Head-to-Head Model Comparison

Gemma 4 26B A4B vs Qwen3.6 27B

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.

Gemma 4 26B A4BProfile

Qwen3.6 27BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemma 4 26B A4B

Modelo Base de Pesos Abiertos

Gemma 4 26B A4B de Google es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 262K, velocidad de 160 tokens/s, índice de inteligencia de 74/100 y

Intelligence Index74 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

61%

Alibaba

Qwen3.6 27B

IA Multimodal Nativa

Qwen3.6 27B de Alibaba es un modelo de IA diseñado para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, con ventana de contexto de 262K, velocidad de 92 tokens/s, índice de inteligencia de 77/100 y tarifa de

Intelligence Index77 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Diferencias Clave de un Vistazo

Both models cost the same under this workload — choose on capability, not price.
Gemma 4 26B A4B is faster at 160 tok/s vs 92 tok/s.

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

Característica	Gemma 4 26B A4B	Qwen3.6 27B
Intelligence Index Composite reasoning & knowledge score (0-100)	74	77
Provider	Google	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	33K	8K
Throughput Speed Tokens generated per second	160 tok/s	92 tok/s
SWE-bench Verified Real-world software engineering	61%	N/A
GPQA Diamond Graduate-level reasoning	71.5%	58%

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Tied

Identical monthly runtime cost

Velocidad

Gemma 4 26B A4B

+74% faster generation

Contexto

Tied

262.1K tokens window

Puntuaciones de Benchmarks Verificadas

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

Gemma 4 26B A4BQwen3.6 27B

SWE-bench Verified (Autonomous Coding)

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

61%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

71.5%vs58%

MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs75.5%

LMSYS Chatbot Arena Elo

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

1365ptsvs1295pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs92 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

Gemma 4 26B A4B0.29s

Qwen3.6 27B0.39s

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

Gemma 4 26B A4B3.42s

Qwen3.6 27B5.82s

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)

Gemma 4 26B A4B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.6 27B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Gemma 4 26B A4B · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 33K tokens de salida máxima (SWE-bench Verified 61% · GPQA Diamond 71.5%)
Rendimiento ágil de 160 tokens/segundo con latencia inicial (TTFT) de solo 290 ms
Pesos abiertos (26B (4B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Qwen3.6 27B · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 8K tokens de salida máxima (GPQA Diamond 58% · MMLU-Pro 75.5%)
Rendimiento ágil de 92 tokens/segundo con latencia inicial (TTFT) de solo 390 ms
Pesos abiertos (27B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
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	Gemma 4 26B A4B	Qwen3.6 27B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	8K	14K
Reasoning Tokens / Task Thinking tokens burned per task	3K	3K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Gemma 4 26B A4B	Qwen3.6 27B
Official API Model ID (SDK)	models/gemma-4-26b-a4b	qwen3-6-27b-instruct
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-04	2026-04
Knowledge Cutoff	January 2026	February 2026
Architecture	Sparse Mixture-of-Experts open transformer (26B total, 4B active)	Refined Dense Multimodal Transformer
Parameters	26B (4B active MoE)	27B
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
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	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 16 GB VRAM (4-bit Quantized) · 56 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 17 GB VRAM (4-bit Quantized) · 58 GB VRAM (Native BF16)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text, vision
Output Modalities	text	text

Gemma 4 26B A4B vs Qwen3.6 27B: Respuestas Rápidas (FAQ)

Which is more intelligent, Gemma 4 26B A4B or Qwen3.6 27B?

Qwen3.6 27B is more intelligent. It scores 77 compared with 74 on the composite Intelligence Index.

Which model is faster?

Gemma 4 26B A4B is faster, generating 160 tokens per second compared with 92 tokens per second.

Which model has lower latency?

Gemma 4 26B A4B has lower latency, with a time to first token of 0.29s compared with 0.39s.

Veredicto

Tanto Gemma 4 26B A4B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Qwen3.6 27B (Ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen3.6 27B lidera en inteligencia general y programación, Gemma 4 26B A4B ofrece mayor velocidad de generación de tokens y Gemma 4 26B A4B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Gemma 4 26B A4B o Qwen3.6 27B?+

En las pruebas de inteligencia compuesta y resolución de código, Qwen3.6 27B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Gemma 4 26B A4B frente a Qwen3.6 27B?+

Gemma 4 26B A4B cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Qwen3.6 27B cuesta $0.00 / $0.00. En costo combinado, Gemma 4 26B A4B es la opción más económica.

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

Gemma 4 26B A4B cuenta con 262.1K tokens de contexto frente a 262.1K tokens de Qwen3.6 27B. En velocidad de inferencia, Gemma 4 26B A4B 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)

Gemma 4 26B A4B vs Qwen3.6 27B

Cambia y Compara Cualquier Modelo

Gemma 4 26B A4B

Qwen3.6 27B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Gemma 4 26B A4B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.6 27B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Gemma 4 26B A4B vs Qwen3.6 27B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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