> ## AI Agent Context
> Get the complete documentation index at: https://aistudio.google.com/docs/llms.txt
> Use this file to discover all available pages.
> For the full documentation in a single file, fetch: https://aistudio.google.com/docs/llms-full.txt

# What's new in Gemini 4 Argon

Gemini 4 Argon (`gemini-4-argon`) is our latest, most capable model. It can reason longer on hard problems for better results on complex coding, agentic, math, and science tasks. Gemini 4 Argon is available through the Gemini API and in Google AI Studio.

Gemini 4 Argon supports a unified 1M-token context window, long decode up to 1M tokens, and tunable thinking levels (`low`, `medium`, `high`).

{% card-grid %}
  {% card description="Build apps from scratch with Gemini 4 Argon and AI Studio." href="https://aistudio.google.com/apps?model=gemini-4-argon" icon="experiment" title="Try it in AI Studio" /%}

  {% card description="Play and remix apps, built with Gemini 4 Argon." href="https://aistudio.google.com/learn/gemini-4-argon-showcase" icon="smart_toy" title="App Gallery" /%}

  {% card description="Get started with Gemini 4 Argon with starter prompts." href="https://aistudio.google.com/learn/gemini-4-argon" icon="rocket_launch" title="Get started" /%}

{% /card-grid %}

This guide explains what's new in Gemini 4 Argon, API changes, code examples, and migration guidance.

## New model  {% id="new-model" %}

| Model | Model ID | Default thinking level | Pricing | Description |
| :--- | :--- | :---: | :--- | :--- |
| Gemini 4 Argon | `gemini-4-argon` | `medium` | Available through Nov 30, 2026 at an introductory price of $2/1M input tokens and $10/1M output tokens ($4/1M input and $20/1M output thereafter). See [pricing](/docs/pricing) for more details. | Our most capable model, built to solve the hardest problems with depth and precision across real-world software engineering and knowledge work tasks. |

For complete details, see the [Gemini 4 Argon model page](/docs/models/gemini-4-argon). For introductory pricing details, see the [pricing section](/docs/latest-model#pricing) later on this page or the [pricing page](/docs/pricing#gemini-4-argon).

## Quickstart  {% id="quickstart" %}

Use the following example to send a request to `gemini-4-argon` with the recommended default `thinking_level` (`medium`) and a 64K output token limit:

{% tabs %}
  {% tab label="Python" %}
  ```python
  from google import genai
  
  client = genai.Client()
  
  interaction = client.interactions.create(
      model="gemini-4-argon",
      input="Write a three.js script that renders a realistic 3D black hole with gravitational lensing."
  )
  
  print(interaction.output_text)
  ```

  {% /tab %}

  {% tab label="JavaScript" %}
  ```javascript
  import { GoogleGenAI } from "@google/genai";
  
  const client = new GoogleGenAI({});
  
  const interaction = await client.interactions.create({
    model: "gemini-4-argon",
    input: "Write a three.js script that renders a realistic 3D black hole with gravitational lensing.",
  });
  
  console.log(interaction.output_text);
  ```

  {% /tab %}

  {% tab label="REST" %}
  ```bash
  curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "gemini-4-argon",
      "input": "Write a three.js script that renders a realistic 3D black hole with gravitational lensing."
    }'
  ```

  {% /tab %}

{% /tabs %}

## What's new in Gemini 4 Argon  {% id="whats-new-gemini-4-argon" %}

* **Unified 1M input-output token limit:** Input, thinking, and output share one window of 1,048,576 tokens.
* **Long decode:** A single response can now go past 64K tokens, up to 1M tokens minus your input. See [Long decode](/docs/long-decode).
* **Agentic video understanding:** [Agentic video understanding](/docs/video-understanding#agentic-video-understanding) is now generally available.
* **Supported in Managed Agents:** The [Antigravity agent](/docs/antigravity-agent) in Gemini Managed Agents now supports Gemini 4 Argon.

For more details, see the [Gemini 4 Argon model page](/docs/models/gemini-4-argon). For pricing details, see the [pricing page](/docs/pricing#gemini-4-argon).

## Larger output and long decode  {% id="long-decode" %}

Gemini 4 Argon can think longer on hard math, science, and coding problems.

