01
Deeper Thinking and Verification
Carbon spends more thinking tokens per turn than Gemini 4 Argon to explore hypotheses, verify its work and check edge cases before it responds.
Automatically append previous messages to maintain multi-turn context. May increase token usage.
Function calling
Grounding with Google Search
Source:Google Search
Thinking process
Reasoning effort
Stream Response
How can I help you today?
Automatically append previous messages to maintain multi-turn context. May increase token usage.
Function calling
Grounding with Google Search
Source:Google Search
Thinking process
Reasoning effort
Stream Response
How can I help you today?
README
Complete guide to using Gemini 4 Carbon
Gemini 4 Carbon API for Deep Reasoning and Coding
Gemini 4 Carbon reads text, images, video, audio and PDFs and answers in text. Input, thinking and answer tokens share one 1,048,576-token context window. A standard response has a 65,536-token budget for thinking and answer combined, and long decode extends it up to 1M tokens minus the input.
Key Capabilities of Gemini 4 Carbon
Carbon suits work that needs deeper reasoning and verification, such as debugging across a large codebase or analyzing several dense documents together.
02
Unified 1M-Token Context Window
Input, thinking and answer tokens share one 1,048,576-token window, so a request can include long documents or large sections of a codebase and still leave room for the model to reason and respond.
03
Long Decode Beyond 65,536 Tokens
Google's documentation gives a standard synchronous response a budget of 65,536 tokens, counting thinking and answer tokens together, and long decode raises that budget to 1M tokens minus the input. The Kie.ai API docs list no output-limit parameter for this model.
04
Coding With Build-and-Verify Loops
Google positions Carbon for deeper build-and-verify loops across complex repositories, frontend UI design and 3D scenes, isolating root causes and checking fixes against tests. On Kie.ai the model currently supports chat requests only.
05
Multimodal Knowledge Work
Carbon accepts text, images, video, audio and PDFs. It grounds answers in its sources with fewer unsupported claims when working with dense documents, spreadsheet data and slide content, and it adheres strictly to output schemas.
06
Three Thinking Levels
Low, medium and high thinking levels balance quality, latency and token use. Medium is the default and is recommended for most tasks. On Kie.ai, set reasoning_effort in the OpenAI-compatible API or generationConfig.thinkingConfig.thinkingLevel in the native API.
Gemini 4 Carbon Specifications
Model specifications for Gemini 4 Carbon from Google's documentation.
| Specification | Gemini 4 Carbon |
|---|---|
| Developer | |
| Based on | Gemini 4 Argon |
| Input | Text, image, video, audio, PDF |
| Output | Text |
| Context window | 1,048,576 tokens, shared by input, thinking and output |
| Standard output limit | 65,536 tokens per synchronous response, thinking and answer combined |
| Long decode | Up to 1M tokens minus the input in a single response, thinking and answer combined |
| Thinking levels | Low, medium (default), high; reasoning_effort or generationConfig.thinkingConfig.thinkingLevel on Kie.ai |
| Sampling parameters | Custom temperature, top_p and top_k values are ignored |
| Not supported | Image generation, audio generation, Live API |
| Knowledge cutoff | March 2026 |
Gemini 4 Carbon Benchmark Results
Results reported by Google on the Gemini 4 Carbon model card, as of October 2026.
| Benchmark | Gemini 4 Carbon | Gemini 4 Argon |
|---|---|---|
| AA Index v5.0 | 59 | 50 |
| Vals Index v2.2 | 70.4% | 69.2% |
| SWE-sweep | 9.6% | 4.3% |
| FrontierSWE 2 | 61.2% | 55.0% |
| Terminal-Bench 4.0 (AA) | 65.2% | 57.1% |
| Agents' Last Exam v1.1 | 77.4% | 69.0% |
| OSWorld-2.1 (offline subset) | 81.5% | 74.7% |
| AutomationBench (Zapier) | 53.9% | 51.3% |
| Chartography | 72.5% | 71.6% |
| GraphWalks (256k to 1M, BFS, F1) | 96.2% | 84.2% |
| RiemannBench | 80.0% | 76.0% |
| Terminal-Bench-Science 0.1 (AA) | 64.8% | 52.9% |
How to Work With Gemini 4 Carbon
Four choices shape quality, latency and token use.
