DeepSeek Reasoner vs Nemotron 3 Nano 30B A3B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek Reasoner by DeepSeek and Nemotron 3 Nano 30B A3B by NVIDIA represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Nemotron 3 Nano 30B A3B holds a notable pricing advantage for token ingestion. On output generation, Nemotron 3 Nano 30B A3B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, DeepSeek Reasoner provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Nemotron 3 Nano 30B A3B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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DeepSeek Reasoner
オープンウェイトモデルDeepSeek が提供する DeepSeek Reasoner は、最大 1M トークンのコンテキストウィンドウを備え、入力 $0.28/100万トークン・出力 $0.42/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.28 / 1M
$0.42 / 1M
1M tokens
56.5%
Nemotron 3 Nano 30B A3B
オープンウェイトモデルNVIDIA が提供する Nemotron 3 Nano 30B A3B は、最大 262K トークンのコンテキストウィンドウを備え、入力 $0.1/100万トークン・出力 $0.25/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.10 / 1M
$0.25 / 1M
262.1K tokens
38%
ひと目でわかる主な違い
- Nemotron 3 Nano 30B A3B is 2.1x cheaper for API usage (input + output tokens combined).
- DeepSeek Reasoner has a larger context window (1M vs 262K tokens).
- Nemotron 3 Nano 30B A3B is faster at 130 tok/s vs 52 tok/s.
- DeepSeek Reasoner leads on SWE-bench coding (56.5% vs 38.0%).
| Feature | DeepSeek Reasoner | Nemotron 3 Nano 30B A3B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 54 | 45 |
Provider | DeepSeek | NVIDIA |
Input Price Cost per million input tokens | $0.28 / 1M | $0.10 / 1M |
Output Price Cost per million output tokens | $0.42 / 1M | $0.25 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.12 / 1M | $0.06 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 33K | 16K |
Throughput Speed Tokens generated per second | 52 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 56.5% | 38% |
GPQA Diamond Graduate-level reasoning | 76.5% | 54% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.02 / 1M |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek Reasoner · Strengths & Key Advantages
- DeepSeek の高度な Reinforcement-Learned Reasoning MoE アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.28/1M、出力 $0.42/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Nemotron 3 Nano 30B A3B · Strengths & Key Advantages
- NVIDIA の高度な Sparse Mixture-of-Experts (30B Total / 3B Active) アーキテクチャと 262K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.1/1M、出力 $0.25/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek Reasoner | Nemotron 3 Nano 30B A3B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.22 | $0.12 |
Output Tokens / Task Tokens generated per task | 45K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 10K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek Reasoner | Nemotron 3 Nano 30B A3B |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | nemotron-3-nano-30b-a3b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-12 | 2025-12 |
| Knowledge Cutoff | September 2025 | October 2025 |
| Architecture | Reinforcement-Learned Reasoning MoE | Sparse Mixture-of-Experts (30B Total / 3B Active) |
| Parameters | 671B Total (37B Active) | 30B (3B active MoE) |
| 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 ($0.28/1M in) across full 1000K window | Flat rate ($0.1/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.028/1M (no extra cache-write fee) | Cached input at $0.02/1M 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) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 65 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 Reasoner vs Nemotron 3 Nano 30B A3B: よくある質問 (FAQ)
Which is more intelligent, DeepSeek Reasoner or Nemotron 3 Nano 30B A3B?
DeepSeek Reasoner is more intelligent. It scores 54 compared with 45 on the composite Intelligence Index.
Which model is faster?
Nemotron 3 Nano 30B A3B is faster, generating 130 tokens per second compared with 52 tokens per second.
Which model is cheaper to run?
Nemotron 3 Nano 30B A3B is cheaper at $0.06 per 1M tokens blended, compared with $0.12 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron 3 Nano 30B A3B has lower latency, with a time to first token of 0.32s compared with 0.82s.
Which model has a larger context window?
DeepSeek Reasoner has a larger context window, supporting 1M tokens compared with 262.1K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose DeepSeek Reasoner if your priority is Long-form code audits, mathematical analysis, and budget-friendly autonomous reasoning loops. Its performance in demanding tasks justifies its operational token cost. Choose Nemotron 3 Nano 30B A3B if your workflow prioritizes Local developer workstations, high-concurrency enterprise chatbots, and budget RAG pipelines. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does DeepSeek Reasoner compare to Nemotron 3 Nano 30B A3B in API token pricing and cost per token?+
DeepSeek Reasoner costs $0.28 per 1M input tokens and $0.42 per 1M output tokens, whereas Nemotron 3 Nano 30B A3B costs $0.10 input and $0.25 output. Overall, Nemotron 3 Nano 30B A3B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: DeepSeek Reasoner or Nemotron 3 Nano 30B A3B?+
DeepSeek Reasoner supports a context window of 1,000K tokens (~750k words), while Nemotron 3 Nano 30B A3B supports 262K tokens (~197k words). Therefore, DeepSeek Reasoner can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: DeepSeek Reasoner or Nemotron 3 Nano 30B A3B?+
DeepSeek Reasoner scores 56.5% on SWE-bench Verified while Nemotron 3 Nano 30B A3B scores 38%. For autonomous software engineering and multi-file debugging, DeepSeek Reasoner holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between DeepSeek Reasoner and Nemotron 3 Nano 30B A3B?+
DeepSeek Reasoner features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nemotron 3 Nano 30B A3B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between DeepSeek Reasoner and Nemotron 3 Nano 30B A3B?+
DeepSeek Reasoner generates approximately 52 tokens per second, while Nemotron 3 Nano 30B A3B delivers 130 tokens per second. In latency-sensitive production environments, Nemotron 3 Nano 30B A3B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek Reasoner and Nemotron 3 Nano 30B A3B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron 3 Nano 30B A3B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek Reasoner. This tiered routing pattern cuts overall API bills by up to 70%.
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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs