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Gemini 4 Argon

Try Gemini 4 Argon, Google's first Gemini 4 model, in the browser or through an OpenAI-compatible API. Up to 1M output tokens and #1 on the Vals Index.

EntradaOficial $2.00 por 1 M de tokensAIReiter $0.60 por 1 M de tokensSalidaOficial $10.00 por 1 M de tokensAIReiter $3.00 por 1 M de tokensLectura de cachéOficial $0.10 por 1 M de tokensAIReiter $0.03 por 1 M de tokensCreación de cachéAIReiter $0.00 por 1 M de tokens
Ejecutar con API

ENTRADA

SALIDA

Example
Generated in
42.7 seconds
Token de entrada
134
Token de salida
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Detalles del modelo

Usa la misma clave de modelo en Playground, solicitudes API y flujos de trabajo internos.

ID del modelo
gemini-4-argon
Proveedor
Google
Protocolo
OpenAI Chat Completions
Ventana de contexto
1,048,576 tokens
Salida máxima
-
Token de entrada
60 créditos / 1 M de tokens
Token de salida
300 créditos / 1 M de tokens
Lectura de caché
3 créditos / 1 M de tokens
Escritura de caché
0 créditos / 1 M de tokens

What Gemini 4 Argon is good at

Google built Argon for long, multi-step professional work. Figures below are reported by Google unless an independent leaderboard is named.

Long-horizon software engineering

Argon scores 77.9% on DeepSWE v1.1, a benchmark of real engineering tasks that take many steps to finish, and Google reports it as the state of the art. Use it for debugging across a large repository, refactors and code migrations.

Finance, legal and business work

Argon is #1 on the Vals Index, an independent leaderboard that weights finance, coding, legal and tax tasks by their share of U.S. GDP. It also ranks first on Zapier's AutomationBench with 51.3%.

Finding and fixing vulnerabilities

Google trained Argon to find, validate and patch software vulnerabilities. It ties for first on CWE-bench v1 with 68%. Point it at code you own and ask it to locate a flaw, explain how it is reached and write the fix.

Charts, documents and long video

Argon reads professional charts, works across a set of documents and picks out details from long videos. It scores 91.7% on LVBench, a long-video understanding benchmark, the highest published result.

One response can run to a million tokens

Earlier Gemini models stopped at 64K output tokens. Argon raises the limit to 1M, so a single response can hold a full report, a long translation or a multi-file change.

Finish long jobs in one pass

Generate a complete document, a whole module or a book-length translation without stitching several responses together and losing context at the seams.

Room to think

With hundreds of thousands of tokens of headroom, Argon can reason through a hard problem and write the answer in a single trajectory instead of stopping half way.

Set max_tokens on purpose

Argon writes long. On the Artificial Analysis index it produced about 62K output tokens per task, against about 27K for GPT-6 Astra. Keep max_tokens low for chat and short answers, and raise it only for jobs that need the room.

What Argon has already done at Google

Google ran Argon on its own engineering work before release. These are the results Google has published.

Rewriting C and C++ in Rust

Argon agents are migrating Google codebases to Rust, from libraries such as re2 up to the 800K-line Fuchsia Zircon kernel. In the libgav1 video decoder they replaced 32K lines of SIMD code, and the result runs 2.7x faster than the earlier Rust port with identical output.

Saving data-center memory

A team of Argon agents read fleet-wide profiling data and applied memory optimizations across Google's data centers, freeing more than 300 TiB.

Quantum algorithm design

Argon helped Google's quantum researchers reduce the qubit and gate cost of key subroutines, beating a published baseline by 40% within minutes.

A vulnerability others missed

Wiz used Argon to uncover a critical flaw in healthcare software used by hospitals worldwide that exposed personal data, a risk earlier frontier models had not found.

Gemini 4 Argon vs Claude Opus 5.5 vs GPT-6 Astra

Three frontier models released within weeks of each other. All three run on AIReiter with the same API key.

Gemini 4 ArgonClaude Opus 5.5GPT-6 Astra
DeveloperGoogleAnthropicOpenAI
Vals Index v2.168.9% (#1)67.0%63.1%
Max output per response1M tokens128K tokens128K tokens
Context window1M tokens1M tokens1.05M tokens
Model id on AIReitergemini-4-argonclaude-opus-5-5gpt-6-astra

Vals Index scores are from the Vals AI leaderboard as of September 30, 2026. The index weights finance, coding, legal and tax work by economic impact. Argon's lead is clearest when a task needs very long output. On short tasks, send the same prompt to each model and compare the answers.

How to call the Gemini 4 Argon API

AIReiter serves Argon over the OpenAI Chat Completions protocol. An existing OpenAI client needs two changes.

01

Repoint the client

Set the base URL to https://aireiter.com/api/v1 and use your AIReiter API key. The official OpenAI SDKs work once the base URL is changed.

02

Send the request

POST https://aireiter.com/api/v1/chat/completions { "model": "gemini-4-argon", "max_tokens": 4096, "messages": [{"role": "user", "content": "Hello"}] }

03

Size the output

Keep max_tokens modest for chat. Raise it for long reports, migrations and translations, where the 1M output limit is the reason to choose Argon.

Gemini 4 Argon API FAQ

/ 01

Which model id do I send?

gemini-4-argon, the same id Google uses in the Gemini API.

/ 02

How is Argon different from Gemini 3.x?

Argon is the first Gemini 4 model and the first Gemini model named with a codename instead of a Pro or Flash tier. The biggest practical change is output length: up to 1M tokens per response, up from 64K.

/ 03

Do I need a Google account or a Gemini API key?

No. Use your AIReiter key with the AIReiter endpoint. To try the model before writing any code, use the playground on this page.

/ 04

Are the benchmark numbers independent?

The Vals Index is an independent leaderboard. DeepSWE, LVBench, CWE-bench and AutomationBench results are reported by Google. Run Argon on your own tasks before you switch a production workload.

/ 05

Should I pick Argon or Claude Opus 5.5?

Pick Argon when a job needs very long output, or understanding of charts and long video. For other work, both models run on one AIReiter key, so send the same real task to each and keep the one that does it better.