DeepSeek V4.1-Flash vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare DeepSeek V4.1-Flash by DeepSeek against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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DeepSeek V4.1-Flash
오픈 웨이트 모델DeepSeek의 DeepSeek V4.1-Flash 모델은 1M 토큰 컨텍스트 윈도우와 입력 $0.3/1M 토큰, 출력 $1.2/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
Llama 5
오픈 웨이트 모델Meta의 Llama 5 모델은 5M 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
한눈에 보는 핵심 차이점
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 1M tokens).
- DeepSeek V4.1-Flash is faster at 110 tok/s vs 65 tok/s.
- DeepSeek V4.1-Flash leads on SWE-bench coding (74.2% vs 74.0%).
| Feature | DeepSeek V4.1-Flash | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 94 |
Provider | DeepSeek | Meta |
Input Price Cost per million input tokens | $0.30 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.18 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 384K | 128K |
Throughput Speed Tokens generated per second | 110 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 74.2% | 74% |
GPQA Diamond Graduate-level reasoning | 90.9% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek V4.1-Flash · Strengths & Key Advantages
- DeepSeek의 첨단 Causal Encoder-Decoder (20 enc + 20 dec layers) 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.3/1M, 출력 $1.2/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Llama 5 · Strengths & Key Advantages
- Meta의 첨단 Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) 아키텍처와 최대 5M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek V4.1-Flash | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.27 | $0.00 |
Output Tokens / Task Tokens generated per task | 58K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 20K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek V4.1-Flash | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | meta-llama/Llama-5 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-04 |
| Knowledge Cutoff | June 2026 | January 2026 |
| Architecture | Causal Encoder-Decoder (20 enc + 20 dec layers) | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 763B total (8B prefill / 16B decode active) | 600B+ total (~40-80B active) |
| 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 ($0.3/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.006/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 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, vision | text, vision, video, audio |
| Output Modalities | text | text |
DeepSeek V4.1-Flash vs Llama 5: 핵심 요약 문답 (FAQ)
Which is more intelligent, DeepSeek V4.1-Flash or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 58 on the composite Intelligence Index.
Which model is faster?
DeepSeek V4.1-Flash is faster, generating 110 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.18 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek V4.1-Flash has lower latency, with a time to first token of 0.46s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.
종합 평가
Choose DeepSeek V4.1-Flash for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 deepseek 기반 프로덕션 파이프라인, or choose Llama 5 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 meta 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: DeepSeek V4.1-Flash or Llama 5?+
DeepSeek V4.1-Flash costs $0.3/1M input and $1.2/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: DeepSeek V4.1-Flash or Llama 5?+
DeepSeek V4.1-Flash supports 1M tokens whereas Llama 5 supports 5M tokens.
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.
공식 출처: DeepSeek & Meta Official Documentation