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Head-to-Head Model Comparison

Mistral Medium 3.5 vs DeepSeek V4.1-Flash

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Mistral Medium 3.5 vs DeepSeek V4.1-Flash: Benchmarks, Pricing & Speed Comparison

Mistral AI의 Mistral Medium 3.5와 DeepSeek의 DeepSeek V4.1-Flash는 현재 가장 주목받는 AI 모델입니다. Mistral Medium 3.5는 262.1K 토큰 컨텍스트 윈도우와 100만 토큰당 $0.40/$2.00(입력/출력)의 요금을 제공하며, DeepSeek V4.1-Flash는 1M 토큰 컨텍스트와 $0.30/$1.20의 요금을 제공합니다. 아래에서 벤치마크, 지연 시간 및 비용을 상세히 비교해 보세요.

1:1 모델 맞대결 비교 (VS)

원하는 AI 모델 선택 및 실시간 비교

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Mistral AI

Mistral Medium 3.5

오픈 웨이트 파운데이션 모델

Mistral AI의 Mistral Medium 3.5 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 262K 컨텍스트 윈도우, 초당 90 토큰의 생성 속도, 종합 지능 지수 55/100점(1M 토큰당 입력 $0.4 / 출력 $2)을 제공합니다.

Intelligence Index55 / 100
Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

54%

DeepSeek

DeepSeek V4.1-Flash

오픈 웨이트 파운데이션 모델

DeepSeek의 DeepSeek V4.1-Flash 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 110 토큰의 생성 속도, 종합 지능 지수 58/100점(1M 토큰당 입력 $0.3 / 출력 $1.2)을 제공합니다.

Intelligence Index58 / 100
Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

74.2%

한눈에 보는 핵심 차이점

  • DeepSeek V4.1-Flash is 1.7x cheaper for API usage (input + output tokens combined).
  • DeepSeek V4.1-Flash has a larger context window (1M vs 262K tokens).
  • DeepSeek V4.1-Flash is faster at 110 tok/s vs 90 tok/s.
  • DeepSeek V4.1-Flash leads on SWE-bench coding (74.2% vs 54.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMistral Medium 3.5DeepSeek V4.1-Flash
Intelligence Index
Composite reasoning & knowledge score (0-100)
5558
Provider
Mistral AIDeepSeek
Input Price
Cost per million input tokens
$0.40 / 1M$0.30 / 1M
Output Price
Cost per million output tokens
$2.00 / 1M$1.20 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.35 / 1M$0.18 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
33K384K
Throughput Speed
Tokens generated per second
90 tok/s110 tok/s
SWE-bench Verified
Real-world software engineering
54%74.2%
GPQA Diamond
Graduate-level reasoning
68%90.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.01 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
DeepSeek V4.1-Flash
+20.2% SWE-bench lead
Cost
DeepSeek V4.1-Flash
1.7x cheaper monthly runtime
Speed
DeepSeek V4.1-Flash
+22% faster generation
Context
DeepSeek V4.1-Flash
1M tokens window

검증된 벤치마크 점수 비교

Official evaluation metrics verified by independent third-party research labs.

Mistral Medium 3.5DeepSeek V4.1-Flash
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

54%vs74.2%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

68%vs90.9%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

84.5%vs89.5%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1370ptsvs1455pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs16.8%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

46%vs31.2%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs48%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs73%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

78%vs92.4%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

85%vs93%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs110 tok/s

지연 시간(Latency) 및 응답 속도

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Mistral Medium 3.50.45s
DeepSeek V4.1-Flash0.46s
Est. End-to-End Response (500 tokens)TTFT + generation time
Mistral Medium 3.56.01s
DeepSeek V4.1-Flash5.01s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Mistral Medium 3.5 Monthly API Bill
$2.55 / month
$0.4/1M in · $2/1M out
DeepSeek V4.1-Flash Monthly API Bill
$1.54 / month
$0.3/1M in · $1.2/1M out
DeepSeek V4.1-Flash saves approximately $1.01 / month (40%) under this workload.1.7x Savings

Mistral Medium 3.5 · Strengths & Key Advantages

  • 262K 컨텍스트 윈도우 및 최대 33K 출력 토큰 생성 지원 (SWE-bench Verified 54% · GPQA Diamond 68%)
  • 초당 90 토큰의 빠른 처리 속도와 450ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (Confidential (~80B MoE))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

DeepSeek V4.1-Flash · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 384K 출력 토큰 생성 지원 (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
  • 초당 110 토큰의 빠른 처리 속도와 460ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (763B total (8B prefill / 16B decode ))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricMistral Medium 3.5DeepSeek V4.1-Flash
Cost per Task
Estimated USD to finish one task
$0.38$0.27
Output Tokens / Task
Tokens generated per task
38K58K
Reasoning Tokens / Task
Thinking tokens burned per task
18K28K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureMistral Medium 3.5DeepSeek V4.1-Flash
Official API Model ID (SDK)mistral-medium-3-5-latestdeepseek-reasoner
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-042026-09
Knowledge CutoffFebruary 2026June 2026
ArchitectureEnterprise Hybrid Reasoning MoECausal Encoder-Decoder (20 enc + 20 dec layers)
ParametersConfidential (~80B MoE)763B total (8B prefill / 16B decode active)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.4/1M in) across full 262K windowFlat rate ($0.3/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.1/1M tokensAutomatic prefix caching at $0.006/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Mistral Medium 3.5 vs DeepSeek V4.1-Flash: 핵심 요약 문답 (FAQ)

Which is more intelligent, Mistral Medium 3.5 or DeepSeek V4.1-Flash?

DeepSeek V4.1-Flash is more intelligent. It scores 58 compared with 55 on the composite Intelligence Index.

Which model is faster?

DeepSeek V4.1-Flash is faster, generating 110 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

DeepSeek V4.1-Flash is cheaper at $0.18 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Medium 3.5 has lower latency, with a time to first token of 0.45s compared with 0.46s.

Which model has a larger context window?

DeepSeek V4.1-Flash has a larger context window, supporting 1M tokens compared with 262.1K tokens.

종합 평가

Mistral Medium 3.5(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 DeepSeek V4.1-Flash(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 DeepSeek V4.1-Flash가 앞서며, 토큰 생성 속도에서는 DeepSeek V4.1-Flash가 빠르고, 대규모 API 운영 비용 면에서는 DeepSeek V4.1-Flash가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Mistral Medium 3.5와 DeepSeek V4.1-Flash 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 DeepSeek V4.1-Flash가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Mistral Medium 3.5와 DeepSeek V4.1-Flash의 API 요금 차이는 얼마인가요?+

Mistral Medium 3.5는 100만 토큰당 입력 $0.40 / 출력 $2.00이며, DeepSeek V4.1-Flash는 입력 $0.30 / 출력 $1.20입니다. 혼합 비용 기준으로는 DeepSeek V4.1-Flash가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Mistral Medium 3.5가 262.1K 토큰, DeepSeek V4.1-Flash가 1M 토큰를 지원하며, 출력 속도 면에서는 DeepSeek V4.1-Flash가 더 빠릅니다.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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: Mistral AI 및 DeepSeek 공식 API 문서, 시스템 카드 및 독립 벤치마크 평가 (2026).