DeepSeek-R1 Distill Llama 70B vs QwQ 32B Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek-R1 Distill Llama 70B di DeepSeek e QwQ 32B Preview di Alibaba sono due modelli di IA di riferimento. DeepSeek-R1 Distill Llama 70B offre una finestra di contesto di 131.1K token a $0.35/$1.05 per milione di token, mentre QwQ 32B Preview mette a disposizione 32.8K token di contesto a $0.20/$0.60 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
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DeepSeek-R1 Distill Llama 70B
Ragionamento a Pesi ApertiDeepSeek-R1 Distill Llama 70B di DeepSeek è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 131K, velocità di 48 token/s, indice di intelligenza di 4
$0.35 / 1M
$1.05 / 1M
131.1K tokens
57.5%
QwQ 32B Preview
Ragionamento a Pesi ApertiQwQ 32B Preview di Alibaba è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 33K, velocità di 52 token/s, indice di intelligenza di 47/100 e tariffa
$0.20 / 1M
$0.60 / 1M
32.8K tokens
54.2%
Differenze Chiave a Colpo d’Occhio
- QwQ 32B Preview is 1.7x cheaper for API usage (input + output tokens combined).
- DeepSeek-R1 Distill Llama 70B has a larger context window (131K vs 33K tokens).
- QwQ 32B Preview is faster at 52 tok/s vs 48 tok/s.
- DeepSeek-R1 Distill Llama 70B leads on SWE-bench coding (57.5% vs 54.2%).
| Feature | DeepSeek-R1 Distill Llama 70B | QwQ 32B Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 47 |
Provider | DeepSeek | Alibaba |
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 | $0.14 / 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 | 8K | 8K |
Throughput Speed Tokens generated per second | 48 tok/s | 52 tok/s |
SWE-bench Verified Real-world software engineering | 57.5% | 54.2% |
GPQA Diamond Graduate-level reasoning | 70% | 65.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $0.05 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek-R1 Distill Llama 70B · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 57.5% · GPQA Diamond 70%)
- Throughput elevato di 48 token/secondo con latenza al primo token (TTFT) di soli 880 ms
- Pesi aperti (70B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
QwQ 32B Preview · Strengths & Key Advantages
- Finestra di contesto da 33K con capacità massima di output fino a 8K token (SWE-bench Verified 54.2% · GPQA Diamond 65.2%)
- Throughput elevato di 52 token/secondo con latenza al primo token (TTFT) di soli 820 ms
- Pesi aperti (32B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek-R1 Distill Llama 70B | QwQ 32B Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.42 | $0.45 |
Output Tokens / Task Tokens generated per task | 27K | 22K |
Reasoning Tokens / Task Thinking tokens burned per task | 21K | 18K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek-R1 Distill Llama 70B | QwQ 32B Preview |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | qwq-32b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-01 | 2024-11 |
| Knowledge Cutoff | July 2024 | September 2024 |
| Architecture | Dense Distilled Reasoning Transformer | Reinforcement Learning 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 33K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.035/1M (no extra cache-write fee) | 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) · 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 QwQ 32B Preview: Risposte Rapide (FAQ)
Which is more intelligent, DeepSeek-R1 Distill Llama 70B or QwQ 32B Preview?
DeepSeek-R1 Distill Llama 70B is more intelligent. It scores 48 compared with 47 on the composite Intelligence Index.
Which model is faster?
QwQ 32B Preview is faster, generating 52 tokens per second compared with 48 tokens per second.
Which model is cheaper to run?
QwQ 32B Preview is cheaper at $0.14 per 1M tokens blended, compared with $0.20 (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 0.88s.
Which model has a larger context window?
DeepSeek-R1 Distill Llama 70B has a larger context window, supporting 131.1K tokens compared with 32.8K tokens.
Verdetto
Sia DeepSeek-R1 Distill Llama 70B (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) sia QwQ 32B Preview (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: DeepSeek-R1 Distill Llama 70B guida nei benchmark di ragionamento e programmazione, QwQ 32B Preview garantisce una generazione più rapida e QwQ 32B Preview offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, DeepSeek-R1 Distill Llama 70B o QwQ 32B Preview?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale DeepSeek-R1 Distill Llama 70B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra DeepSeek-R1 Distill Llama 70B e QwQ 32B Preview?+
DeepSeek-R1 Distill Llama 70B costa $0.35 / $1.05 per milione di token di input/output, mentre QwQ 32B Preview costa $0.20 / $0.60. Nel costo combinato, QwQ 32B Preview risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
DeepSeek-R1 Distill Llama 70B supporta 131.1K token di contesto contro 32.8K token di QwQ 32B Preview, mentre QwQ 32B Preview offre una velocità di generazione superiore.
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.
Fonti ufficiali: Documentazione API ufficiale di DeepSeek e Alibaba, schede di sistema e benchmark indipendenti (2026).