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 dari DeepSeek dan QwQ 32B Preview dari Alibaba adalah dua model AI unggulan. DeepSeek-R1 Distill Llama 70B menawarkan jendela konteks 131.1K token dengan harga $0.35/$1.05 per 1 juta token, sedangkan QwQ 32B Preview menyediakan konteks 32.8K token seharga $0.20/$0.60 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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DeepSeek-R1 Distill Llama 70B
Penalaran Bobot TerbukaDeepSeek-R1 Distill Llama 70B dari DeepSeek adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 131K, kecepatan 48 token/detik, indeks kecerda
$0.35 / 1M
$1.05 / 1M
131.1K tokens
57.5%
QwQ 32B Preview
Penalaran Bobot TerbukaQwQ 32B Preview dari Alibaba adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 33K, kecepatan 52 token/detik, indeks kecerdasan 47/100, sert
$0.20 / 1M
$0.60 / 1M
32.8K tokens
54.2%
Perbedaan Utama Sekilas
- 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 |
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 Llama 70B · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 57.5% · GPQA Diamond 70%)
- Kecepatan generasi tinggi 48 token/detik dengan latensi token pertama (TTFT) hanya 880 ms
- Bobot terbuka (70B) 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
QwQ 32B Preview · Strengths & Key Advantages
- Jendela konteks 33K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 54.2% · GPQA Diamond 65.2%)
- Kecepatan generasi tinggi 52 token/detik dengan latensi token pertama (TTFT) hanya 820 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
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik DeepSeek-R1 Distill Llama 70B (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun QwQ 32B Preview (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: DeepSeek-R1 Distill Llama 70B unggul dalam penalaran dan pemrograman, QwQ 32B Preview memberikan kecepatan generasi token lebih tinggi, dan QwQ 32B Preview lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, DeepSeek-R1 Distill Llama 70B atau QwQ 32B Preview?+
Dalam indeks kecerdasan gabungan dan benchmark coding, DeepSeek-R1 Distill Llama 70B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API DeepSeek-R1 Distill Llama 70B vs QwQ 32B Preview?+
DeepSeek-R1 Distill Llama 70B mengenakan biaya $0.35 / $1.05 per 1M token input/output, sedangkan QwQ 32B Preview sebesar $0.20 / $0.60. Secara keseluruhan, QwQ 32B Preview lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
DeepSeek-R1 Distill Llama 70B mendukung konteks 131.1K token dibandingkan 32.8K token pada QwQ 32B Preview, sementara QwQ 32B Preview 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.
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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 & Alibaba serta evaluasi benchmark independen (2026).