DeepSeek-R1 Distill Qwen 32B vs Llama 3.3 Nemotron Super 49B v1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek-R1 Distill Qwen 32B di DeepSeek e Llama 3.3 Nemotron Super 49B v1 di NVIDIA sono due modelli di IA di riferimento. DeepSeek-R1 Distill Qwen 32B offre una finestra di contesto di 131.1K token a $0.20/$0.60 per milione di token, mentre Llama 3.3 Nemotron Super 49B v1 mette a disposizione 131.1K token di contesto a $0.25/$0.50 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
DeepSeek-R1 Distill Qwen 32B
Ragionamento a Pesi ApertiDeepSeek-R1 Distill Qwen 32B 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 58 token/s, indice di intelligenza di 46
$0.20 / 1M
$0.60 / 1M
131.1K tokens
51.2%
Llama 3.3 Nemotron Super 49B v1
Modello Base a Pesi ApertiLlama 3.3 Nemotron Super 49B v1 di NVIDIA è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 131K, velocità di 95 token/s, indice di intelli
$0.25 / 1M
$0.50 / 1M
131.1K tokens
40%
Differenze Chiave a Colpo d’Occhio
- DeepSeek-R1 Distill Qwen 32B is 1.1x cheaper for API usage (input + output tokens combined).
- Llama 3.3 Nemotron Super 49B v1 is faster at 95 tok/s vs 58 tok/s.
- DeepSeek-R1 Distill Qwen 32B leads on SWE-bench coding (51.2% vs 40.0%).
| Feature | DeepSeek-R1 Distill Qwen 32B | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 46 |
Provider | DeepSeek | NVIDIA |
Input Price Cost per million input tokens | $0.20 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.11 / 1M | $0.16 / 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 | 58 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | 51.2% | 40% |
GPQA Diamond Graduate-level reasoning | 65.9% | 56% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.08 / 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 Qwen 32B · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 51.2% · GPQA Diamond 65.9%)
- Throughput elevato di 58 token/secondo con latenza al primo token (TTFT) di soli 760 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
Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 40% · GPQA Diamond 56%)
- Throughput elevato di 95 token/secondo con latenza al primo token (TTFT) di soli 400 ms
- Pesi aperti (49B) 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 Qwen 32B | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.26 | $0.22 |
Output Tokens / Task Tokens generated per task | 25K | 30K |
Reasoning Tokens / Task Thinking tokens burned per task | 19K | 12K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek-R1 Distill Qwen 32B | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | llama-3-3-nemotron-super-49b-v1 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-01 | 2025-04 |
| Knowledge Cutoff | July 2024 | January 2025 |
| Architecture | Dense Distilled Reasoning Transformer | Compact High-Density Transformer |
| Parameters | 32B | 49B |
| 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 ($0.2/1M in) across full 131K window | Flat rate ($0.25/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.02/1M (no extra cache-write fee) | Cached input at $0.08/1M 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 · 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) · 30 GB VRAM (4-bit Quantized) · 105 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 Qwen 32B vs Llama 3.3 Nemotron Super 49B v1: Risposte Rapide (FAQ)
Which model is faster?
Llama 3.3 Nemotron Super 49B v1 is faster, generating 95 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 $0.16 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.3 Nemotron Super 49B v1 has lower latency, with a time to first token of 0.40s compared with 0.76s.
Verdetto
Sia DeepSeek-R1 Distill Qwen 32B (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) sia Llama 3.3 Nemotron Super 49B v1 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: DeepSeek-R1 Distill Qwen 32B guida nei benchmark di ragionamento e programmazione, Llama 3.3 Nemotron Super 49B v1 garantisce una generazione più rapida e DeepSeek-R1 Distill Qwen 32B offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, DeepSeek-R1 Distill Qwen 32B o Llama 3.3 Nemotron Super 49B v1?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale DeepSeek-R1 Distill Qwen 32B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra DeepSeek-R1 Distill Qwen 32B e Llama 3.3 Nemotron Super 49B v1?+
DeepSeek-R1 Distill Qwen 32B costa $0.20 / $0.60 per milione di token di input/output, mentre Llama 3.3 Nemotron Super 49B v1 costa $0.25 / $0.50. Nel costo combinato, DeepSeek-R1 Distill Qwen 32B risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
DeepSeek-R1 Distill Qwen 32B supporta 131.1K token di contesto contro 131.1K token di Llama 3.3 Nemotron Super 49B v1, mentre Llama 3.3 Nemotron Super 49B v1 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.
Related reading
- DeepSeek-R1 Distill Llama 70B vs DeepSeek-R1 Distill Qwen 32B: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aion-3.0 vs Aion-Mini: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aion-3.0 vs Gemini 3.6 Flash: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aion-Mini vs Laguna XS 2.1: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aya Expanse 32B vs Llama 3.2 90B Vision: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
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 NVIDIA, schede di sistema e benchmark indipendenti (2026).