Qwen3 32B vs Qwen3 235B-A22B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3 32B by Alibaba and Qwen3 235B-A22B 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 131K tokens. In latency-sensitive production environments, Qwen3 32B 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 32B
オープンウェイトモデルAlibaba が提供する Qwen3 32B は、最大 131K トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
131.1K tokens
40%
Qwen3 235B-A22B
オープンウェイトモデルAlibaba が提供する Qwen3 235B-A22B は、最大 131K トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
131.1K tokens
48.5%
ひと目でわかる主な違い
- 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%).
| 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% |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3 32B · Strengths & Key Advantages
- Alibaba の高度な Dense Autoregressive Transformer アーキテクチャと 131K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Qwen3 235B-A22B · Strengths & Key Advantages
- Alibaba の高度な Sparse Mixture-of-Experts Autoregressive Transformer アーキテクチャと 131K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
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 |
技術仕様・スペック
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: よくある質問 (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.
総合評価
Verdict & Model Selection Recommendation: Choose Qwen3 32B if your priority is Local developer workstations, edge servers, and targeted domain fine-tuning. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3 235B-A22B if your workflow prioritizes Enterprise self-hosted software engineering, private data analysis, and advanced research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Qwen3 32B compare to Qwen3 235B-A22B in API token pricing and cost per token?+
Both Qwen3 32B and Qwen3 235B-A22B are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Qwen3 32B or Qwen3 235B-A22B?+
Both Qwen3 32B and Qwen3 235B-A22B offer an identical context window of 131K tokens (~98k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Qwen3 32B or Qwen3 235B-A22B?+
Qwen3 32B scores 40% on SWE-bench Verified while Qwen3 235B-A22B scores 48.5%. For autonomous software engineering and multi-file debugging, Qwen3 235B-A22B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3 32B and Qwen3 235B-A22B?+
Qwen3 32B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3 235B-A22B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3 32B and Qwen3 235B-A22B?+
Qwen3 32B generates approximately 85 tokens per second, while Qwen3 235B-A22B delivers 68 tokens per second. In latency-sensitive production environments, Qwen3 32B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3 32B and Qwen3 235B-A22B to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Qwen3 32B, and route deep architectural or complex reasoning queries to Qwen3 32B. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs