Head-to-Head Model Comparison

Qwen3 32B vs Qwen3 235B-A22B

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.

Qwen3 32BProfile

Qwen3 235B-A22BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3 32B

Modello Base a Pesi Aperti

Qwen3 32B 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 78/100 e tar

Intelligence Index78 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40%

Alibaba

Qwen3 235B-A22B

Modello Base a Pesi Aperti

Qwen3 235B-A22B 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 68 token/s, indice di intelligenza di 83/100

Intelligence Index83 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

48.5%

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Qwen3 32B is faster at 85 tok/s vs 68 tok/s.
Qwen3 235B-A22B leads on SWE-bench coding (48.5% vs 40.0%).

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

Feature	Qwen3 32B	Qwen3 235B-A22B
Intelligence Index Composite reasoning & knowledge score (0-100)	78	83
Provider	Alibaba	Alibaba
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	131.1K tokens~197 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	85 tok/s	68 tok/s
SWE-bench Verified Real-world software engineering	40%	48.5%
GPQA Diamond Graduate-level reasoning	59%	67%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Qwen3 235B-A22B

+8.5% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3 32B

+25% faster generation

Context

Tied

131.1K tokens window

Punteggi Benchmark Verificati

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

Qwen3 32BQwen3 235B-A22B

SWE-bench Verified (Autonomous Coding)

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

40%vs48.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

59%vs67%

MMLU-Pro (Multi-task Language Understanding)

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

76%vs81.5%

LMSYS Chatbot Arena Elo

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

1310ptsvs1365pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs68 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

Qwen3 32B0.44s

Qwen3 235B-A22B0.62s

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

Qwen3 32B6.32s

Qwen3 235B-A22B7.97s

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)

Qwen3 32B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3 235B-A22B 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

Qwen3 32B · Strengths & Key Advantages

Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 40% · GPQA Diamond 59%)
Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 440 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

Qwen3 235B-A22B · Strengths & Key Advantages

Finestra di contesto da 131K con capacità massima di output fino a 16K token (SWE-bench Verified 48.5% · GPQA Diamond 67%)
Throughput elevato di 68 token/secondo con latenza al primo token (TTFT) di soli 620 ms
Pesi aperti (235B total (22B MoE)) 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	Qwen3 32B	Qwen3 235B-A22B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	16K	22K
Reasoning Tokens / Task Thinking tokens burned per task	4K	8K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3 32B	Qwen3 235B-A22B
Official API Model ID (SDK)	qwen3-32b-instruct	qwen3-235b-a22b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-04	2025-04
Knowledge Cutoff	February 2025	February 2025
Architecture	Dense Autoregressive Transformer	Sparse Mixture-of-Experts Autoregressive Transformer
Parameters	32B	235B total (22B active MoE)
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) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 146 GB VRAM (4-bit Quantized) · 505 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

Qwen3 32B vs Qwen3 235B-A22B: Risposte Rapide (FAQ)

Which is more intelligent, Qwen3 32B or Qwen3 235B-A22B?

Qwen3 235B-A22B is more intelligent. It scores 83 compared with 78 on the composite Intelligence Index.

Which model is faster?

Qwen3 32B is faster, generating 85 tokens per second compared with 68 tokens per second.

Which model has lower latency?

Qwen3 32B has lower latency, with a time to first token of 0.44s compared with 0.62s.

Verdetto

Sia Qwen3 32B (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 Qwen3 235B-A22B (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: Qwen3 235B-A22B guida nei benchmark di ragionamento e programmazione, Qwen3 32B garantisce una generazione più rapida e Qwen3 32B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Qwen3 32B o Qwen3 235B-A22B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Qwen3 235B-A22B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Qwen3 32B e Qwen3 235B-A22B?+

Qwen3 32B costa $0.00 / $0.00 per milione di token di input/output, mentre Qwen3 235B-A22B costa $0.00 / $0.00. Nel costo combinato, Qwen3 32B risulta più economico.

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

Qwen3 32B supporta 131.1K token di contesto contro 131.1K token di Qwen3 235B-A22B, mentre Qwen3 32B 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)

Qwen3 32B vs Qwen3 235B-A22B

Cambia e Confronta Qualsiasi Modello

Qwen3 32B

Qwen3 235B-A22B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Qwen3 32B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3 235B-A22B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Qwen3 32B vs Qwen3 235B-A22B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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