QwQ 32B Preview vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare QwQ 32B Preview by Alibaba against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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QwQ 32B Preview
オープンウェイトモデルAlibaba が提供する QwQ 32B Preview は、最大 32K トークンのコンテキストウィンドウを備え、入力 $0.2/100万トークン・出力 $0.6/100万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。
$0.20 / 1M
$0.60 / 1M
32.8K tokens
54.2%
Claude Opus 5.5
高度な推論モデルAnthropic が提供する Claude Opus 5.5 は、最大 1M トークンのコンテキストウィンドウを備え、入力 $4.0/100万トークン・出力 $20.0/100万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。
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
ひと目でわかる主な違い
- QwQ 32B Preview is 26.9x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 33K tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 52 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 54.2%).
| Feature | QwQ 32B Preview | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 47 | 94 |
Provider | Alibaba | Anthropic |
Input Price Cost per million input tokens | $0.20 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.14 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 52 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 54.2% | 84.6% |
GPQA Diamond Graduate-level reasoning | 65.2% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.40 / 1M |
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.
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.
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.
QwQ 32B Preview · Strengths & Key Advantages
- Alibaba の高度な Reinforcement Learning Reasoning Transformer アーキテクチャと 32K 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 $0.2/1M、出力 $0.6/1M）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
Claude Opus 5.5 · Strengths & Key Advantages
- Anthropic の高度な Extended Autonomous Hybrid Reasoning Transformer アーキテクチャと 1M 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 $4.0/1M、出力 $20.0/1M）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 | QwQ 32B Preview | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.45 | $3.85 |
Output Tokens / Task Tokens generated per task | 22K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 42K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | QwQ 32B Preview | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | qwq-32b-instruct | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2024-11 | 2026-09 |
| Knowledge Cutoff | September 2024 | June 2026 |
| Architecture | Reinforcement Learning Reasoning Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 32B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 33K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| 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 | 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| 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) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
QwQ 32B Preview vs Claude Opus 5.5: よくある質問 (FAQ)
Which is more intelligent, QwQ 32B Preview or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 47 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 52 tokens per second.
Which model is cheaper to run?
QwQ 32B Preview is cheaper at $0.14 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
QwQ 32B Preview has lower latency, with a time to first token of 0.82s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 32.8K tokens.
総合評価
Choose QwQ 32B Preview for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、alibaba エコシステムの統合, or choose Claude Opus 5.5 for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、anthropic エコシステムの統合.
よくある質問 (FAQ)
Which is cheaper: QwQ 32B Preview or Claude Opus 5.5?+
QwQ 32B Preview costs $0.2/1M input and $0.6/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: QwQ 32B Preview or Claude Opus 5.5?+
QwQ 32B Preview supports 33K tokens whereas Claude Opus 5.5 supports 1M tokens.
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: Alibaba & Anthropic Official Documentation