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Google Developer Experts profile image Robina Mirbahar
Robina Mirbahar for Google Developer Experts

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Building a Live 3D & 1M-Token Engineering Cookbook with Google DeepMind's Gemini 4 Argon

#webdev #ai #python #opensource

Robina's Gemini 4 Argon 3D & Coding Cookbook

Google DeepMind recently introduced Gemini 4 Argon (gemini-4-argon), marking a major architectural leap for long-horizon software engineering and 3D spatial reasoning:

  • 1,000,000 Input / 1,000,000 Output Unified Token Horizon (a 15.6x increase in output capacity from 64K tokens via the Long-Decode Continuation Protocol)
  • 87.0% SOTA on Embodied 3D Reasoning (ERQA) and 91.7% on LVBench (long video understanding)
  • 77.9% SOTA on DeepSWE v1.1 (autonomous software engineering and multi-file refactoring)
  • First-Class thought Summaries & Cryptographic Signatures via the Gemini Interactions API (client.interactions.create)

Instead of just reading the benchmarks, I built and open-sourced the Gemini 4 Argon Cookbook & Live 3D Starter Kit from scratch -- complete with live playable WebGL 3D simulations, a 100% pure-Python zero-dependency 3D raytracer, a procedural 3D Wavefront .obj Pagoda generator, and production 1M-token continuation loops.

Try the Live 3D Demos in Your Browser

1. Unifying 1M Input & 1M Output Tokens (Long-Decode Continuation Protocol)

Generating up to 1,000,000 output tokens in a single logical generation requires resilient connection handling. Gemini 4 Argon introduces the Long-Decode Continuation Protocol in the Gemini Interactions API v2.

When a generation slice reaches a per-turn boundary while the model still has more content to emit, interaction.status returns "incomplete" along with a cryptographic continuation_token. You simply pass that continuation_token into the next client.interactions.create(...) call to seamlessly resume decoding:

from google import genai

client = genai.Client()

def generate_up_to_1m_tokens(prompt: str) -> str:
    full_output: list[str] = []
    continuation_token: str | None = None

    while True:
        kwargs = {
            "model": "gemini-4-argon",
            "generation_config": {
                "thinking_level": "high",
                "thinking_summaries": "auto",
                "max_output_tokens": 1_000_000,
            },
        }
        if continuation_token is None:
            kwargs["input"] = prompt
        else:
            kwargs["continuation_token"] = continuation_token

        interaction = client.interactions.create(**kwargs)

        for item in interaction.outputs or []:
            if getattr(item, "type", None) == "text":
                full_output.append(item.text)

        if interaction.status == "incomplete" and getattr(interaction, "continuation_token", None):
            continuation_token = interaction.continuation_token
            continue
        break

    return "".join(full_output)
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Full Runnable Examples:

2. First-Class thought Steps & Cryptographic Signatures

With client.interactions.create, reasoning is no longer hidden in raw text blocks. Gemini 4 Argon emits structured thought outputs containing both a human-readable summary and a verifiable signature (thought_signature) so multi-turn agentic workflows can preserve exact reasoning state across turns:

stream = client.interactions.create(
    model="gemini-4-argon",
    input="Design a lock-free ring buffer in Rust and prove linearizability.",
    stream=True,
    generation_config={
        "thinking_level": "high",
        "thinking_summaries": "auto",
    },
)

for chunk in stream:
    if chunk.event_type == "content.delta":
        if chunk.delta.type == "thought_summary":
            print(f"[Thought] {chunk.delta.content.text}")
        elif chunk.delta.type == "thought_signature":
            print(f"[Signature] {chunk.delta.signature[:24]}...")
        elif chunk.delta.type == "text":
            print(chunk.delta.text, end="", flush=True)
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Full Runnable Example: examples/02_deep_reasoning_thoughts.py

3. Built-From-Scratch 3D Engines & Interactive WebGL Simulations (87% ERQA SOTA)

To test Gemini 4 Argon's 3D spatial reasoning, I created four 100% original, built-from-scratch 3D engines and interactive simulations:

A. Procedural 5-Tier 3D Pagoda Temple (.OBJ Generator + Live WebGL)

Procedural 3D Pagoda Temple

examples/08_procedural_3d_pagoda_temple.py uses pure Python math (sin, cos, parabolic catenary curves) with zero external dependencies to procedurally generate a multi-tiered 3D architectural Pagoda Temple as a standard Wavefront .obj 3D mesh, complete with upturned eaves, corner pillars, and a golden finial spire. You can also explore it live in the browser under Preset #2 in the Main 3D Studio.

B. Interactive 3D Crystalline Shatter & Voxel Physics Arena

3D Crystalline Shatter Physics Arena

Play Live in Your Browser | Source (09_3d_physics_shatter_arena.html)

Click anywhere in 3D space to fire high-velocity kinetic spheres via 3D raycasting (THREE.Raycaster). On impact, a 240-shard crystal monolith fractures using radial impulse transfer, gravity, angular momentum, floor restitution, expanding shockwave rings, and built-in .webm video recording.

C. 6-DOF 3D Robotics Inverse Kinematics & Ballistic Simulator

6-DOF 3D Robotics IK Simulator

Play Live in Your Browser | Source (10_3d_robotics_ik_simulator.html)

Solves 2-link planar + base yaw 3D inverse kinematics (acos / atan2 law of cosines) and parabolic ballistic trajectories. Click Toss Payload at Target to watch the articulated 6-DOF robot arm track a moving target platform and launch payloads right into the bullseye.

D. 100% Pure-Python Zero-Dependency 3D Ray-Tracing Engine

Pure-Python 3D Ray-Traced Scene

examples/04_vanilla_3d_raytracer.py implements a complete 3D ray-tracing engine using only the Python standard library (math, struct, zlib) -- rendering recursive mirror reflections, Blinn-Phong specular highlights, soft area-light shadows, and a procedural checkerboard parquet floor directly to .ppm and .png.

4. Multimodal Image & Video -> 3D Scene Graph Reconstruction

In examples/07_multimodal_image_video_to_3d.py, you can pass any 2D photograph or video clip to gemini-4-argon with a strict JSON Schema (response_schema) to extract a 3D scene graph containing:

  • 3D bounding boxes (center_xyz, dimensions_whd, rotation_euler_deg)
  • Physically Based Rendering (PBR) materials (roughness, metallic, albedo_hex)
  • Rigid-body physics mass (estimated_mass_kg) and camera intrinsics (fov_degrees)

And in the Live 3D WebGL Studio, you can click Upload Image or Video -> 3D Mesh to turn any photo or video on your device into a live 3D depth-mapped point cloud in real time!

Quick Start 

git clone https://github.com/RobinaMirbahar/gemini-4-argon-cookbook.git
cd gemini-4-argon-cookbook
pip install -r requirements.txt

# 1. Generate a 3D Pagoda Temple Wavefront (.obj) mesh from scratch
python examples/08_procedural_3d_pagoda_temple.py

# 2. Render the 100% Pure-Python 3D Raytracer (.ppm / .png)
python examples/04_vanilla_3d_raytracer.py

# 3. Run Multimodal Image/Video -> 3D Scene Reconstruction
python examples/07_multimodal_image_video_to_3d.py --demo

# 4. Run the 1M Token Long-Decode Continuation Protocol
python examples/01_long_decode_continuation.py --demo
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What Are You Building First?

Check out the full open-source repository at github.com/RobinaMirbahar/gemini-4-argon-cookbook and give it a star if you find it useful! Let me know in the comments whether you're most excited about Gemini 4 Argon's 1M output token horizon, autonomous code auditing, or 3D spatial reasoning.

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