DeepSeek-R1 Distill Llama 70B vs DeepSeek-R1 Distill Qwen 32B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek-R1 Distill Llama 70B by DeepSeek and DeepSeek-R1 Distill Qwen 32B by DeepSeek 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, DeepSeek-R1 Distill Qwen 32B holds a notable pricing advantage for token ingestion. On output generation, DeepSeek-R1 Distill Qwen 32B is more economical per million generated tokens. 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, DeepSeek-R1 Distill Qwen 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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DeepSeek-R1 Distill Llama 70B
Modèle open-sourceModèle DeepSeek-R1 Distill Llama 70B développé par DeepSeek, offrant une inférence rapide et une grande fiabilité pour les flux professionnels.
$0.35 / 1M
$1.05 / 1M
131.1K tokens
57.5%
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%
Différences Clés en un Coup d’Œil
- DeepSeek-R1 Distill Qwen 32B is 1.7x cheaper for API usage (input + output tokens combined).
- DeepSeek-R1 Distill Qwen 32B is faster at 58 tok/s vs 48 tok/s.
- DeepSeek-R1 Distill Llama 70B leads on SWE-bench coding (57.5% vs 51.2%).
| Caractéristique | DeepSeek-R1 Distill Llama 70B | DeepSeek-R1 Distill Qwen 32B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 46 |
Provider | DeepSeek | DeepSeek |
Input Price Cost per million input tokens | $0.35 / 1M | $0.20 / 1M |
Output Price Cost per million output tokens | $1.05 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.20 / 1M | $0.11 / 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 | 8K |
Throughput Speed Tokens generated per second | 48 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 57.5% | 51.2% |
GPQA Diamond Graduate-level reasoning | 70% | 65.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $0.02 / 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 Llama 70B · Strengths & Key Advantages
- Architecture haute performance optimisée par DeepSeek
- Excellente prise en charge des sorties structurées JSON et des appels d outils
- Stabilité démontrée en environnement de production
Considerations & Trade-offs
- Nécessite une infrastructure dédiée ou des clés API adaptées pour les forts volumes
- Attention à la taille de la fenêtre de contexte pour les très grands documents
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek-R1 Distill Llama 70B | DeepSeek-R1 Distill Qwen 32B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.42 | $0.26 |
Output Tokens / Task Tokens generated per task | 27K | 25K |
Reasoning Tokens / Task Thinking tokens burned per task | 21K | 19K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | DeepSeek-R1 Distill Llama 70B | DeepSeek-R1 Distill Qwen 32B |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-01 | 2025-01 |
| Knowledge Cutoff | July 2024 | July 2024 |
| Architecture | Dense Distilled Reasoning Transformer | Dense Distilled Reasoning Transformer |
| Parameters | 70B | 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) | Flat rate ($0.35/1M in) across full 131K window | Flat rate ($0.2/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.035/1M (no extra cache-write fee) | Automatic prefix caching at $0.02/1M (no extra cache-write fee) |
| 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) · 43 GB VRAM (4-bit Quantized) · 151 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 |
DeepSeek-R1 Distill Llama 70B vs DeepSeek-R1 Distill Qwen 32B: Réponses Rapides (FAQ)
Which is more intelligent, DeepSeek-R1 Distill Llama 70B or DeepSeek-R1 Distill Qwen 32B?
DeepSeek-R1 Distill Llama 70B is more intelligent. It scores 48 compared with 46 on the composite Intelligence Index.
Which model is faster?
DeepSeek-R1 Distill Qwen 32B is faster, generating 58 tokens per second compared with 48 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 $0.20 (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 0.88s.
Verdict
Verdict & Model Selection Recommendation: Choose DeepSeek-R1 Distill Llama 70B if your priority is Enterprise research, academic scientific reasoning, and autonomous multi-agent pipelines. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek-R1 Distill Qwen 32B if your workflow prioritizes Private offline reasoning agents, legal analysis, math proofs, and local Ollama/vLLM setups. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does DeepSeek-R1 Distill Llama 70B compare to DeepSeek-R1 Distill Qwen 32B in API token pricing and cost per token?+
DeepSeek-R1 Distill Llama 70B costs $0.35 per 1M input tokens and $1.05 per 1M output tokens, whereas DeepSeek-R1 Distill Qwen 32B costs $0.20 input and $0.60 output. Overall, DeepSeek-R1 Distill Qwen 32B 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: DeepSeek-R1 Distill Llama 70B or DeepSeek-R1 Distill Qwen 32B?+
Both DeepSeek-R1 Distill Llama 70B and DeepSeek-R1 Distill Qwen 32B 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: DeepSeek-R1 Distill Llama 70B or DeepSeek-R1 Distill Qwen 32B?+
DeepSeek-R1 Distill Llama 70B scores 57.5% on SWE-bench Verified while DeepSeek-R1 Distill Qwen 32B scores 51.2%. For autonomous software engineering and multi-file debugging, DeepSeek-R1 Distill Llama 70B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between DeepSeek-R1 Distill Llama 70B and DeepSeek-R1 Distill Qwen 32B?+
DeepSeek-R1 Distill Llama 70B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek-R1 Distill Qwen 32B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between DeepSeek-R1 Distill Llama 70B and DeepSeek-R1 Distill Qwen 32B?+
DeepSeek-R1 Distill Llama 70B generates approximately 48 tokens per second, while DeepSeek-R1 Distill Qwen 32B delivers 58 tokens per second. In latency-sensitive production environments, DeepSeek-R1 Distill Qwen 32B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek-R1 Distill Llama 70B and DeepSeek-R1 Distill Qwen 32B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek-R1 Distill Qwen 32B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek-R1 Distill Llama 70B. 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