Head-to-Head Model Comparison

Gemini 2.0 Flash vs Gemini 1.5 Flash

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 2.0 FlashProfile

Gemini 1.5 FlashProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 2.0 Flash

Inferencia Ultrarrápida

Gemini 2.0 Flash 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 40/100 y tarifa de $

Intelligence Index40 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.40 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

42%

Google

Gemini 1.5 Flash

Inferencia Ultrarrápida

Gemini 1.5 Flash 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 180 tokens/s, índice de inteligencia de 38/100 y tarifa de $

Intelligence Index38 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.30 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

20%

Diferencias Clave de un Vistazo

Gemini 1.5 Flash is 1.3x cheaper for API usage (input + output tokens combined).
Gemini 2.0 Flash is faster at 240 tok/s vs 180 tok/s.
Gemini 2.0 Flash leads on SWE-bench coding (42.0% vs 20.0%).

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

Característica	Gemini 2.0 Flash	Gemini 1.5 Flash
Intelligence Index Composite reasoning & knowledge score (0-100)	40	38
Provider	Google	Google
Input Price Cost per million input tokens	$0.10 / 1M	$0.07 / 1M
Output Price Cost per million output tokens	$0.40 / 1M	$0.30 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.08 / 1M	$0.06 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,573 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	240 tok/s	180 tok/s
SWE-bench Verified Real-world software engineering	42%	20%
GPQA Diamond Graduate-level reasoning	55.4%	42.1%
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.02 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Gemini 2.0 Flash

+22.0% SWE-bench lead

Costo

Gemini 1.5 Flash

1.3x cheaper monthly runtime

Velocidad

Gemini 2.0 Flash

+33% faster generation

Contexto

Tied

1.05M tokens window

Puntuaciones de Benchmarks Verificadas

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

Gemini 2.0 FlashGemini 1.5 Flash

SWE-bench Verified (Autonomous Coding)

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

42%vs20%

GPQA Diamond (Doctorate-Level Science & Math)

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

55.4%vs42.1%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs69.8%

LMSYS Chatbot Arena Elo

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

1302ptsvs1228pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

8.9%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

27.7%vsN/A

SciCode (Scientific Computing)

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

25.2%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

44.9%vsN/A

AIME (Advanced Math Competition)

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

63.7%vsN/A

Long-Context Reasoning

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

63.9%vs82.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs180 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 2.0 Flash0.28s

Gemini 1.5 Flash0.32s

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

Gemini 2.0 Flash2.36s

Gemini 1.5 Flash3.10s

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 2.0 Flash Monthly API Bill

$0.55 / month

$0.1/1M in · $0.4/1M out

Gemini 1.5 Flash Monthly API Bill

$0.41 / month

$0.075/1M in · $0.3/1M out

Gemini 1.5 Flash saves approximately $0.14 / month (25%) under this workload.1.3x Savings

Gemini 2.0 Flash · Strengths & Key Advantages

Ventana de contexto de 1.0M con hasta 8K tokens de salida máxima (SWE-bench Verified 42% · GPQA Diamond 55.4%)
Rendimiento ágil de 240 tokens/segundo con latencia inicial (TTFT) de solo 280 ms
Economía de API competitiva ($0.1/$0.4 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

Gemini 1.5 Flash · Strengths & Key Advantages

Ventana de contexto de 1.0M con hasta 8K tokens de salida máxima (SWE-bench Verified 20% · GPQA Diamond 42.1%)
Rendimiento ágil de 180 tokens/segundo con latencia inicial (TTFT) de solo 320 ms
Economía de API competitiva ($0.075/$0.3 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

Token Use & Task Economics

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

Metric	Gemini 2.0 Flash	Gemini 1.5 Flash
Cost per Task Estimated USD to finish one task	$0.13	$0.20
Output Tokens / Task Tokens generated per task	60K	24K
Reasoning Tokens / Task Thinking tokens burned per task	33K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Gemini 2.0 Flash vs Gemini 1.5 Flash: Respuestas Rápidas (FAQ)

Which is more intelligent, Gemini 2.0 Flash or Gemini 1.5 Flash?

Gemini 2.0 Flash is more intelligent. It scores 40 compared with 38 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Flash is faster, generating 240 tokens per second compared with 180 tokens per second.

Which model is cheaper to run?

Gemini 1.5 Flash is cheaper at $0.06 per 1M tokens blended, compared with $0.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 2.0 Flash has lower latency, with a time to first token of 0.28s compared with 0.32s.

Veredicto

Tanto Gemini 2.0 Flash (Ideal para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, sistemas RAG empresariales y automatización mediante API.) como Gemini 1.5 Flash (Ideal para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Gemini 2.0 Flash lidera en inteligencia general y programación, Gemini 2.0 Flash ofrece mayor velocidad de generación de tokens y Gemini 1.5 Flash resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Gemini 2.0 Flash o Gemini 1.5 Flash?+

En las pruebas de inteligencia compuesta y resolución de código, Gemini 2.0 Flash obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Gemini 2.0 Flash frente a Gemini 1.5 Flash?+

Gemini 2.0 Flash cuesta $0.10 / $0.40 por millón de tokens de entrada/salida, mientras que Gemini 1.5 Flash cuesta $0.07 / $0.30. En costo combinado, Gemini 1.5 Flash es la opción más económica.

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

Gemini 2.0 Flash cuenta con 1.05M tokens de contexto frente a 1.05M tokens de Gemini 1.5 Flash. En velocidad de inferencia, Gemini 2.0 Flash 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)

Característica	Gemini 2.0 Flash	Gemini 1.5 Flash
Official API Model ID (SDK)	models/gemini-2-flash	models/gemini-1-5-flash
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2024-12	2024-05
Knowledge Cutoff	August 2024	May 2024
Architecture	Multimodal Flash MoE	Distilled Sparse MoE Multimodal
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.025/1M with configurable TTL	Implicit + Explicit Context Caching at $0.01875/1M with configurable TTL
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	Vertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
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	Google DeepMind Frontier Safety Framework 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, audio, video	text, vision, audio, video
Output Modalities	text	text

Gemini 2.0 Flash vs Gemini 1.5 Flash

Cambia y Compara Cualquier Modelo

Gemini 2.0 Flash

Gemini 1.5 Flash

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Gemini 2.0 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 1.5 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Gemini 2.0 Flash vs Gemini 1.5 Flash: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

Related reading