- 1
Step 1: Use Carbon for the Hardest Tasks
Use Carbon for complex root-cause debugging, architectural design, dense multimodal synthesis and scientific proofs. Gemini 4 Argon gives faster responses and lower token spend per turn on interactive coding and day-to-day feature work. - 2
Step 2: Choose the Lowest Thinking Level That Meets Your Bar
Gemini 4 Carbon has three thinking levels. Low uses the fewest tokens, medium is the default for most tasks, and high suits hard math and science and the most difficult code. On Kie.ai, set reasoning_effort (OpenAI-compatible) or generationConfig.thinkingConfig.thinkingLevel (native). Google advises running evaluations at all three and keeping the lowest level that meets the quality bar. - 3
Step 3: Leave Room in the Output Limit
Thinking tokens and the answer share one output budget, so leave room for the final answer on hard prompts. Google recommends that apps with a small output limit raise it to at least 32,768 tokens; the Kie.ai API docs list no output-limit parameter for this model. - 4
Step 4: Plan for Long Requests
Google notes that hard prompts can take several minutes and recommends background execution or client timeouts of at least 15 minutes. In the OpenAI-compatible API on Kie.ai, set stream to true to receive the answer as server-sent events.
What You Can Build With Gemini 4 Carbon
Google positions Carbon for long-horizon agentic work across software engineering, knowledge work and science. On Kie.ai the model currently supports chat requests only, so the uses below are tasks you run through chat.
Code Investigation and Migration Planning
Work through multi-package migrations, cross-subsystem root-cause investigations and complex architectural redesigns, with the model reasoning over the code you provide.
Document and Data Analysis
Analyze dense PDFs, interpret spreadsheet data and summarize slide content with answers grounded in the sources.
Scientific and Mathematical Reasoning
Work through formal proofs and analyze experimental data in physics, math and the life sciences.
Web and 3D App Code
Google reports that Carbon builds web apps by running build-and-verify loops and checking both its code and the rendered UI. Through chat on Kie.ai, you can use it to write and review that code.
Gemini 4 Carbon on Kie.ai
Gemini 4 Carbon API Quick Start
Send a POST request to https://api.kie.ai/gemini-4-carbon-openai/v1/chat/completions with your messages, or use the native Gemini format at https://api.kie.ai/gemini/v1/models/gemini-4-carbon:streamGenerateContent, with an Authorization: Bearer YOUR_API_KEY header. Set reasoning_effort or generationConfig.thinkingConfig.thinkingLevel to low, medium or high. On Kie.ai the model currently supports chat requests only and has not yet been adapted for agent use. Connecting it to an agent may cause request errors or unexpected results.
Chat Models on Kie.ai Today
Browse the chat models available on Kie.ai today, including Gemini 4 Argon.
One Key Across the Kie.ai Market
All models in the Kie.ai market use the same authentication method: an Authorization: Bearer YOUR_API_KEY header with the key from your Kie.ai API Key Management page.
One Credit Balance
Your account has one credit balance that all generation services draw from, and you can check it with the Get Remaining Credits API.
Frequently Asked Questions
What kind of model is Gemini 4 Carbon?
Gemini 4 Carbon is a reasoning model from Google, based on Gemini 4 Argon. It is built for sustained agentic workflows across complex software engineering, multimodal knowledge work and scientific reasoning.
How large is the Gemini 4 Carbon context window?
The context window is 1,048,576 tokens. Input, thinking and output tokens all share that one window.
How long can a Gemini 4 Carbon response be?
Google's documentation gives a standard synchronous response a budget of 65,536 tokens for thinking and answer text combined, and long decode raises it to as much as 1M tokens minus the input. The Kie.ai API docs list no output-limit parameter for this model.
What inputs does Gemini 4 Carbon accept?
It accepts text, images, video, audio and PDF files and returns text. It does not generate images or audio.
What are the thinking levels in Gemini 4 Carbon?
There are three: low, medium and high. Medium is the default; low uses the fewest tokens with the lowest latency, and high is for complex reasoning and hard math and science. On Kie.ai, set reasoning_effort in the OpenAI-compatible API or generationConfig.thinkingConfig.thinkingLevel in the native API.
Why does Gemini 4 Carbon use more thinking tokens?
Carbon is built for tasks that push the limits of reasoning depth. It spends more thinking tokens per turn than Gemini 4 Argon to explore thoroughly and verify its work, which increases per-turn token use and latency compared to Argon.
Does Gemini 4 Carbon support temperature settings?
Google's documentation says custom temperature, top_p and top_k values are ignored, and the Kie.ai API docs list no such fields. Reasoning depth is controlled with the thinking level instead.
How do I call the Gemini 4 Carbon API on Kie.ai?
Create a key on the Kie.ai API Key Management page and send it in an Authorization: Bearer YOUR_API_KEY header. Send a POST request to /gemini-4-carbon-openai/v1/chat/completions for the OpenAI-compatible format or /gemini/v1/models/gemini-4-carbon:streamGenerateContent for the native format; with streaming, the last chunk carries usage data and credits_consumed. The model currently supports chat requests only and has not yet been adapted for agent use. Connecting it to an agent may cause request errors or unexpected results. See the API documentation.