To prompt Gemini 4 Argon well, put your rules first, your documents next and your question last, then pick a thinking level instead of capping output tokens. Argon ignores custom temperature, top_p and top_k values, and Google says it does not follow length targets. Structure and settings now do the work that sampling tricks and word counts used to do.
This guide shows how to prompt Gemini 4 Argon for real work, with 12 copy-paste Gemini 4 Argon prompts for document analysis, extraction, planning, code, charts, video, writing and learning content. It also explains the settings behind them: thinking levels, token limits and long decode. Details are current as of October 2026.
One honest limit up front. The templates are built from Google’s documentation and the strengths listed on the model card. We have not run them, and nothing below is a real Argon output. Treat each one as a starting point to test on your own material before you rely on it.
How is prompting Gemini 4 Argon different from earlier Gemini models?
Mostly in what you stop doing. Google’s documentation says Gemini 4 models disregard any temperature, top_p or top_k setting you pass, so remove them from your requests. You steer output with instructions, structure and the thinking level instead. The core habits from Google’s prompt guide still apply: be direct, send context first and ask last.
The rules below come from Google’s prompt design guide:
- Put critical constraints, personas and output format rules in the system instruction, or open the user prompt with them.
- Supply all documents or code first, then put the question at the end, linked by a phrase such as “Based on the information above.”
- Pick one delimiter style, such as XML-style tags, and keep it consistent.
- Include few-shot examples. Google recommends them, but warns that too many can make the model overfit to them.
- Ask for detail when you want it. Google’s guide says Gemini 3 models answer directly and efficiently by default.
Google’s launch announcement adds that Argon follows style instructions better, hedges less and repeats itself less than earlier Gemini models. That is why the writing template later in this guide leans on style rules rather than persuasion.
The Argon model card lists the work it suits: multi-turn investigations, complex engineering, deep visual understanding, multimodal document analysis, legal and financial analysis, and scientific reasoning. It also lists hallucinations and occasional slowness or timeouts as limits. The templates below are grouped by those uses, and every one asks for quotes or locations you can check.
If you also work with OpenAI’s model, the habits differ in places. See our guide to how to prompt GPT-6 Astra for the comparison.
What basic prompt structure should you start from?
Use a five-part skeleton: rules, inputs, task, output format and a definition of done. Put the rules first, the inputs in tagged blocks and the task last. This follows Google’s advice on context placement. It also gives every template below the same shape, so you change the contents and keep the structure.
You are a ROLE. Rules that apply to everything below: - Use only the supplied material. If it does not say, write "Not stated". - STYLE OR SAFETY RULE - Output format: TABLE, JSON, OUTLINE <documents> DOCUMENT TEXT OR ATTACHED FILES </documents> Based on the documents above, ONE SPECIFIC TASK. Done means: CHECKABLE END STATE.
Replace everything in double braces and delete any line you do not need. The “Not stated” rule matters most. It gives the model a safe answer when the material is silent, which is where invented detail usually starts.
Which Gemini 4 Argon thinking level should you use?
Start at medium. It is the default, and Google recommends it for most tasks, including complex code and agentic work. Use low for simple, well-defined jobs and high for hard math, science or difficult code and agent tasks. Google’s advice is to test all three levels on your own evaluation set and keep the lowest one that clears your quality bar.
Note that thinking_level, max_output_tokens and long decode are API settings. The Gemini app announcement we reviewed describes a chat and tasks toggle but no thinking control. App users get the prompt techniques, but not these dials. Our guide to accessing Gemini 4 Argon shows where each option lives.
| Level | What Google says | Good starting use (our suggestion) | Watch for |
|---|---|---|---|
| Low | Fewest tokens and lowest latency; quality can drop on hard tasks | Extraction, classification, reformatting, short rewrites | Missed steps in multi-step reasoning |
| Medium (default) | Recommended for most tasks, including complex code and agentic use | Document review, planning, code changes, learning content | May devote more of a token limit to thinking than earlier models |
| High | Most tokens and slowest first token; suited to hard math, science and difficult code or agent tasks | Hard reasoning that failed at medium | Cost, latency and empty output when the token limit is small |
The first column and the “What Google says” column come from Google’s Gemini 4 Argon guide. The “good starting use” column is our judgment, so test it.
