Head-to-Head Model Comparison

Gemma 4 31B vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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

Ragionamento a Pesi Aperti

Gemma 4 31B di Google è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 262K, velocità di 80 token/s, indice di intelligenza di 81/100 e tariffa di a

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

Modello Base a Pesi Aperti

Llama 3.3 70B di Meta è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 280 token/s, indice di intelligenza di 38/100 e t

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

Differenze Chiave a Colpo d’Occhio

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 →

Feature	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

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Gemma 4 31B

+29.0% SWE-bench lead

Cost

Gemma 4 31B

1.0x cheaper monthly runtime

Speed

Llama 3.3 70B

+250% faster generation

Context

Gemma 4 31B

262.1K tokens window

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 262K con capacità massima di output fino a 33K token (SWE-bench Verified 68.5% · GPQA Diamond 77%)
Throughput elevato di 80 token/secondo con latenza al primo token (TTFT) di soli 480 ms
Pesi aperti (31B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Llama 3.3 70B · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 4K token (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Throughput elevato di 280 token/secondo con latenza al primo token (TTFT) di soli 250 ms
Pesi aperti (70B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	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: Risposte Rapide (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.

Verdetto

Sia Gemma 4 31B (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) sia Llama 3.3 70B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Gemma 4 31B guida nei benchmark di ragionamento e programmazione, Llama 3.3 70B garantisce una generazione più rapida e Gemma 4 31B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Gemma 4 31B o Llama 3.3 70B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Gemma 4 31B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Gemma 4 31B e Llama 3.3 70B?+

Gemma 4 31B costa $0.00 / $0.00 per milione di token di input/output, mentre Llama 3.3 70B costa $0.00 / $0.00. Nel costo combinato, Gemma 4 31B risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Gemma 4 31B supporta 262.1K token di contesto contro 128K token di Llama 3.3 70B, mentre Llama 3.3 70B offre una velocità di generazione superiore.

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 e Confronta Qualsiasi Modello

Gemma 4 31B

Llama 3.3 70B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Gemma 4 31B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Gemma 4 31B vs Llama 3.3 70B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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