Gemini 4 Argon — Specifications
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | Google DeepMind |
|---|---|
| Tipo | Razonamiento / agente |
| Modalidad | Text, vision, video |
| Parámetros | No revelado |
| Salida máxima | 1 millón |
| Licencia | Propietario |
| Pesos abiertos | No |
| Lanzado | 2026-09 |
| Precio de entrada | $2.00 /1M |
| Precio de salida | $10.00 /1M |
| Proveedores de API | Google (limited access: Fairwind Program; paid API customers first) |
Benchmarks
| Google-reported | DeepSWE v1.1 77.9%; AutomationBench 51.3% (#1); LVBench 91.7%; CWE-bench v1 68% (tied first). Prices are introductory; $4 / $20 per 1M afterwards. |
|---|
¿Qué es Gemini 4 Argon?
Gemini 4 Argon is Google DeepMind’s frontier model, announced on 30 September 2026 as the first model of the Gemini 4 generation. According to Google, it is built for long, multi-step work in software engineering, enterprise knowledge work such as law and finance, and cybersecurity defence, and its output limit rises to 1 million tokens from 64,000.
At announcement it was available only to trusted cyber defenders through the Fairwind Program; Google says wider access starts with paid API customers and Google AI Ultra subscribers, with no date given. The introductory API price is $2 per million input tokens and $10 output (cached input 95% off), rising to $4 / $20 after the introductory period. A plain-language guide: Gemini 4 Argon explicado.
Gemini 4 Argon pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $2.00 |
|---|---|
| Salida (por cada millón de tokens) | $10.00 |
| Relación salida/entrada | 5× |
| Combinada (4:1 entrada:salida) | $3.60 por 1 millón de tokens |
What Gemini 4 Argon costs per month
Gasto mensual real con una mezcla entrada:salida de 4:1, es decir, la proporción que realmente genera una carga de trabajo típica de chat o RAG.
| Carga de trabajo | Tokens/mes | Coste mensual |
|---|---|---|
| Proyecto secundario | 1 millón de tokens de entrada / 0,25 millones de tokens de salida | $4.50 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $90 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $900 |
Calcule sus propios números en la Calculadora de costos de API de IA.
Cheaper alternatives to Gemini 4 Argon
| Modelo | Dólares por millón combinados | Usted ahorra |
|---|---|---|
| Mistral 7B abierta | $0.0220 | 99 % más barato |
| Llama 3.1 8B abierta | $0.0220 | 99 % más barato |
| Mistral NeMo 12B abierta | $0.0240 | 99 % más barato |
Preguntas frecuentes
How much does Gemini 4 Argon cost per 1M tokens?
Gemini 4 Argon costs $2.00 per 1M input tokens and $10.00 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $3.60 per 1M tokens.
How much does Gemini 4 Argon cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $90 on Gemini 4 Argon. A side project (1M in / 0.25M out) costs roughly $4.50.
What is a cheaper alternative to Gemini 4 Argon?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 99% less than Gemini 4 Argon. It is also open-weight, so self-hosting is an option.
Can I run Gemini 4 Argon locally?
No. Gemini 4 Argon is a closed, API-only model — the weights are not released, so it cannot be self-hosted.
Why does Gemini 4 Argon charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Gemini 4 Argon charges 5× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Los precios corresponden a las tarifas oficiales publicadas para la API principal del modelo y se revisan periódicamente conforme los proveedores los actualicen. No incluyen descuentos por volumen, procesamiento por lotes ni entradas en caché. Compare todos los modelos uno al lado del otro en la Base de datos de modelos de IA o el Clasificación de modelos de lenguaje grande (LLM).