Head-to-Head Model Comparison

Llama 3.3 70B vs GPT-4o

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.

Llama 3.3 70BProfile

GPT-4oProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

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%

OpenAI

GPT-4o

Modello Vocale e Audio Nativo

GPT-4o di OpenAI è un modello di IA progettato per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, con finestra di contesto da 128K, velocità di 105 token/s, indice di intelligenza di 41/100 e tariffa di $

Intelligence Index41 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38.8%

Differenze Chiave a Colpo d’Occhio

Llama 3.3 70B is 97.5x cheaper for API usage (input + output tokens combined).
Llama 3.3 70B is faster at 280 tok/s vs 105 tok/s.
Llama 3.3 70B leads on SWE-bench coding (39.5% vs 38.8%).

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

Feature	Llama 3.3 70B	GPT-4o
Intelligence Index Composite reasoning & knowledge score (0-100)	38	41
Provider	Meta	OpenAI
Input Price Cost per million input tokens	$0.00 / 1M	$2.50 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.07 / 1M	$2.38 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	16K
Throughput Speed Tokens generated per second	280 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	39.5%	38.8%
GPQA Diamond Graduate-level reasoning	48.9%	53.6%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$1.25 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Llama 3.3 70B

+0.7% SWE-bench lead

Cost

Llama 3.3 70B

97.5x cheaper monthly runtime

Speed

Llama 3.3 70B

+167% faster generation

Context

Tied

128K tokens window

Punteggi Benchmark Verificati

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

Llama 3.3 70BGPT-4o

SWE-bench Verified (Autonomous Coding)

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

39.5%vs38.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.9%vs53.6%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs72.6%

LMSYS Chatbot Arena Elo

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

1285ptsvs1286pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

7.8%vs4.1%

Terminal-Bench (Agentic Coding & Terminal Use)

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

26.1%vs18.5%

SciCode (Scientific Computing)

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

23.7%vs27.3%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

42.5%vs43.9%

AIME (Advanced Math Competition)

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

56.2%vs29.7%

Long-Context Reasoning

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

63.9%vs58.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

280 tok/s vs105 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

Llama 3.3 70B0.25s

GPT-4o0.41s

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

Llama 3.3 70B2.04s

GPT-4o5.17s

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)

Llama 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

GPT-4o Monthly API Bill

$14.63 / month

$2.5/1M in · $10/1M out

Llama 3.3 70B saves approximately $14.47 / month (99%) under this workload.97.5x Savings

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

GPT-4o · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 16K token (SWE-bench Verified 38.8% · GPQA Diamond 53.6%)
Throughput elevato di 105 token/secondo con latenza al primo token (TTFT) di soli 410 ms
Economia API competitiva ($2.5/$10 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Token Use & Task Economics

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

Metric	Llama 3.3 70B	GPT-4o
Cost per Task Estimated USD to finish one task	$0.21	$0.68
Output Tokens / Task Tokens generated per task	45K	24K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.3 70B	GPT-4o
Official API Model ID (SDK)	meta-llama/Llama-3.3-70B	gpt-4o
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2024-12	2024-05
Knowledge Cutoff	December 2023	October 2023
Architecture	Dense Autoregressive Transformer	Omni-Multimodal Dense
Parameters	70B	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($2.5/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix caching at $1.25/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision, audio
Output Modalities	text	text, audio

Llama 3.3 70B vs GPT-4o: Risposte Rapide (FAQ)

Which is more intelligent, Llama 3.3 70B or GPT-4o?

GPT-4o is more intelligent. It scores 41 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 105 tokens per second.

Which model is cheaper to run?

Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $2.38 (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 0.41s.

Verdetto

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.) sia GPT-4o (Ideale per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GPT-4o guida nei benchmark di ragionamento e programmazione, Llama 3.3 70B garantisce una generazione più rapida e Llama 3.3 70B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

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

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

Qual è la differenza di prezzo API tra Llama 3.3 70B e GPT-4o?+

Llama 3.3 70B costa $0.00 / $0.00 per milione di token di input/output, mentre GPT-4o costa $2.50 / $10.00. Nel costo combinato, Llama 3.3 70B risulta più economico.

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

Llama 3.3 70B supporta 128K token di contesto contro 128K token di GPT-4o, 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)

Llama 3.3 70B vs GPT-4o

Cambia e Confronta Qualsiasi Modello

Llama 3.3 70B

GPT-4o

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4o · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Llama 3.3 70B vs GPT-4o: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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