DeepSeek-R1 Distill Qwen 32B vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare DeepSeek-R1 Distill Qwen 32B by DeepSeek against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
DeepSeek-R1 Distill Qwen 32B
Standard du raisonnement localModèle de raisonnement distillé à partir de DeepSeek-R1 sur base Qwen-2.5-32B, offrant des performances comparables à o1-mini en local.
$0.20 / 1M
$0.60 / 1M
131.1K tokens
51.2%
Claude Opus 5.5
Titan d'intelligence de pointed'Anthropic pinnacle modèle de pointe conçu pour à long terme agentic ingénierie logicielle, dense multimodal document synthèse, et autonome recherche flux de travail.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Différences Clés en un Coup d’Œil
- DeepSeek-R1 Distill Qwen 32B is 28.3x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 131K tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 58 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 51.2%).
| Caractéristique | DeepSeek-R1 Distill Qwen 32B | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 94 |
Provider | DeepSeek | 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 | $0.11 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 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 | 58 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 51.2% | 84.6% |
GPQA Diamond Graduate-level reasoning | 65.9% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.40 / 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.
DeepSeek-R1 Distill Qwen 32B · Strengths & Key Advantages
- Raisonnement exceptionnel hérité du modèle R1 de 671 milliards de paramètres
- Fonctionne sur stations de travail équipées de GPU grand public
- Exposition transparente de la chaîne de réflexion pas à pas
- Licence MIT entièrement libre et commerciale
Considerations & Trade-offs
- Légèrement moins profond que le modèle complet 671B
- Temps de réflexion parfois long pour des réponses simples
Claude Opus 5.5 · Strengths & Key Advantages
- inégalé autonome multi-étapes codage agentique et refactorisation
- Flawless multidocument raisonnement sur 1M token contexte avec no long-prompt surcharge
- le plus élevé Artificial analyse intelligence Index ever measured (58/100), leading SWE-bench Pro à 89.9%
- Precise outil exécution avec robust error-recovery boucles
Considerations & Trade-offs
- Premium sortie tarification ($20.00/M) plus heavy raisonnement-token usage (~119K sortie tokens par index tâche) keeps long agentic fonctionne expensive
- long pensée times à maximum effort peut push un unique hard agentic tâche toward ~$13
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek-R1 Distill Qwen 32B | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.26 | $3.85 |
Output Tokens / Task Tokens generated per task | 25K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 19K | 42K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | DeepSeek-R1 Distill Qwen 32B | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-01 | 2026-09 |
| Knowledge Cutoff | July 2024 | June 2026 |
| Architecture | Dense Distilled 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 131K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.02/1M (no extra cache-write fee) | $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 · 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 | 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 |
DeepSeek-R1 Distill Qwen 32B vs Claude Opus 5.5: Réponses Rapides (FAQ)
Which is more intelligent, DeepSeek-R1 Distill Qwen 32B or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 46 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 58 tokens per second.
Which model is cheaper to run?
DeepSeek-R1 Distill Qwen 32B is cheaper at $0.11 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek-R1 Distill Qwen 32B has lower latency, with a time to first token of 0.76s 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 131.1K tokens.
Verdict
Choose DeepSeek-R1 Distill Qwen 32B for agents d'analyse privés hors ligne, recherche juridique et serveurs ollama/vllm, or choose Claude Opus 5.5 for grand-échelle logiciel development agents, d'entreprise codebases, et mission critique scientifique synthèse.
Questions fréquentes
Which is cheaper: DeepSeek-R1 Distill Qwen 32B or Claude Opus 5.5?+
DeepSeek-R1 Distill Qwen 32B 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: DeepSeek-R1 Distill Qwen 32B or Claude Opus 5.5?+
DeepSeek-R1 Distill Qwen 32B supports 131K 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: DeepSeek & Anthropic Official Documentation