Thinking costs money. Google bills thinking tokens at the output rate. On the standard tier that is $10 per million tokens through November 30, 2026 and $20 from December 1. Our Gemini 4 Argon pricing breakdown has the full tier table.
Log Every Level You Test
Keep a one-page log with the prompt version, thinking level, tokens used and whether a reviewer accepted the answer. Because thinking tokens bill as output, the log shows what each accepted answer costs, which tells you whether high ever pays for itself.
Why does lowering max_output_tokens not make Argon cheaper?
Because max_output_tokens is a hard cap on thinking and visible output combined. Lowering it does not make Argon think less. It cuts the response off sooner. If you want lower cost or lower latency, lower thinking_level instead. Google’s thinking guide says exactly this.
If you set a limit at all, do not set it low. Google’s migration guidance says apps with limits under 32K should raise them to at least 32,768, because it may devote a larger share of the limit to thinking than earlier models did. Ordinary requests can leave the 65,536 default alone. In a normal synchronous request, any value above 65,536 returns a 400 error.
What should you do when Argon returns empty text?
Check the response status first. A status of incomplete with empty text means the model used all of its max_output_tokens while still thinking. Google bills the thinking tokens anyway, so fix it rather than retrying blindly.
- Resend the request with a higher max_output_tokens.
- Or lower thinking_level so less of the budget goes to reasoning.
- If you rely on high, never pair it with a small cap.
Can you ask Gemini 4 Argon for a word count?
You can ask, but Google says the model does not follow length targets, and it lists fixed-length requests as a poor fit for long decode. Control length through structure instead: a set number of sections, bullets, table rows or items, plus a clear end point. Then edit the result to size yourself.
For example, replace “write 800 words” with “write four sections of one paragraph each, each paragraph two to four sentences.” For lengthy outputs, Google’s thinking guide also suggests prompting the model to think less, which saves tokens.
When is long decode worth using?
Only when a single response must think and write beyond the default limit. Google’s long decode guide names hard math, research, formal proofs and the hardest coding tasks. It says not to use it for chat, Q&A, summarization, classification or extraction, which finish well under 32K tokens.
To turn it on, set background to true, store to true and max_output_tokens above 65,536. Google suggests starting at 262,144 for complex reasoning. Three costs matter:
- Time: runs can take several minutes, so avoid latency-sensitive requests.
- Money: each continuation hop resends the prompt plus everything generated so far, and every hop is billed in full, even a hop that only thinks.
- Room: output is capped at 1,048,576 tokens minus your input, so prompts near the limit leave little space.
This also explains why headlines about a million-token response mislead. Per-request output is 65,536 tokens by default. The 12 templates below are built for ordinary requests, so none of them is designed to need long decode.
How do you prompt Gemini 4 Argon for long documents and compliance review?
Give Argon every document first in tagged blocks, then ask one specific question and say what to do when the sources are silent. The model card lists multimodal document analysis and legal and financial analysis as intended uses. It also lists hallucinations as a limit, so require exact quotes and locations that a person can check.
Template 1: Multi-source synthesis with conflicts
Use this when three or more sources should agree but may not. It forces disagreements into a table instead of letting the answer smooth them over. Starting level: medium.
You are a research analyst. Rules: use only the supplied sources. Cite each claim as (source name, section). Write "Not stated" where the sources are silent. Do not smooth over disagreements. <source name="SOURCE_1_NAME">TEXT_1</source> <source name="SOURCE_2_NAME">TEXT_2</source> <source name="SOURCE_3_NAME">TEXT_3</source> Based on the sources above, answer this question: QUESTION Return: (1) a three-sentence answer, (2) a table with columns Claim | Source | Agrees or conflicts with, (3) a list of open questions the sources do not settle.
Template 2: Finance-style report review
Use this on annual reports, budget packs or variance reports. If you call the API, turn on code execution, which Argon supports, because Google’s guide recommends it for arithmetic. Starting level: medium.
