Head-to-Head Model Comparison

Qwen3 235B-A22B vs QwQ 32B Preview

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Qwen3 235B-A22BProfile

QwQ 32B PreviewProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3 235B-A22B

Model Dasar Bobot Terbuka

Qwen3 235B-A22B dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 68 token/detik, indeks kecerdasan 83/100, serta akses gra

Intelligence Index83 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

48.5%

Alibaba

QwQ 32B Preview

Penalaran Bobot Terbuka

QwQ 32B Preview dari Alibaba adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 33K, kecepatan 52 token/detik, indeks kecerdasan 47/100, sert

Intelligence Index47 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

54.2%

Perbedaan Utama Sekilas

Qwen3 235B-A22B is 1.0x cheaper for API usage (input + output tokens combined).
Qwen3 235B-A22B has a larger context window (131K vs 33K tokens).
Qwen3 235B-A22B is faster at 68 tok/s vs 52 tok/s.
QwQ 32B Preview leads on SWE-bench coding (54.2% vs 48.5%).

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

Feature	Qwen3 235B-A22B	QwQ 32B Preview
Intelligence Index Composite reasoning & knowledge score (0-100)	83	47
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.20 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.14 / 1M
Context Window Maximum input + output tokens	131.1K tokens~197 A4 pages of text	32.8K tokens~49 A4 pages of text
Max Output Tokens Per single request	16K	8K
Throughput Speed Tokens generated per second	68 tok/s	52 tok/s
SWE-bench Verified Real-world software engineering	48.5%	54.2%
GPQA Diamond Graduate-level reasoning	67%	65.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.05 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

QwQ 32B Preview

+5.7% SWE-bench lead

Cost

Qwen3 235B-A22B

1.0x cheaper monthly runtime

Speed

Qwen3 235B-A22B

+31% faster generation

Context

Qwen3 235B-A22B

131.1K tokens window

Skor Benchmark Terverifikasi

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

Qwen3 235B-A22BQwQ 32B Preview

SWE-bench Verified (Autonomous Coding)

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

48.5%vs54.2%

GPQA Diamond (Doctorate-Level Science & Math)

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

67%vs65.2%

MMLU-Pro (Multi-task Language Understanding)

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

81.5%vs74.8%

LMSYS Chatbot Arena Elo

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

1365ptsvs1312pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs9.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs29.5%

SciCode (Scientific Computing)

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

N/Avs36.2%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs58.4%

AIME (Advanced Math Competition)

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

N/Avs79.7%

Long-Context Reasoning

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

N/Avs60.1%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs52 tok/s

Latensi & Waktu Respons

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 235B-A22B0.62s

QwQ 32B Preview0.82s

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

Qwen3 235B-A22B7.97s

QwQ 32B Preview10.44s

Simulator Biaya Token Interaktif

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 235B-A22B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

QwQ 32B Preview Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

Qwen3 235B-A22B saves approximately $0.92 / month (100%) under this workload.1.0x Savings

Qwen3 235B-A22B · Strengths & Key Advantages

Jendela konteks 131K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 48.5% · GPQA Diamond 67%)
Kecepatan generasi tinggi 68 token/detik dengan latensi token pertama (TTFT) hanya 620 ms
Bobot terbuka (235B total (22B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

QwQ 32B Preview · Strengths & Key Advantages

Jendela konteks 33K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 54.2% · GPQA Diamond 65.2%)
Kecepatan generasi tinggi 52 token/detik dengan latensi token pertama (TTFT) hanya 820 ms
Bobot terbuka (32B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

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

Metric	Qwen3 235B-A22B	QwQ 32B Preview
Cost per Task Estimated USD to finish one task	$0.00	$0.45
Output Tokens / Task Tokens generated per task	22K	22K
Reasoning Tokens / Task Thinking tokens burned per task	8K	18K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3 235B-A22B	QwQ 32B Preview
Official API Model ID (SDK)	qwen3-235b-a22b-instruct	qwq-32b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-04	2024-11
Knowledge Cutoff	February 2025	September 2024
Architecture	Sparse Mixture-of-Experts Autoregressive Transformer	Reinforcement Learning Reasoning Transformer
Parameters	235B total (22B active MoE)	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 ($0.2/1M in) across full 33K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.05/1M 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
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) · 146 GB VRAM (4-bit Quantized) · 505 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

Qwen3 235B-A22B vs QwQ 32B Preview: Jawaban Cepat (FAQ)

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

Qwen3 235B-A22B is more intelligent. It scores 83 compared with 47 on the composite Intelligence Index.

Which model is faster?

Qwen3 235B-A22B is faster, generating 68 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

Qwen3 235B-A22B is cheaper at $0.00 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3 235B-A22B has lower latency, with a time to first token of 0.62s compared with 0.82s.

Which model has a larger context window?

Qwen3 235B-A22B has a larger context window, supporting 131.1K tokens compared with 32.8K tokens.

Kesimpulan

Baik Qwen3 235B-A22B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun QwQ 32B Preview (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3 235B-A22B unggul dalam penalaran dan pemrograman, Qwen3 235B-A22B memberikan kecepatan generasi token lebih tinggi, dan Qwen3 235B-A22B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Qwen3 235B-A22B atau QwQ 32B Preview?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3 235B-A22B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Qwen3 235B-A22B vs QwQ 32B Preview?+

Qwen3 235B-A22B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan QwQ 32B Preview sebesar $0.20 / $0.60. Secara keseluruhan, Qwen3 235B-A22B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Qwen3 235B-A22B mendukung konteks 131.1K token dibandingkan 32.8K token pada QwQ 32B Preview, sementara Qwen3 235B-A22B unggul dalam kecepatan output token per detik.

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 235B-A22B vs QwQ 32B Preview

Ganti & Bandingkan Model Apa Saja

Qwen3 235B-A22B

QwQ 32B Preview

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Qwen3 235B-A22B · Strengths & Key Advantages

Considerations & Trade-offs

QwQ 32B Preview · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Qwen3 235B-A22B vs QwQ 32B Preview: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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