Head-to-Head Model Comparison

Llama 3.1 8B vs Llama 3.1 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

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Llama 3.1 8BProfile

Llama 3.1 70BProfile

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Popular Matchups:

Llama 3.1 8B

Modello Base a Pesi Aperti

Llama 3.1 8B 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 160 token/s, indice di intelligenza di 60/100 e ta

Intelligence Index60 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22%

Llama 3.1 70B

Modello Base a Pesi Aperti

Llama 3.1 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 75 token/s, indice di intelligenza di 75/100 e ta

Intelligence Index75 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

42%

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Llama 3.1 8B is faster at 160 tok/s vs 75 tok/s.
Llama 3.1 70B leads on SWE-bench coding (42.0% vs 22.0%).

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

Feature	Llama 3.1 8B	Llama 3.1 70B
Intelligence Index Composite reasoning & knowledge score (0-100)	60	75
Provider	Meta	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	$0.00 / 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	8K	8K
Throughput Speed Tokens generated per second	160 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	22%	42%
GPQA Diamond Graduate-level reasoning	38%	52%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Llama 3.1 70B

+20.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Llama 3.1 8B

+113% faster generation

Context

Tied

128K tokens window

Punteggi Benchmark Verificati

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

Llama 3.1 8BLlama 3.1 70B

SWE-bench Verified (Autonomous Coding)

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

22%vs42%

GPQA Diamond (Doctorate-Level Science & Math)

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

38%vs52%

MMLU-Pro (Multi-task Language Understanding)

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

65%vs79.5%

LMSYS Chatbot Arena Elo

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

1245ptsvs1330pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs75 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 3.1 8B0.22s

Llama 3.1 70B0.40s

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

Llama 3.1 8B3.35s

Llama 3.1 70B7.07s

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 3.1 8B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 3.1 70B 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

Llama 3.1 8B · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 22% · GPQA Diamond 38%)
Throughput elevato di 160 token/secondo con latenza al primo token (TTFT) di soli 220 ms
Pesi aperti (8B) 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 3.1 70B · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 42% · GPQA Diamond 52%)
Throughput elevato di 75 token/secondo con latenza al primo token (TTFT) di soli 400 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	Llama 3.1 8B	Llama 3.1 70B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	4K	6K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.1 8B	Llama 3.1 70B
Official API Model ID (SDK)	meta-llama/Llama-3.1-8B	meta-llama/Llama-3.1-70B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-07	2024-07
Knowledge Cutoff	December 2023	December 2023
Architecture	Dense auto-regressive multilingual transformer	High-capacity dense multilingual transformer
Parameters	8B	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	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	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)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 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	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	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	text
Output Modalities	text	text

Llama 3.1 8B vs Llama 3.1 70B: Risposte Rapide (FAQ)

Which is more intelligent, Llama 3.1 8B or Llama 3.1 70B?

Llama 3.1 70B is more intelligent. It scores 75 compared with 60 on the composite Intelligence Index.

Which model is faster?

Llama 3.1 8B is faster, generating 160 tokens per second compared with 75 tokens per second.

Which model has lower latency?

Llama 3.1 8B has lower latency, with a time to first token of 0.22s compared with 0.40s.

Verdetto

Sia Llama 3.1 8B (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 Llama 3.1 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: Llama 3.1 70B guida nei benchmark di ragionamento e programmazione, Llama 3.1 8B garantisce una generazione più rapida e Llama 3.1 8B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Llama 3.1 8B o Llama 3.1 70B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Llama 3.1 70B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Llama 3.1 8B e Llama 3.1 70B?+

Llama 3.1 8B costa $0.00 / $0.00 per milione di token di input/output, mentre Llama 3.1 70B costa $0.00 / $0.00. Nel costo combinato, Llama 3.1 8B risulta più economico.

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

Llama 3.1 8B supporta 128K token di contesto contro 128K token di Llama 3.1 70B, mentre Llama 3.1 8B 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 3.1 8B vs Llama 3.1 70B

Cambia e Confronta Qualsiasi Modello

Llama 3.1 8B

Llama 3.1 70B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Llama 3.1 8B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.1 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Llama 3.1 8B vs Llama 3.1 70B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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