You are a financial analyst reviewing DOCUMENT TYPE for AUDIENCE. Rules: quote every figure with its page or table reference. Recalculate any total or percentage you report and show the arithmetic. Label anything you infer as "Inference". Do not give investment advice. <report>REPORT_TEXT</report> Based on the report above, list the five largest year-on-year changes in METRIC GROUP, give the stated reason for each, and flag any figure that does not reconcile with another figure in the report.
Template 3: Compliance policy review for training gaps
This one is for L&D teams. It maps each policy requirement to your current course so you can see what the training does not cover. Starting level: medium.
You are an instructional designer supporting a compliance team. Rules: quote policy language exactly and do not paraphrase obligations. If a requirement can be read two ways, show both readings and mark it "Needs legal review". You are not giving legal advice. <policy>POLICY_TEXT</policy> <current_training>COURSE OUTLINE OR TRANSCRIPT</current_training> Based on the policy and training above, return a table with columns: Policy requirement (exact quote) | Audience affected | Covered in current training? (Yes, Partly, No) | Evidence | Suggested learning objective | Suggested format. Then list the three highest-risk gaps and why.
Make The SME Check Quotes
Compliance and finance outputs need a subject matter expert, not a second prompt. Because these templates demand exact quotes with locations, the expert can verify each row against the source in minutes. Reject any row whose quote they cannot find word for word.
How do you get structured data and multi-step plans from Argon?
For extraction, define the fields, say what to return when a value is missing, and use the API’s structured output instead of describing JSON in prose. Google recommends structured output for complex JSON. For plans, ask for dependencies, risks and the assumptions most likely to be wrong. Start extraction at low and planning at medium, then test.
Template 4: Structured extraction with evidence
Add one worked example, because Google recommends few-shot examples, and keep its format identical to the output you want. Validate every value in your own code, since valid JSON can still hold a wrong value. Starting level: low.
Extract the fields below from each document. Rules: if a field is missing, return null. Do not guess. For each value, add "source_quote" with the exact words that support it. <fields>FIELD NAMES, TYPES, ALLOWED VALUES</fields> <example> <input>EXAMPLE_INPUT</input> <output>EXAMPLE_OUTPUT</output> </example> <documents> <document id="1">TEXT_1</document> <document id="2">TEXT_2</document> </documents> Based on the documents above, return one JSON object per document that follows the fields and example.
Template 5: Multi-step plan with assumptions
Use this for projects with dependencies, such as an LMS migration or a course launch. It makes the plan state its guesses instead of hiding them. Starting level: medium.
You are a program manager. Rules: every task needs an owner role, a dependency, an estimate in days and a done-check. State assumptions before the plan. If information is missing, make the most reasonable assumption, list it and continue. <context>BRIEF, CONSTRAINTS, TEAM SIZE, DEADLINE</context> Based on the context above, produce a phased plan for GOAL. For each phase give tasks, dependencies, risks with a mitigation, and a go or no-go check. End with the three assumptions most likely to be wrong.
How do you prompt Gemini 4 Argon for code migrations, charts and video?
Give code migrations explicit rules and the existing tests, and ask for diffs plus a human-review list. For charts and video, attach the file before the text and ask for a table or timeline. Argon outputs text only, so request chart code or a data table, never an image. Our Gemini 4 Argon benchmarks post covers how it scores on coding and visual tests.
Template 6: Code migration with a safety rule
The “do not change behavior” rule and the review list keep the model from making confident edits it cannot justify. Start at medium, which Google recommends for complex code, and move to high only for the hardest cases.
You are a senior engineer. Rules: do not change public function signatures or behavior. Every change must trace to a migration rule below. If you are unsure a change is safe, do not make it. List it under "Needs human review". <migration_rules>E.G. REPLACE LIBRARY A WITH B; TARGET LANGUAGE VERSION</migration_rules> <code path="PATH">CODE</code> <tests>EXISTING_TESTS</tests> Based on the code and tests above, return: (1) a migration plan ordered by risk, (2) the changed code as a unified diff per file, (3) new tests that would fail if behavior changed, (4) the "Needs human review" list.
Template 7: Chart reading with a redraw check
Attach the chart image or a PDF page first. Because Argon cannot draw, the redraw code gives you a way to compare its reading against the original. Starting level: medium.
You are a data analyst.
