Head-to-Head Model Comparison

Gemini 3.5 Flash-Lite vs Grok 4.6

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

Cambia y Compara Cualquier Modelo

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Gemini 3.5 Flash-LiteProfile

Grok 4.6Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 3.5 Flash-Lite

Inferencia Ultrarrápida

Gemini 3.5 Flash-Lite de Google es un modelo de IA diseñado para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, con ventana de contexto de 1.0M, velocidad de 240 tokens/s, índice de inteligencia de 69/100 y tarifa

Intelligence Index69 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

xAI

Grok 4.6

Modelo Insignia de Frontera

Grok 4.6 de xAI es un modelo de IA diseñado para razonamiento empresarial de frontera, programación avanzada y flujos de producción, con ventana de contexto de 500K, velocidad de 115 tokens/s, índice de inteligencia de 77/100 y tarifa de $2

Intelligence Index77 / 100

Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

500K tokens

SWE-bench Verified:

75.8%

Diferencias Clave de un Vistazo

Gemini 3.5 Flash-Lite is 3.6x cheaper for API usage (input + output tokens combined).
Gemini 3.5 Flash-Lite has a larger context window (1M vs 500K tokens).
Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 115 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	Gemini 3.5 Flash-Lite	Grok 4.6
Intelligence Index Composite reasoning & knowledge score (0-100)	69	77
Provider	Google	xAI
Input Price Cost per million input tokens	$0.40 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$2.00 / 1M	$6.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.35 / 1M	$1.35 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,573 A4 pages of text	500K tokens~750 A4 pages of text
Max Output Tokens Per single request	33K	66K
Throughput Speed Tokens generated per second	240 tok/s	115 tok/s
SWE-bench Verified Real-world software engineering	N/A	75.8%
GPQA Diamond Graduate-level reasoning	N/A	77.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.50 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Gemini 3.5 Flash-Lite

3.6x cheaper monthly runtime

Velocidad

Gemini 3.5 Flash-Lite

+109% faster generation

Contexto

Gemini 3.5 Flash-Lite

1.05M tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Gemini 3.5 Flash-LiteGrok 4.6

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs75.8%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs77.2%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

81.2%vs88%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1375ptsvs1450pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs14.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs51%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs49.2%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs75%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs89.5%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs89%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs115 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Gemini 3.5 Flash-Lite0.29s

Grok 4.60.58s

Est. End-to-End Response (500 tokens)TTFT + generation time

Gemini 3.5 Flash-Lite2.37s

Grok 4.64.93s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Gemini 3.5 Flash-Lite Monthly API Bill

$2.55 / month

$0.4/1M in · $2/1M out

Grok 4.6 Monthly API Bill

$9.15 / month

$2/1M in · $6/1M out

Gemini 3.5 Flash-Lite saves approximately $6.60 / month (72%) under this workload.3.6x Savings

Gemini 3.5 Flash-Lite · Strengths & Key Advantages

Ventana de contexto de 1.0M con hasta 33K tokens de salida máxima (MMLU-Pro 81.2% · Arena Elo 1375)
Rendimiento ágil de 240 tokens/segundo con latencia inicial (TTFT) de solo 290 ms
Economía de API competitiva ($0.4/$2 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Grok 4.6 · Strengths & Key Advantages

Ventana de contexto de 500K con hasta 66K tokens de salida máxima (SWE-bench Verified 75.8% · GPQA Diamond 77.2%)
Rendimiento ágil de 115 tokens/segundo con latencia inicial (TTFT) de solo 580 ms
Economía de API competitiva ($2/$6 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Gemini 3.5 Flash-Lite	Grok 4.6
Cost per Task Estimated USD to finish one task	$0.55	$0.65
Output Tokens / Task Tokens generated per task	18K	45K
Reasoning Tokens / Task Thinking tokens burned per task	7K	22K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Gemini 3.5 Flash-Lite	Grok 4.6
Official API Model ID (SDK)	models/gemini-3-5-flash-lite	grok-4-6-latest
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	xAI REST API (OpenAI-Compatible)
Release Date	2026-07	2026-08
Knowledge Cutoff	March 2026	April 2026
Architecture	Lightweight efficient multimodal transformer	1.5T Colossus Sparse MoE
Parameters	Confidential	1.5T (MoE)
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Flat rate ($2/1M in) across full 500K window
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.1/1M with configurable TTL	Cached input at $0.5/1M tokens
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Real-time Web & X (Twitter) Search Grounding + Code Execution
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	API Prompt Engineering & RAG Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	xAI Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision	text, vision
Output Modalities	text	text

Gemini 3.5 Flash-Lite vs Grok 4.6: Respuestas Rápidas (FAQ)

Which is more intelligent, Gemini 3.5 Flash-Lite or Grok 4.6?

Grok 4.6 is more intelligent. It scores 77 compared with 69 on the composite Intelligence Index.

Which model is faster?

Gemini 3.5 Flash-Lite is faster, generating 240 tokens per second compared with 115 tokens per second.

Which model is cheaper to run?

Gemini 3.5 Flash-Lite is cheaper at $0.35 per 1M tokens blended, compared with $1.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.5 Flash-Lite has lower latency, with a time to first token of 0.29s compared with 0.58s.

Which model has a larger context window?

Gemini 3.5 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 500K tokens.

Veredicto

Tanto Gemini 3.5 Flash-Lite (Ideal para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, sistemas RAG empresariales y automatización mediante API.) como Grok 4.6 (Ideal para razonamiento empresarial de frontera, programación avanzada y flujos de producción, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Grok 4.6 lidera en inteligencia general y programación, Gemini 3.5 Flash-Lite ofrece mayor velocidad de generación de tokens y Gemini 3.5 Flash-Lite resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Gemini 3.5 Flash-Lite o Grok 4.6?+

En las pruebas de inteligencia compuesta y resolución de código, Grok 4.6 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Gemini 3.5 Flash-Lite frente a Grok 4.6?+

Gemini 3.5 Flash-Lite cuesta $0.40 / $2.00 por millón de tokens de entrada/salida, mientras que Grok 4.6 cuesta $2.00 / $6.00. En costo combinado, Gemini 3.5 Flash-Lite es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Gemini 3.5 Flash-Lite cuenta con 1.05M tokens de contexto frente a 500K tokens de Grok 4.6. En velocidad de inferencia, Gemini 3.5 Flash-Lite ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Gemini 3.5 Flash-Lite vs Grok 4.6

Cambia y Compara Cualquier Modelo

Gemini 3.5 Flash-Lite

Grok 4.6

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Gemini 3.5 Flash-Lite · Strengths & Key Advantages

Considerations & Trade-offs

Grok 4.6 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Gemini 3.5 Flash-Lite vs Grok 4.6: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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