* **Use `thinking_level` to control reasoning depth:** The thinking level decides how deeply the model reasons before it answers. `max_output_tokens` is a hard limit: the response stops once thinking and output together reach it, so set it high enough that it doesn't cut the answer short.
* **Generate up to 1M tokens with background execution:** Use background execution with `max_output_tokens` greater than 65,536 to let a single response go past 64K tokens, up to 1M tokens. The model generates long responses in hops of up to 64K tokens, and each hop continues from where the previous one stopped. With background execution, our servers run these continuation hops for you. Because each hop adds a `model_output` step to `interaction.steps`, join the text across all `model_output` steps when the interaction completes.

{% tabs %}
  {% tab label="Python" %}
  ```python
  import time
  from google import genai
  
  client = genai.Client()
  
  interaction = client.interactions.create(
      model="gemini-4-argon",
      input="Write a complete, rigorous proof of the prime number theorem.",
      background=True,
      generation_config={
          "max_output_tokens": 262144,
          "thinking_level": "high",
      },
  )
  
  while interaction.status in ("in_progress", "queued"):
      time.sleep(5)
      interaction = client.interactions.get(id=interaction.id)
  
  # Each hop adds a model_output step, so join the text of all of them.
  text = "".join(
      content.text
      for step in interaction.steps
      if step.type == "model_output"
      for content in step.content
      if content.type == "text"
  )
  print(text)
  print("Status:", interaction.status)
  print("Usage:", interaction.usage)
  ```

  {% /tab %}

  {% tab label="JavaScript" %}
  ```javascript
  import { GoogleGenAI } from "@google/genai";
  
  const client = new GoogleGenAI({});
  
  let interaction = await client.interactions.create({
    model: "gemini-4-argon",
    input: "Write a complete, rigorous proof of the prime number theorem.",
    background: true,
    generation_config: {
      max_output_tokens: 262144,
      thinking_level: "high",
    },
  });
  
  while (interaction.status === "in_progress" || interaction.status === "queued") {
    await new Promise((resolve) => setTimeout(resolve, 5000));
    interaction = await client.interactions.get(interaction.id);
  }
  
  // Each hop adds a model_output step, so join the text of all of them.
  const text = interaction.steps
    .filter((step) => step.type === "model_output")
    .flatMap((step) => step.content ?? [])
    .filter((content) => content.type === "text")
    .map((content) => content.text)
    .join("");
  console.log(text);
  console.log("Status:", interaction.status);
  console.log("Usage:", interaction.usage);
  ```

  {% /tab %}

  {% tab label="REST" %}
  ```bash
  ID=$(curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "gemini-4-argon",
      "input": "Write a complete, rigorous proof of the prime number theorem.",
      "background": true,
      "generation_config": {
        "max_output_tokens": 262144,
        "thinking_level": "high"
      }
    }' | jq -r '.id')
  
  while true; do
    RESP=$(curl -s "https://generativelanguage.googleapis.com/v1beta/interactions/$ID" \
      -H "x-goog-api-key: $GEMINI_API_KEY")
    STATUS=$(echo "$RESP" | jq -r '.status')
    if [[ "$STATUS" != "in_progress" && "$STATUS" != "queued" ]]; then
      echo "$RESP" | jq -r '[.steps[]? | select(.type == "model_output") | .content[]? | select(.type == "text") | .text] | join("")'
      break
    fi
    sleep 5
  done
  ```

  {% /tab %}

{% /tabs %}

{% callout type="note" %}
  If you set `background: true` without defining `max_output_tokens`, the server stops after 64K tokens.

{% /callout %}

Background execution is the easiest way to run long decode. It runs the continuation loop server-side until the model finishes or reaches `max_output_tokens`. If you need finer-grained control over retries, backoff, and error handling (or don't want your interactions stored), you can use client-side continuation in the [GenerateContent API](/docs/long-decode?generate-content=true). Client-side continuation is also coming soon to the Interactions API. For streaming, reconnection, token counting, and when to use long decode, see the [long decode guide](/docs/long-decode).

### Setting output token limits  {% id="setting-output-token-limits" %}

`max_output_tokens` sets the total thinking and output tokens for a response. Set it high enough that thinking doesn't cut the answer short, and low enough to limit spend when a hard prompt uses all of it.

