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 dari DeepSeek dan Llama 3.3 Nemotron Super 49B v1 dari NVIDIA adalah dua model AI unggulan. DeepSeek-R1 Distill Qwen 32B menawarkan jendela konteks 131.1K token dengan harga $0.20/$0.60 per 1 juta token, sedangkan Llama 3.3 Nemotron Super 49B v1 menyediakan konteks 131.1K token seharga $0.25/$0.50 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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DeepSeek-R1 Distill Qwen 32B
Penalaran Bobot TerbukaDeepSeek-R1 Distill Qwen 32B dari DeepSeek adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 131K, kecepatan 58 token/detik, indeks kecerdas
$0.20 / 1M
$0.60 / 1M
131.1K tokens
51.2%
Llama 3.3 Nemotron Super 49B v1
Model Dasar Bobot TerbukaLlama 3.3 Nemotron Super 49B v1 dari NVIDIA adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 95 token/detik, indeks kecerdasan 46/100,
$0.25 / 1M
$0.50 / 1M
131.1K tokens
40%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek-R1 Distill Qwen 32B · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 51.2% · GPQA Diamond 65.9%)
- Kecepatan generasi tinggi 58 token/detik dengan latensi token pertama (TTFT) hanya 760 ms
- Bobot terbuka (32B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 40% · GPQA Diamond 56%)
- Kecepatan generasi tinggi 95 token/detik dengan latensi token pertama (TTFT) hanya 400 ms
- Bobot terbuka (49B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik DeepSeek-R1 Distill Qwen 32B (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 3.3 Nemotron Super 49B v1 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: DeepSeek-R1 Distill Qwen 32B unggul dalam penalaran dan pemrograman, Llama 3.3 Nemotron Super 49B v1 memberikan kecepatan generasi token lebih tinggi, dan DeepSeek-R1 Distill Qwen 32B lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, DeepSeek-R1 Distill Qwen 32B atau Llama 3.3 Nemotron Super 49B v1?+
Dalam indeks kecerdasan gabungan dan benchmark coding, DeepSeek-R1 Distill Qwen 32B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API DeepSeek-R1 Distill Qwen 32B vs Llama 3.3 Nemotron Super 49B v1?+
DeepSeek-R1 Distill Qwen 32B mengenakan biaya $0.20 / $0.60 per 1M token input/output, sedangkan Llama 3.3 Nemotron Super 49B v1 sebesar $0.25 / $0.50. Secara keseluruhan, DeepSeek-R1 Distill Qwen 32B lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
DeepSeek-R1 Distill Qwen 32B mendukung konteks 131.1K token dibandingkan 131.1K token pada Llama 3.3 Nemotron Super 49B v1, sementara Llama 3.3 Nemotron Super 49B v1 unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Sumber resmi: Dokumentasi API resmi DeepSeek & NVIDIA serta evaluasi benchmark independen (2026).