Head-to-Head Model Comparison

Qwen3.5 9B vs Qwen3.5 35B-A3B

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.

Qwen3.5 9BProfile

Qwen3.5 35B-A3BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3.5 9B

Modello Base a Pesi Aperti

Qwen3.5 9B 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 262K, velocità di 165 token/s, indice di intelligenza di 70/100 e t

Intelligence Index70 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen3.5 35B-A3B

IA Multimodale Nativa

Qwen3.5 35B-A3B di Alibaba è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 262K, velocità di 125 token/s, indice di intelligenza di 77/10

Intelligence Index77 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Qwen3.5 9B is faster at 165 tok/s vs 125 tok/s.

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

Feature	Qwen3.5 9B	Qwen3.5 35B-A3B
Intelligence Index Composite reasoning & knowledge score (0-100)	70	77
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	262.1K tokens~393 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	165 tok/s	125 tok/s
SWE-bench Verified Real-world software engineering	N/A	N/A
GPQA Diamond Graduate-level reasoning	N/A	59%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3.5 9B

+32% faster generation

Context

Tied

262.1K tokens window

Punteggi Benchmark Verificati

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

Qwen3.5 9BQwen3.5 35B-A3B

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs59%

MMLU-Pro (Multi-task Language Understanding)

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

68%vs76.5%

LMSYS Chatbot Arena Elo

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

1230ptsvs1305pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

165 tok/s vs125 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

Qwen3.5 9B0.22s

Qwen3.5 35B-A3B0.34s

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

Qwen3.5 9B3.25s

Qwen3.5 35B-A3B4.34s

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)

Qwen3.5 9B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.5 35B-A3B 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

Qwen3.5 9B · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 8K token (MMLU-Pro 68% · Arena Elo 1230)
Throughput elevato di 165 token/secondo con latenza al primo token (TTFT) di soli 220 ms
Pesi aperti (9B) 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

Qwen3.5 35B-A3B · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 8K token (GPQA Diamond 59% · MMLU-Pro 76.5%)
Throughput elevato di 125 token/secondo con latenza al primo token (TTFT) di soli 340 ms
Pesi aperti (35B total (3B 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	Qwen3.5 9B	Qwen3.5 35B-A3B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	10K	14K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3.5 9B	Qwen3.5 35B-A3B
Official API Model ID (SDK)	qwen3-5-9b-instruct	qwen3-5-35b-a3b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2026-02
Knowledge Cutoff	December 2025	December 2025
Architecture	Compact Dense Autoregressive Transformer	Lightweight Multimodal MoE Transformer
Parameters	9B	35B total (3B 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)	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	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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 6 GB VRAM (4-bit Quantized) · 19 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 22 GB VRAM (4-bit Quantized) · 75 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, vision
Output Modalities	text	text

Qwen3.5 9B vs Qwen3.5 35B-A3B: Risposte Rapide (FAQ)

Which is more intelligent, Qwen3.5 9B or Qwen3.5 35B-A3B?

Qwen3.5 35B-A3B is more intelligent. It scores 77 compared with 70 on the composite Intelligence Index.

Which model is faster?

Qwen3.5 9B is faster, generating 165 tokens per second compared with 125 tokens per second.

Which model has lower latency?

Qwen3.5 9B has lower latency, with a time to first token of 0.22s compared with 0.34s.

Verdetto

Sia Qwen3.5 9B (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 Qwen3.5 35B-A3B (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Qwen3.5 35B-A3B guida nei benchmark di ragionamento e programmazione, Qwen3.5 9B garantisce una generazione più rapida e Qwen3.5 9B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Qwen3.5 9B o Qwen3.5 35B-A3B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Qwen3.5 35B-A3B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Qwen3.5 9B e Qwen3.5 35B-A3B?+

Qwen3.5 9B costa $0.00 / $0.00 per milione di token di input/output, mentre Qwen3.5 35B-A3B costa $0.00 / $0.00. Nel costo combinato, Qwen3.5 9B risulta più economico.

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

Qwen3.5 9B supporta 262.1K token di contesto contro 262.1K token di Qwen3.5 35B-A3B, mentre Qwen3.5 9B 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)

Qwen3.5 9B vs Qwen3.5 35B-A3B

Cambia e Confronta Qualsiasi Modello

Qwen3.5 9B

Qwen3.5 35B-A3B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Qwen3.5 9B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.5 35B-A3B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Qwen3.5 9B vs Qwen3.5 35B-A3B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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