Use these values as a starting point:

* **Standard tasks:** Keep the default of 65,536 or don't set any value.
* **Existing apps with a limit lower than 32K:** Raise `max_output_tokens` to at least 32,768. Gemini 4 Argon can spend more of the limit on thinking than earlier models. To use fewer tokens, lower the thinking level instead.
* **Complex reasoning and coding:** Set `background: true` and set `max_output_tokens` to 131,072 or 262,144, up to 1,048,576 minus your input tokens.

## Understanding thinking levels  {% id="understanding-thinking-levels" %}

The [thinking level](/docs/thinking) is your main control over quality, latency, and token use in Gemini 4 Argon:

| Thinking level | Token use | When to use |
| :--- | :--- | :--- |
| `low` | Fewest tokens, lowest latency | Simpler tasks and cost-sensitive work. Also works for analysis and writing tasks that need light thinking. Quality can drop on hard tasks. |
| `medium` (default) | Balanced | Recommended for most tasks, including complex code and agentic use cases. |
| `high` | Most tokens, slowest first token | Complex reasoning, hard math and science, and the most difficult code or agent tasks. Pairs well with long decode for long outputs. |

Run your evaluations at all three levels, then pick the lowest level that meets your quality bar. To learn more, see the [thinking guide](/docs/thinking).

{% tabs %}
  {% tab label="Python" %}
  ```python
  from google import genai
  
  client = genai.Client()
  
  interaction = client.interactions.create(
      model="gemini-4-argon",
      input="Analyze this payment processing pipeline for race conditions during retry attempts and rewrite the transaction locks safely.",
      generation_config={
          "thinking_level": "medium"  # Balanced reasoning effort for complex tasks
      }
  )
  
  print(interaction.output_text)
  ```

  {% /tab %}

  {% tab label="JavaScript" %}
  ```javascript
  import { GoogleGenAI } from "@google/genai";
  
  const client = new GoogleGenAI({});
  
  const interaction = await client.interactions.create({
    model: "gemini-4-argon",
    input: "Analyze this payment processing pipeline for race conditions during retry attempts and rewrite the transaction locks safely.",
    generation_config: {
      thinking_level: "medium", // Balanced reasoning effort for complex tasks
    },
  });
  
  console.log(interaction.output_text);
  ```

  {% /tab %}

  {% tab label="REST" %}
  ```bash
  curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "gemini-4-argon",
      "input": "Analyze this payment processing pipeline for race conditions during retry attempts and rewrite the transaction locks safely.",
      "generation_config": {
        "thinking_level": "medium"
      }
    }'
  ```

  {% /tab %}

{% /tabs %}

## Gemini 4 Argon in Managed Agents  {% id="managed-agents" %}

The [Antigravity agent](/docs/antigravity-agent) in Gemini Managed Agents supports Gemini 4 Argon. To use it, set the model in `agent_config`:

{% tabs %}
  {% tab label="Python" %}
  ```python
  from google import genai
  
  client = genai.Client()
  
  interaction = client.interactions.create(
      agent="antigravity-preview-09-2026",
      input=(
          "Audit https://web.dev for performance, Core Web Vitals, and SEO. "
          "Query Google's PageSpeed Insights API for both Mobile and Desktop strategies. "
          "Check search indexing with Google Search for site:web.dev. "
          "Format the output as a side-by-side scorecard table with prioritized fixes."
      ),
      environment="remote",
      agent_config={
          "type": "antigravity",
          "model": "gemini-4-argon",
      },
  )
  
  print(interaction.output_text)
  ```

  {% /tab %}

  {% tab label="JavaScript" %}
  ```javascript
  import { GoogleGenAI } from "@google/genai";
  
  const client = new GoogleGenAI({});
  
  const interaction = await client.interactions.create({
    agent: "antigravity-preview-09-2026",
    input:
      "Audit https://web.dev for performance, Core Web Vitals, and SEO. " +
      "Query Google's PageSpeed Insights API for both Mobile and Desktop strategies. " +
      "Check search indexing with Google Search for site:web.dev. " +
      "Format the output as a side-by-side scorecard table with prioritized fixes.",
    environment: "remote",
    agent_config: {
      type: "antigravity",
      model: "gemini-4-argon",
    },
  }, { timeout: 300000 });
  
  console.log(interaction.output_text);
  ```