Rules: read values only from the attached chart. When you estimate a value from a bar or line position, mark it "approx." and give a range. Do not use outside data.
ATTACH CHART IMAGE OR PDF PAGE BEFORE THIS TEXT
Based on the chart above: (1) state the chart type, axes, units and time range, (2) transcribe the data into a table, (3) give the three most important patterns, (4) list what the chart does not show that a reader might assume. Then write Python code that redraws the chart from your table so I can compare it with the original.
Template 8: Timestamped training video breakdown
Google’s video documentation says to place the text prompt after the video and to use MM:SS timestamps. It also says the default static mode samples one frame per second, while the opt-in agentic mode suits long videos and targeted questions. Starting level: medium.
You are a learning content analyst.
Rules: give an MM:SS timestamp for every claim. Describe only what is visible or audible. If something is unclear, write "Unclear at MM:SS".
ATTACH VIDEO BEFORE THIS TEXT
Based on the video above, return: (1) a five-bullet summary, (2) a timestamped outline with one line per segment, (3) any on-screen steps in order with the timestamp of each, (4) three moments where a learner could misread the demonstration.
How do you prompt Gemini 4 Argon for writing and learning content?
Write style rules as checkable constraints, supply a sample of your own writing, and control length through structure. For learning content, tie every output to an objective, a Bloom level and a source section so a subject matter expert can verify it fast. Argon returns text, so it drafts specs and items. Your authoring tool builds the course.
Template 9: Long-form writing under style rules
Rules like “sentences under 25 words” can be checked by a person or a script, which “write in a clear tone” cannot. Starting level: medium.
You are a writing editor for PUBLICATION. Style rules, applied to every sentence: - Audience: AUDIENCE. Reading level: grade 9. - Active voice. Sentences under 25 words. Paragraphs of two to four sentences. - No hedging unless the notes themselves are uncertain. - No sentence may repeat a point already made. - Structure: exactly N sections with these headings: HEADINGS. - Use only facts from the notes. Mark unsupported claims "NEEDS SOURCE". <notes>NOTES</notes> <style_sample>A SAMPLE OF YOUR OWN WRITING</style_sample> Based on the notes above, write the draft in the voice of the style sample.
Template 10: SOP to microlearning outline
This turns a standard operating procedure into short units, each with one task and one objective. Five minutes is a common microlearning target, so change it to fit yours. Starting level: medium.
You are an instructional designer using ADDIE. Rules: each unit teaches one task, has one objective and takes under five minutes. Objectives start with an observable verb. Do not add steps that are not in the SOP. <sop>SOP_TEXT</sop> <audience>ROLE, PRIOR KNOWLEDGE, WORK ENVIRONMENT</audience> Based on the SOP above, return a table with columns: Unit | Objective | Bloom level | SOP steps covered (with section) | Format | Practice activity | Check question. Then list SOP steps that are ambiguous and need an SME to confirm.
Template 11: Assessment items tied to objectives and Bloom levels
Distractor rationales let a reviewer see why each wrong answer exists, which makes weak items easy to spot. Starting level: medium.
You are an assessment writer. Rules: each item maps to exactly one learning objective and one Bloom level. Multiple-choice items have one correct answer and three plausible distractors, each tied to a specific misconception. Do not use "all of the above" or "none of the above". <objectives> 1. OBJECTIVE_1 (Bloom: LEVEL) 2. OBJECTIVE_2 (Bloom: LEVEL) </objectives> <source_material>COURSE_CONTENT</source_material> Based on the material above, write N items per objective. Return a table: Item | Objective | Bloom level | Stem | Options A to D | Correct | Distractor rationales | Source section. Flag any objective the material does not support.
Template 12: SCORM-ready content specification
Argon cannot output a SCORM package. It can write the specification an authoring tool or developer builds from. Compare tools in our AI course authoring tools comparison. Starting level: medium.
