> ## Documentation Index > Fetch the complete documentation index at: https://docs.apiyi.com/llms.txt > Use this file to discover all available pages before exploring further. # Gemini 4 Argon Is Live on APIYI > Google's first Gemini 4 flagship, gemini-4-argon, is now available on APIYI at the limited-time promotional rate of $2/$10. 400+ pre-launch test calls: same accuracy as 3.1 Pro with about 1/12 of the thinking, and a verified 1M context. ## Key Takeaways * **Available from day one**: Google announced Gemini 4 Argon on September 30 and opened it to the paid tier of the Gemini API on October 13. APIYI is among the first platforms with access. Model name `gemini-4-argon`, in the `default` and `svip` groups, served from official Google capacity * **Limited-time promotional rate**: \$2 input / \$10 output per 1M tokens through November 30, 2026. **From December 1, Google's price goes to \$4 / \$20, and APIYI will adjust at the same time** * **Same accuracy, an order of magnitude less thinking**: on 12 auto-graded tasks run 3 times each, Argon and 3.1 Pro both scored 36/36, but Argon used about 1/12 of the thinking tokens, and median latency dropped from 35.0s to 15.4s * **1M context, verified**: a 1.01M-token input returned normally; the limit is 1,048,576 * **One required migration change**: Argon does not support `thinkingBudget` and passing it may return a 400; use `thinkingLevel` instead ## Background Argon is the first model in the Gemini 4 family. Google is not pitching it as a better chat model but as a model for **long-horizon work**: large codebase refactors, cross-language migrations, finding and patching vulnerabilities, and multi-step knowledge work in fields like law and finance. The rollout came in stages. On September 30 it went to trusted cyber defenders through the Fairwind Program; from October 13 it opened to the Gemini API paid tier, Google AI Ultra subscribers and enterprise customers. The internal case studies in Google's posts are ambitious: helping the Chrome team split a roughly 6-million-line legacy folder out of the 40-million-line Chromium codebase, speeding up Gemma inference on vLLM by 2.85x, and migrating 800K lines of the Fuchsia kernel from C/C++ to Rust. The biggest API change is a **1M-token window shared by input and output**. Earlier Gemini models capped output at 64K; Argon can in principle generate up to "1M minus your input" in one go. ## Detailed Analysis ### Benchmarks The figures below are **published by Google** and have not been independently reproduced: | Benchmark | What it measures | Gemini 4 Argon | | - | - | - | | DeepSWE v1.1 | Real-world long-horizon software engineering | 77.9％ | | AutomationBench (Zapier) | End-to-end business workflow execution | 51.3％ (#1) | | LVBench | Long video understanding | 91.7％ | | CWE-bench v1 | Security vulnerability remediation | 68％ (tied #1) | | ERQA | Embodied / spatial reasoning | 87％ | | Vals Index | Finance / coding / legal / tax | #1 | One outlet, citing Bloomberg, reported that some Google engineers with early access found Argon underwhelming on real front-end work. We could not verify this and include it for context only: **high benchmark scores do not guarantee results on your workload, so test with your own tasks before switching**. ### APIYI Test Results We ran 400+ calls on October 13–14 (UTC+8), using the production `gemini-3.1-pro-preview` and `gemini-3.8-flash` as references. **Quality**: 12 auto-graded tasks (math, date arithmetic, counting, strictly formatted Chinese writing, JSON extraction, and two Python coding tasks graded by running unit tests locally), 3 runs each: | Model | Correct | Median latency | Median thinking tokens | Output + thinking total | | - | - | - | - | - | | **gemini-4-argon** | 36/36 | 15.4s | 115 | 15,797 | | gemini-3.1-pro-preview | 36/36 | 35.0s | 1,566 | 87,029 | | gemini-3.8-flash | 36/36 | 9.4s | 619 | 29,039 | None of these tasks were hard for any of the three models, so accuracy does not separate them. The real difference is **how many tokens each model spent to get there**: Argon used less than a fifth of 3.1 Pro's output plus thinking tokens. **Compatibility**: across 150 paired cases on both protocols, Argon scored 14/14 on the native endpoint and 12/13 on the OpenAI-compatible endpoint, with its one miss matching 3.1 Pro. 3.1 Pro actually lost points in two places: parallel function calling on the OpenAI endpoint (0/2), and a long-context task with an 8.6K-token prefix and a 128-token output cap (0/2, because thinking used up the output budget). **Speed**: writing a roughly 1,300-token article in Chinese, Argon's first token arrived in 2 to 7 seconds versus 27 to 54 seconds for 3.1 Pro (which also timed out once at 10 minutes). All three models generated at 70 to 97 tokens per second; Argon's fast first token comes from doing almost no thinking on writing tasks. ### Key Features and Caveats Defaults to `medium`. Low and medium adapt to difficulty and answer simple questions directly; for reliable deep reasoning, pass `high` explicitly (on the same proof, thinking went from 0 to 1,097 tokens). Google's docs say it is deprecated with a warning; on APIYI we have seen