  {% /tab %}

  {% tab label="REST" %}
  ```bash
  curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "Content-Type: application/json" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -d '{
      "agent": "antigravity-preview-09-2026",
      "input": "Audit https://web.dev for performance, Core Web Vitals, and SEO. Query Google'\''s PageSpeed Insights API for both Mobile and Desktop strategies. Check search indexing with Google Search for site:web.dev. Format the output as a side-by-side scorecard table with prioritized fixes.",
      "environment": "remote",
      "agent_config": {
        "type": "antigravity",
        "model": "gemini-4-argon"
      }
    }'
  ```

  {% /tab %}

{% /tabs %}

For more options, see [model selection](/docs/antigravity-agent#model-selection) in the Antigravity agent guide.

## Migration checklist  {% id="migration-checklist" %}

{% callout type="note" %}
  **Automate this migration with a coding agent.** If you use a coding agent that supports skills (like Antigravity), install the [gemini-api-dev skill](/docs/coding-agents#gemini-api-dev) and run:

  ```none
  /gemini-api-dev migrate my app to Gemini 4 Argon
  ```

{% /callout %}

### Migrate to gemini-4-argon  {% id="migrate-to-gemini-4-argon" %}

* **Update model ID:** Change your target model string to `gemini-4-argon`.
* **Review `max_output_tokens`:** If you set it explicitly today, consider raising it. We recommend at least 32,768, since Gemini 4 Argon can spend more of the limit on thinking.
* **Configure thinking level:** Choose `low`, `medium` (default), or `high`, and test your workload at all three.
* **Move off explicit context caching:** Explicit caching (`cachedContents` / `caches.create`) is not supported on Gemini 4 Argon. Remove calls that create or reference `cached_content` from your requests. [Implicit caching](/docs/caching) is enabled by default for prompts of at least 4,096 tokens and discounts cached input tokens by 95% on cache hits.
* **Remove sampling parameters:** Remove `temperature`, `top_p`, and `top_k` from your requests. If provided, they are ignored.
* **Use background execution for long outputs:** For tasks that need more than 64K tokens, set `background` to true and `max_output_tokens` greater than 65,536.
* **Increase client timeouts:** Hard prompts can take several minutes. Use background execution, or set HTTP client timeouts to at least 15 minutes.
* **Keep conversation state on the server:** Use `previous_interaction_id` for multi-turn and function calling conversations.
* **Review your cost estimates:** Every token the model processes is billed, including internal tool system instructions and function declarations when using grounding (see [Pricing](/docs/latest-model#pricing)).

## Pricing  {% id="pricing" %}

Gemini 4 Argon is available at introductory pricing through November 30, 2026:

* **Input tokens:** $2.00 per 1M tokens ($4.00 starting December 1, 2026)  *   **Cached input tokens:** Implicit caching discounts cached input by 95%, to $0.10 per 1M tokens ($0.20 starting December 1, 2026).

* **Output tokens, including thinking tokens:** $10.00 per 1M tokens ($20.00 starting December 1, 2026)

Every token the model processes is counted and billed, including thinking tokens and internal tool system instructions and function declarations when using grounding. Grounding with Google Search also remains billed per query.

Gemini 4 Argon is not available on the Free tier. For complete pricing tiers, see the [pricing page](/docs/pricing#gemini-4-argon).

## Other models  {% id="other-models" %}

* [Gemini 3.8 Flash](/docs/whats-new-gemini-3.8)
* [Gemini 3.6 Flash and 3.5 Flash-Lite](/docs/whats-new-gemini-3.6)
* [See all models](/docs/models)

## What's next  {% id="whats-next" %}

* Read the [long decode guide](/docs/long-decode) for streaming, reconnection, and token counting.
* Learn how thinking levels and thought summaries work in the [thinking guide](/docs/thinking).
* Learn about long-running jobs in the [background execution guide](/docs/background-execution).
* Test and refine prompts in [Google AI Studio](https://aistudio.google.com/prompts/new_chat?model=gemini-4-argon).