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 的 DeepSeek-R1 Distill Qwen 32B 与 NVIDIA 的 Llama 3.3 Nemotron Super 49B v1 是当前备受关注的两款主力 AI 模型。DeepSeek-R1 Distill Qwen 32B 提供 131.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.20 / 输出 $0.60；而 Llama 3.3 Nemotron Super 49B v1 提供 131.1K Token 上下文窗口，定价为每百万 Token 输入 $0.25 / 输出 $0.50。以下为两者的基准测试、延迟与成本全面对比。
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DeepSeek-R1 Distill Qwen 32B
开源权重推理大模型DeepSeek 推出的 DeepSeek-R1 Distill Qwen 32B 是一款通过大规模强化学习训练、支持私有化部署的开源权重推理大模型，具备 131K 上下文窗口、58 Token/秒生成速度与 46/100 综合智能指数（每百万 Token $0.2 输入 / $0.6 输出）。
$0.20 / 1M
$0.60 / 1M
131.1K tokens
51.2%
Llama 3.3 Nemotron Super 49B v1
开源权重基础大模型NVIDIA 推出的 Llama 3.3 Nemotron Super 49B v1 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 131K 上下文窗口、95 Token/秒生成速度与 46/100 综合智能指数（每百万 Token $0.25 输入 / $0.5 输出）。
$0.25 / 1M
$0.50 / 1M
131.1K tokens
40%
核心差异一览
- 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 |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek-R1 Distill Qwen 32B · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 51.2% · GPQA Diamond 65.9%）
- 高达 58 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 760 毫秒
- 开放模型权重 (32B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 40% · GPQA Diamond 56%）
- 高达 95 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 400 毫秒
- 开放模型权重 (49B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
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 |
技术规格参数
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: 常见问题解答 (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.
综合结论
综合来看，DeepSeek-R1 Distill Qwen 32B（适合：通过大规模强化学习训练、支持私有化部署的开源权重推理任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Llama 3.3 Nemotron Super 49B v1（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：DeepSeek-R1 Distill Qwen 32B 在综合智能与编程基准测试中表现更强，Llama 3.3 Nemotron Super 49B v1 拥有更快的 Token 生成速度，而 DeepSeek-R1 Distill Qwen 32B 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，DeepSeek-R1 Distill Qwen 32B 和 Llama 3.3 Nemotron Super 49B v1 哪个更强？+
在综合智能指数与编程基准评测中，DeepSeek-R1 Distill Qwen 32B 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
DeepSeek-R1 Distill Qwen 32B 与 Llama 3.3 Nemotron Super 49B v1 的 API 价格哪个更便宜？+
DeepSeek-R1 Distill Qwen 32B 的 API 价格为输入 $0.20 / 输出 $0.60（每百万 Token），Llama 3.3 Nemotron Super 49B v1 为输入 $0.25 / 输出 $0.50。综合混合成本来看，DeepSeek-R1 Distill Qwen 32B 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
DeepSeek-R1 Distill Qwen 32B 支持 131.1K Token 上下文窗口，而 Llama 3.3 Nemotron Super 49B v1 支持 131.1K Token。在流式生成速度方面，Llama 3.3 Nemotron Super 49B v1 的吞吐表现更快。
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.
数据来源: DeepSeek 与 NVIDIA 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。