Head-to-Head Model Comparison

Llama 3.2 1B vs Qwen 2.5 32B Instruct

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 3.2 1BProfile

Qwen 2.5 32B InstructProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 3.2 1B

Modello Base a Pesi Aperti

Llama 3.2 1B 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 320 token/s, indice di intelligenza di 42/100 e ta

Intelligence Index42 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen 2.5 32B Instruct

Modello Base a Pesi Aperti

Qwen 2.5 32B Instruct di Alibaba 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 131K, velocità di 85 token/s, indice di intelligenza di

Intelligence Index41 / 100

Input Token:

$0.18 / 1M

Output Token:

$0.36 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.8%

Differenze Chiave a Colpo d’Occhio

Llama 3.2 1B is 1.0x cheaper for API usage (input + output tokens combined).
Qwen 2.5 32B Instruct has a larger context window (131K vs 128K tokens).
Llama 3.2 1B is faster at 320 tok/s vs 85 tok/s.

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

Feature	Llama 3.2 1B	Qwen 2.5 32B Instruct
Intelligence Index Composite reasoning & knowledge score (0-100)	42	41
Provider	Meta	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.18 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.36 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.10 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	4K	8K
Throughput Speed Tokens generated per second	320 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	N/A	40.8%
GPQA Diamond Graduate-level reasoning	N/A	48.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.04 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Llama 3.2 1B

1.0x cheaper monthly runtime

Speed

Llama 3.2 1B

+276% faster generation

Context

Qwen 2.5 32B Instruct

131.1K tokens window

Punteggi Benchmark Verificati

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

Llama 3.2 1BQwen 2.5 32B Instruct

SWE-bench Verified (Autonomous Coding)

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

N/Avs40.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs48.5%

MMLU-Pro (Multi-task Language Understanding)

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

46%vs67.2%

LMSYS Chatbot Arena Elo

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

1110ptsvs1255pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs5.6%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs26.2%

SciCode (Scientific Computing)

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

N/Avs26.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs42.1%

AIME (Advanced Math Competition)

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

N/Avs52.3%

Long-Context Reasoning

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

N/Avs64%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

320 tok/s vs85 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 3.2 1B0.12s

Qwen 2.5 32B Instruct0.48s

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

Llama 3.2 1B1.68s

Qwen 2.5 32B Instruct6.36s

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 3.2 1B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen 2.5 32B Instruct Monthly API Bill

$0.65 / month

$0.18/1M in · $0.36/1M out

Llama 3.2 1B saves approximately $0.65 / month (100%) under this workload.1.0x Savings

Llama 3.2 1B · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 4K token (MMLU-Pro 46% · Arena Elo 1110)
Throughput elevato di 320 token/secondo con latenza al primo token (TTFT) di soli 120 ms
Pesi aperti (1B) 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

Qwen 2.5 32B Instruct · Strengths & Key Advantages

Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 40.8% · GPQA Diamond 48.5%)
Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 480 ms
Pesi aperti (32B) 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 3.2 1B	Qwen 2.5 32B Instruct
Cost per Task Estimated USD to finish one task	$0.00	$0.16
Output Tokens / Task Tokens generated per task	2K	28K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.2 1B	Qwen 2.5 32B Instruct
Official API Model ID (SDK)	meta-llama/Llama-3.2-1B	qwen-2-5-32b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-09	2024-09
Knowledge Cutoff	December 2023	June 2024
Architecture	Pruned and distilled ultra-compact transformer	Dense Autoregressive Transformer
Parameters	1B	32B
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)	Flat rate ($0.18/1M in) across full 131K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.036/1M tokens
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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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 3.2 1B vs Qwen 2.5 32B Instruct: Risposte Rapide (FAQ)

Which is more intelligent, Llama 3.2 1B or Qwen 2.5 32B Instruct?

Llama 3.2 1B is more intelligent. It scores 42 compared with 41 on the composite Intelligence Index.

Which model is faster?

Llama 3.2 1B is faster, generating 320 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Llama 3.2 1B is cheaper at $0.00 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.2 1B has lower latency, with a time to first token of 0.12s compared with 0.48s.

Which model has a larger context window?

Qwen 2.5 32B Instruct has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Verdetto

Sia Llama 3.2 1B (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 Qwen 2.5 32B Instruct (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 3.2 1B guida nei benchmark di ragionamento e programmazione, Llama 3.2 1B garantisce una generazione più rapida e Llama 3.2 1B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Llama 3.2 1B o Qwen 2.5 32B Instruct?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Llama 3.2 1B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Llama 3.2 1B e Qwen 2.5 32B Instruct?+

Llama 3.2 1B costa $0.00 / $0.00 per milione di token di input/output, mentre Qwen 2.5 32B Instruct costa $0.18 / $0.36. Nel costo combinato, Llama 3.2 1B risulta più economico.

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

Llama 3.2 1B supporta 128K token di contesto contro 131.1K token di Qwen 2.5 32B Instruct, mentre Llama 3.2 1B 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 3.2 1B vs Qwen 2.5 32B Instruct

Cambia e Confronta Qualsiasi Modello

Llama 3.2 1B

Qwen 2.5 32B Instruct

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Llama 3.2 1B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen 2.5 32B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Llama 3.2 1B vs Qwen 2.5 32B Instruct: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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