You are a learning technologist. Rules: write a specification a developer or authoring tool can build from. Do not write the course content itself. Mark anything the inputs do not specify as "TBD by designer". <course_outline>OUTLINE</course_outline> <requirements>LMS NAME, STANDARD (SCORM 1.2, SCORM 2004 OR xAPI), PASS MARK, ATTEMPTS, BRANDING</requirements> Based on the outline and requirements above, return JSON with: course title, standard, SCOs with ids, titles and objectives, completion rule, scoring rule, mastery score, bookmarking behavior, and the assessment items in each SCO. Then list the questions the LMS administrator must answer.
What does a Gemini 4 Argon prompt checklist look like?
Run the checks below before you send a prompt that matters. They cover structure, settings and review, and each one traces to Google’s documentation or to the model card’s stated limits. If a prompt fails one, fix that line before you change the model or the level.
| Check | What to do | Basis |
|---|---|---|
| Rules first | Put persona, constraints and output format at the start or in the system instruction | Google prompt guide |
| Question last | Send documents first, then the task, linked by “Based on the above” | Google prompt guide |
| One delimiter style | Use XML-style tags throughout | Google prompt guide |
| Silence rule | Say what to write when the source does not answer | Model card lists hallucinations |
| Examples | Add one or two, in the exact output format | Google prompt guide |
| Length by structure | Set sections, rows or items, never a word count | Long decode guide |
| Level, not cap | Lower thinking_level to cut cost or latency | Thinking guide |
| Cap with room | If you set max_output_tokens, keep it at 32,768 or higher | Gemini 4 Argon guide |
| Sampling settings | Remove temperature, top_p and top_k | Google prompt guide |
| Human check | Have an SME verify quotes in compliance, legal and finance output | Model card limits |
What should you do next?
Pick two templates that match work you do every week and run each on a document you already know well. Because you know the right answer, you can see where Argon is accurate and where it drifts. Do it at medium first.
Then rerun the same prompts at low and high and record the results in your log. Keep the lowest level that passes your reviewer. If you work in L&D, start with Templates 3, 10 and 11, since they are the easiest to check against a source.
If you also prompt OpenAI’s models, our ChatGPT prompts collection shows a different style. To see how Argon stacks up before you commit, read our Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5 comparison.
FAQ
Q1. How do you use Gemini 4 Argon?
Google AI Pro and Ultra subscribers get Argon in the Gemini app, which has a toggle between chat and tasks. Developers use the model ID gemini-4-argon on the paid Gemini API tier, which also works in AI Studio. Google says Ultra subscribers can try it in AI Studio and Antigravity. There is no free API tier as of October 2026.
Q2. What is the best thinking level for Gemini 4 Argon?
Start with medium, the default, which Google recommends for most tasks including complex code. Use low for simple, well-defined jobs such as classification, and high for hard math, science or difficult code and agent tasks. Google advises testing all three levels on your own evaluation set and keeping the lowest one that clears your quality bar.
Q3. Does Gemini 4 Argon support temperature, top_p and top_k?
No. Google’s documentation says Gemini 4 models disregard any temperature, top_p or top_k setting you pass, and recommends removing them from your request configuration. To shape output, use clearer instructions, a few well-formatted examples, a structured output schema where you need JSON, and the thinking level.
Q4. Why does Gemini 4 Argon return an empty response?
Usually the model spent its whole max_output_tokens limit on thinking, so the response status is incomplete and the text is empty. Google says to resend with a higher max_output_tokens or lower the thinking level. Thinking tokens are still billed, so avoid pairing high thinking with a small output cap.
Q5. Can Gemini 4 Argon follow a word-count target?
Google’s long decode guide says the model does not follow length targets, so a request such as “write exactly 1,000 words” will not be reliable. Control length by structure instead, such as a fixed number of sections, paragraphs, bullets or table rows, and trim the result by hand if you need an exact size.
Q6. How many tokens can Gemini 4 Argon output in one response?
By default, 65,536 tokens per synchronous request, and that limit counts thinking and visible output together. With long decode in background mode, Google says it can generate up to 1,048,576 tokens minus your input, because the window is shared. Long decode runs take longer and bill every continuation step.
Q7. Can Gemini 4 Argon create images or audio from a prompt?
No. It accepts text, image, video, audio and PDF input, but its output is text only. Google’s model page lists image generation, audio generation and the Live API as unsupported. If you want a chart, ask Argon for a data table or plotting code, then render it in another tool.
