Head-to-Head Model Comparison

Gemma 4 31B vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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

Gemma 4 31BProfile

Llama 3.3 70BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemma 4 31B

Razonamiento de Pesos Abiertos

Gemma 4 31B de Google es un modelo de IA diseñado para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, con ventana de contexto de 262K, velocidad de 80 tokens/s, índice de inteligencia de 81/100 y tar

Intelligence Index81 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

68.5%

Llama 3.3 70B

Modelo Base de Pesos Abiertos

Llama 3.3 70B de Meta es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 280 tokens/s, índice de inteligencia de 38/100 y tar

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

Diferencias Clave de un Vistazo

Gemma 4 31B is 1.0x cheaper for API usage (input + output tokens combined).
Gemma 4 31B has a larger context window (262K vs 128K tokens).
Llama 3.3 70B is faster at 280 tok/s vs 80 tok/s.
Gemma 4 31B leads on SWE-bench coding (68.5% vs 39.5%).

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

Característica	Gemma 4 31B	Llama 3.3 70B
Intelligence Index Composite reasoning & knowledge score (0-100)	81	38
Provider	Google	Meta
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	$0.07 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	33K	4K
Throughput Speed Tokens generated per second	80 tok/s	280 tok/s
SWE-bench Verified Real-world software engineering	68.5%	39.5%
GPQA Diamond Graduate-level reasoning	77%	48.9%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.10 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Gemma 4 31B

+29.0% SWE-bench lead

Costo

Gemma 4 31B

1.0x cheaper monthly runtime

Velocidad

Llama 3.3 70B

+250% faster generation

Contexto

Gemma 4 31B

262.1K tokens window

Puntuaciones de Benchmarks Verificadas

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

Gemma 4 31BLlama 3.3 70B

SWE-bench Verified (Autonomous Coding)

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

68.5%vs39.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

77%vs48.9%

MMLU-Pro (Multi-task Language Understanding)

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

84.8%vs71%

LMSYS Chatbot Arena Elo

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

1410ptsvs1285pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs7.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs26.1%

SciCode (Scientific Computing)

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

N/Avs23.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs42.5%

AIME (Advanced Math Competition)

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

86%vs56.2%

Long-Context Reasoning

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

N/Avs63.9%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs280 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 31B0.48s

Llama 3.3 70B0.25s

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

Gemma 4 31B6.73s

Llama 3.3 70B2.04s

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 31B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

Gemma 4 31B saves approximately $0.15 / month (100%) under this workload.1.0x Savings

Gemma 4 31B · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 33K tokens de salida máxima (SWE-bench Verified 68.5% · GPQA Diamond 77%)
Rendimiento ágil de 80 tokens/segundo con latencia inicial (TTFT) de solo 480 ms
Pesos abiertos (31B) 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

Llama 3.3 70B · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 4K tokens de salida máxima (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Rendimiento ágil de 280 tokens/segundo con latencia inicial (TTFT) de solo 250 ms
Pesos abiertos (70B) 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 31B	Llama 3.3 70B
Cost per Task Estimated USD to finish one task	$0.00	$0.21
Output Tokens / Task Tokens generated per task	12K	45K
Reasoning Tokens / Task Thinking tokens burned per task	6K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Gemma 4 31B	Llama 3.3 70B
Official API Model ID (SDK)	models/gemma-4-31b	meta-llama/Llama-3.3-70B
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-04	2024-12
Knowledge Cutoff	January 2026	December 2023
Architecture	Dense open reasoning transformer with native thinking	Dense Autoregressive Transformer
Parameters	31B	70B
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)	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	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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 67 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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
Output Modalities	text	text

Gemma 4 31B vs Llama 3.3 70B: Respuestas Rápidas (FAQ)

Which is more intelligent, Gemma 4 31B or Llama 3.3 70B?

Gemma 4 31B is more intelligent. It scores 81 compared with 38 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 80 tokens per second.

Which model is cheaper to run?

Gemma 4 31B is cheaper at $0.00 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.48s.

Which model has a larger context window?

Gemma 4 31B has a larger context window, supporting 262.1K tokens compared with 128K tokens.

Veredicto

Tanto Gemma 4 31B (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) como Llama 3.3 70B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Gemma 4 31B lidera en inteligencia general y programación, Llama 3.3 70B ofrece mayor velocidad de generación de tokens y Gemma 4 31B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Gemma 4 31B o Llama 3.3 70B?+

En las pruebas de inteligencia compuesta y resolución de código, Gemma 4 31B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Gemma 4 31B frente a Llama 3.3 70B?+

Gemma 4 31B cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Llama 3.3 70B cuesta $0.00 / $0.00. En costo combinado, Gemma 4 31B es la opción más económica.

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

Gemma 4 31B cuenta con 262.1K tokens de contexto frente a 128K tokens de Llama 3.3 70B. En velocidad de inferencia, Llama 3.3 70B 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 31B vs Llama 3.3 70B

Cambia y Compara Cualquier Modelo

Gemma 4 31B

Llama 3.3 70B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Gemma 4 31B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Gemma 4 31B vs Llama 3.3 70B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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