Qwen3 235B-A22B vs QwQ 32B Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3 235B-A22B by Alibaba and QwQ 32B Preview by Alibaba represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Qwen3 235B-A22B holds a notable pricing advantage for token ingestion. On output generation, Qwen3 235B-A22B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3 235B-A22B provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Qwen3 235B-A22B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Qwen3 235B-A22B
Modèle phare MoE open sourced'Alibaba landmark 235B ouvert MoE modèle de fondation achieving de pointe performances avec 22B actif inférence efficacité.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
48.5%
QwQ 32B Preview
Moteur de raisonnement ouvertModèle de raisonnement spécialisé d'Alibaba utilisant une chaîne de pensée étendue pour égaler OpenAI o1 en mathématiques.
$0.20 / 1M
$0.60 / 1M
32.8K tokens
54.2%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3 235B-A22B · Strengths & Key Advantages
- phare intelligence avec 22B actif inférence vitesse et resource efficacité
- supérieur code architecture compréhension et dépôt-level refactorisation
- Completely à poids ouverts allowing sur-premises d'entreprise air-gapped déploiement
Considerations & Trade-offs
- nécessite clustered GPU infrastructure pour complet unquantized weights
- pensée mode produces lengthy traces qui increase mémoire cache empreinte
QwQ 32B Preview · Strengths & Key Advantages
- Score remarquable de 79.7% au concours de mathématiques AIME
- Disponible sous licence ouverte pour la recherche et l'industrie
- Transparence totale des étapes intermédiaires de réflexion
- Empreinte de 32B accessible sur matériel grand public
Considerations & Trade-offs
- Contexte limité à 32K tokens
- Peut générer un raisonnement verbeux pour des requêtes simples
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (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.
Verdict
Verdict & Model Selection Recommendation: Choose Qwen3 235B-A22B if your priority is Enterprise self-hosted software engineering, private data analysis, and advanced research. Its performance in demanding tasks justifies its operational token cost. Choose QwQ 32B Preview if your workflow prioritizes Olympiad math proofs, algorithmic puzzles, complex logic deductions, and benchmark verification. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Qwen3 235B-A22B compare to QwQ 32B Preview in API token pricing and cost per token?+
Qwen3 235B-A22B costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas QwQ 32B Preview costs $0.20 input and $0.60 output. Overall, Qwen3 235B-A22B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Qwen3 235B-A22B or QwQ 32B Preview?+
Qwen3 235B-A22B supports a context window of 131K tokens (~98k words), while QwQ 32B Preview supports 33K tokens (~25k words). Therefore, Qwen3 235B-A22B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3 235B-A22B or QwQ 32B Preview?+
Qwen3 235B-A22B scores 48.5% on SWE-bench Verified while QwQ 32B Preview scores 54.2%. For autonomous software engineering and multi-file debugging, QwQ 32B Preview holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3 235B-A22B and QwQ 32B Preview?+
Qwen3 235B-A22B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, QwQ 32B Preview provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3 235B-A22B and QwQ 32B Preview?+
Qwen3 235B-A22B generates approximately 68 tokens per second, while QwQ 32B Preview delivers 52 tokens per second. In latency-sensitive production environments, Qwen3 235B-A22B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3 235B-A22B and QwQ 32B Preview to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3 235B-A22B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to QwQ 32B Preview. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs