteed latency or hardware configuration for a particular application.
Its 256K-token context supports a task that combines substantial text with visual evidence. Images and text can be interleaved in one prompt; the manufacturer recommends placing image content before the related text for best results. Visual tasks include object detection, document-page interpretation, screen and interface understanding, charts, multilingual OCR, handwriting and pointing. The output is text: extracted content, an explanation, code or a proposed tool action.
Video understanding processes a sequence of frames. The model card gives a maximum of 60 seconds when the images are processed at one frame per second. Prepare the relevant episode, preserve frame order and supply a question about the visible sequence. This 26B/31B visual profile does not accept audio; the speech capabilities of the smaller audio-enabled Gemma 4 variants do not apply here. If the task needs spoken information, add its transcript as text.
Configurable thinking lets the model reason before answering or give a direct answer. Gemma 4 adds a native system role alongside user and assistant roles. Google’s prompt format enables thinking with the <|think|> control token in the system prompt and separates the thought channel from the final answer; its documented processor can handle that template. For a multi-turn conversation, keep previous final answers in the history and exclude prior thoughts except in tool-call turns, where the manufacturer says they should be preserved.
Native Function Calling supports structured tool use, and Google’s launch description also documents structured JSON output. Define the task, the available function and the expected result; the application performs the requested operation and returns its output for the next step. A coding task can ask for generation, completion or correction, while a visual assistant can use the returned text to explain a chart or screen. A function-call instruction is distinct from a connected action that has actually executed.
Variable image resolution is controlled by the visual token budget. The documented options are 70, 140, 280, 560 and 1,120 tokens. Lower budgets suit classification, captions or frame sequences; higher budgets preserve more detail for OCR, document parsing and small text, with additional computation. Decide whether you need the overall scene or exact lettering before preparing the image. A clear crop of a label can be more useful to an extraction task than an unqualified request to inspect every detail of a full page.
Google recommends temperature 1.0, top-p 0.95 and top-k 64 as the family’s sampling configuration. The model card distinguishes training in more than 140 languages from out-of-the-box support in more than 35 languages. State the answer language and retain domain names, field names or formulas explicitly in the brief. The 256K figure is a context specification; the cited model card does not establish a separate universal maximum-output figure for this edition.
For a document-and-code assignment, supply the relevant page image, the written requirement and the source fragment: “Read the labels, explain how this screen differs from the specification and propose a focused correction to the provided code.” For a chart, ask the answer to separate visible values from an inferred trend. Gemma’s model card notes limits involving ambiguous language, very complex tasks and incorrect or outdated factual statements, so attach the authoritative facts that the requested result must use rather than expecting training knowledge to supply them.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits google/gemma-4-26b-a4b-it | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0003 / 1K tokens Output · Output tokens $0.0012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0003 / 1K tokens Output · Output tokens $0.0012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "google/gemma-4-26b-a4b-it", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='google/gemma-4-26b-a4b-it',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='google/gemma-4-26b-a4b-it',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra google/gemma-4-26b-a4b-it
Use the method paths shown above.
google/gemma-4-26b-a4b-itGemma 4 26B A4B IT is Google’s instruction-tuned mixture-of-experts model for text and visual reasoning. It combines a 256K context, image and frame-based video understanding, configurable thinking and native function calling with text output.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Gemma 4 26B A4B IT is Google DeepMind’s instruction-tuned mixture-of-experts edition of the open Gemma 4 family. The official architecture table lists 25.2 billion total parameters and 3.8 billion active parameters; A4B names the rounded active-parameter scale. Its purpose covers reasoning, coding, agent workflows and multimodal understanding. It is separate from the pretrained edition and from the dense 31B model.
The mixture-of-experts design activates a subset of its parameters for each inference step: eight active experts from 128, plus one shared expert, in the published architecture table. Google positions this edition around inference efficiency relative to the dense model. It combines a 30-layer architecture and a 1,024-token sliding window with global attention, a 256K context and a visual encoder. These architecture figures describe the producer model, rather than a guaranteed latency or hardware configuration for a particular application.
Its 256K-token context supports a task that combines substantial text with visual evidence. Images and text can be interleaved in one prompt; the manufacturer recommends placing image content before the related text for best results. Visual tasks include object detection, document-page interpretation, screen and interface understanding, charts, multilingual OCR, handwriting and pointing. The output is text: extracted content, an explanation, code or a proposed tool action.
Video understanding processes a sequence of frames. The model card gives a maximum of 60 seconds when the images are processed at one frame per second. Prepare the relevant episode, preserve frame order and supply a question about the visible sequence. This 26B/31B visual profile does not accept audio; the speech capabilities of the smaller audio-enabled Gemma 4 variants do not apply here. If the task needs spoken information, add its transcript as text.
Configurable thinking lets the model reason before answering or give a direct answer. Gemma 4 adds a native system role alongside user and assistant roles. Google’s prompt format enables thinking with the <|think|> control token in the system prompt and separates the thought channel from the final answer; its documented processor can handle that template. For a multi-turn conversation, keep previous final answers in the history and exclude prior thoughts except in tool-call turns, where the manufacturer says they should be preserved.
Native Function Calling supports structured tool use, and Google’s launch description also documents structured JSON output. Define the task, the available function and the expected result; the application performs the requested operation and returns its output for the next step. A coding task can ask for generation, completion or correction, while a visual assistant can use the returned text to explain a chart or screen. A function-call instruction is distinct from a connected action that has actually executed.
Variable image resolution is controlled by the visual token budget. The documented options are 70, 140, 280, 560 and 1,120 tokens. Lower budgets suit classification, captions or frame sequences; higher budgets preserve more detail for OCR, document parsing and small text, with additional computation. Decide whether you need the overall scene or exact lettering before preparing the image. A clear crop of a label can be more useful to an extraction task than an unqualified request to inspect every detail of a full page.
Google recommends temperature 1.0, top-p 0.95 and top-k 64 as the family’s sampling configuration. The model card distinguishes training in more than 140 languages from out-of-the-box support in more than 35 languages. State the answer language and retain domain names, field names or formulas explicitly in the brief. The 256K figure is a context specification; the cited model card does not establish a separate universal maximum-output figure for this edition.
For a document-and-code assignment, supply the relevant page image, the written requirement and the source fragment: “Read the labels, explain how this screen differs from the specification and propose a focused correction to the provided code.” For a chart, ask the answer to separate visible values from an inferred trend. Gemma’s model card notes limits involving ambiguous language, very complex tasks and incorrect or outdated factual statements, so attach the authoritative facts that the requested result must use rather than expecting training knowledge to supply them.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits google/gemma-4-26b-a4b-it | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0003 / 1K tokens Output · Output tokens $0.0012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0003 / 1K tokens Output · Output tokens $0.0012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "google/gemma-4-26b-a4b-it",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'google/gemma-4-26b-a4b-it', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'google/gemma-4-26b-a4b-it', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra google/gemma-4-26b-a4b-it
Use the method paths shown above.
Technical example in English
Compare readings and inferred trends\n\nAnalyze this transcribed chart data: week 1, 20 returns from 1,000 orders; week 2, 24 from 1,200; week 3, 18 from 600. Calculate each return rate, distinguish counts from rates and write a two-sentence operations note. Do not infer the cause of any change from these values alone.A three-row rate table and an operations note separating observed values from causal interpretation.Check percentages use each week's own order count and the note does not equate fewer returns with a lower rate.