hard errors (400). Remove `thinkingBudget` when migrating from 3.x. The `minimal` level is not supported either. Google says Argon ignores custom `temperature`, `topP` and `topK`. Passing them does not error in our tests, but do not count on them taking effect. Google says implicit caching is on by default. In our tests most repeated same-prefix requests did not hit, and only some did. Budget at the full input price and treat any hit as extra savings. ### Specifications | Parameter | Value | | - | - | | Model name | `gemini-4-argon` | | Context | 1M tokens shared by input and output (measured input limit 1,048,576) | | Default max output | 65,536 tokens | | Input modalities | Text, image, video, audio | | Thinking levels | low / medium (default) / high | | Endpoints | Gemini native `generateContent`, OpenAI-compatible `chat/completions` | Long decode beyond 64K needs the Interactions API, which the APIYI gateway does not support yet, so design around the 65,536-token output limit for now. ## Practical Use ### Recommended Scenarios * **Refactoring, migrating and reviewing large codebases**: Argon's headline use case; pair it with `thinkingLevel: "high"` * **Long document and long video analysis**: cross-checking contracts, financial reports and legal filings in bulk, all within a 1M context * **Agent orchestration**: Google suggests Argon as the lead planner and reviewer, with [Gemini 3.8 Flash](/en/api-capabilities/gemini-3-8-flash/overview) running parallel subagents, to balance quality and cost * **Security audits**: locating vulnerabilities and proposing fixes ### Code Example ```python theme={null} from google import genai from google.genai import types client = genai.Client( api_key="sk-your-apiyi-key", http_options=types.HttpOptions(base_url="https://api.apiyi.com", timeout=1200_000) ) response = client.models.generate_content( model="gemini-4-argon", contents="Review this code for concurrency safety issues and propose a minimal fix: ...", config=types.GenerateContentConfig( thinking_config=types.ThinkingConfig(thinking_level="high") ) ) print(response.text) ``` With the OpenAI SDK, set `base_url` to `https://api.apiyi.com/v1` and use the model name `gemini-4-argon`. More examples are on the [Gemini 4 Argon Overview](/en/api-capabilities/gemini-4-argon/overview). ### Best Practices 1. **Set timeouts to 15 to 20 minutes**: this is Google's recommendation, and during the launch window we also saw occasional queuing of several minutes 2. **Pass `high` explicitly for deep reasoning**: the default level adapts, so do not rely on it for complex tasks 3. **The OpenAI-compatible endpoint is not the place for fine-grained thinking control**: `reasoning_effort` has little visible effect, so use the native endpoint when you need controllable reasoning 4. **Connect agent clients over the chat protocol**: Argon does not support `/v1/responses`, so configure clients such as Codex to use `/v1/chat/completions`. For multi-turn tool calls on the native endpoint, send the previous turn back unchanged, including `thoughtSignature` ## Pricing and Availability ### Pricing | Item | Promotional rate (through 2026-11-30) | From 2026-12-01 | | - | - | - | | Input | \$2 / 1M tokens | \$4 / 1M tokens | | Output (incl. thinking) | \$10 / 1M tokens | \$20 / 1M tokens | | Cache read | \$0.1 / 1M tokens | Follows Google's change | Model prices are aligned with the official site and may change with it; the table above is for reference only, and the **Model Pricing** tab in the top navigation is authoritative: [Model Pricing](/en/models/index). Billing was checked against real charges: across three calls, the amount actually deducted was within 1.5％ of what the unit prices give. Beyond the unit price, **usage** matters more: to get the same task right, Argon spends far fewer output and thinking tokens than 3.1 Pro. During the promotional period its input price is slightly above 3.1 Pro and its output price slightly below, so most tasks end up costing less. When prices double on December 1, re-run your cost estimates. ### Top-up Promotions Model billing matches the official price item by item; discounts come from top-up bonuses. See [Top-up Promotions](/en/faq/recharge-promotions). ## Summary and Recommendations Gemini 4 Argon showed no capability gaps in our tests. Its standout trait is **reaching the same accuracy with much less thinking**, which makes it both faster and cheaper. Teams using 3.1 Pro for complex reasoning, code and long documents should shift some traffic to Argon during the promotional period and compare. When migrating, remove `thinkingBudget`, raise your timeouts, and leave caching out of your cost model. **Sources** (retrieved 2026-10-14): Google blog posts `blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/` (2026-09-30) and `blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/` (2026-10-13); Google AI Studio developer guide `aistudio.google.com/learn/gemini-4-argon-developer-guide`; the engineer feedback comes from Startup Fortune's summary of a Bloomberg report. Test data comes from APIYI's pre-launch testing on October 13–14, 2026 (UTC+8). This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.