Head-to-Head Model Comparison

Mistral Small 3.2 vs Mistral Medium 3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책

원하는 AI 모델 선택 및 실시간 비교

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Mistral Small 3.2Profile

Mistral Medium 3Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Mistral Small 3.2

오픈 웨이트 파운데이션 모델

Mistral AI의 Mistral Small 3.2 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 100 토큰의 생성 속도, 종합 지능 지수 43/100점(1M 토큰당 입력 $0.1 / 출력 $0.3)을 제공합니다.

Intelligence Index43 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

128K tokens

SWE-bench Verified:

34%

Mistral AI

Mistral Medium 3

오픈 웨이트 파운데이션 모델

Mistral AI의 Mistral Medium 3 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 131K 컨텍스트 윈도우, 초당 85 토큰의 생성 속도, 종합 지능 지수 47/100점(1M 토큰당 입력 $0.4 / 출력 $2)을 제공합니다.

Intelligence Index47 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.5%

한눈에 보는 핵심 차이점

Mistral Small 3.2 is 5.2x cheaper for API usage (input + output tokens combined).
Mistral Medium 3 has a larger context window (131K vs 128K tokens).
Mistral Small 3.2 is faster at 100 tok/s vs 85 tok/s.
Mistral Medium 3 leads on SWE-bench coding (40.5% vs 34.0%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Mistral Small 3.2	Mistral Medium 3
Intelligence Index Composite reasoning & knowledge score (0-100)	43	47
Provider	Mistral AI	Mistral AI
Input Price Cost per million input tokens	$0.10 / 1M	$0.40 / 1M
Output Price Cost per million output tokens	$0.30 / 1M	$2.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.09 / 1M	$0.35 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	100 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	34%	40.5%
GPQA Diamond Graduate-level reasoning	48.5%	57.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.10 / 1M

부문별 우위 모델

At-a-glance decision framework

Coding

Mistral Medium 3

+6.5% SWE-bench lead

Cost

Mistral Small 3.2

5.2x cheaper monthly runtime

Speed

Mistral Small 3.2

+18% faster generation

Context

Mistral Medium 3

131.1K tokens window

검증된 벤치마크 점수 비교

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

Mistral Small 3.2Mistral Medium 3

SWE-bench Verified (Autonomous Coding)

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

34%vs40.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.5%vs57.5%

MMLU-Pro (Multi-task Language Understanding)

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

72.5%vs79.5%

LMSYS Chatbot Arena Elo

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

1255ptsvs1310pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

29%vs36%

AIME (Advanced Math Competition)

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

55%vs68%

Long-Context Reasoning

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

68%vs77%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

100 tok/s vs85 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 Small 3.20.40s

Mistral Medium 30.48s

Est. End-to-End Response (500 tokens)TTFT + generation time

Mistral Small 3.25.40s

Mistral Medium 36.36s

실시간 API 토큰 비용 시뮬레이터

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Mistral Small 3.2 Monthly API Bill

$0.50 / month

$0.1/1M in · $0.3/1M out

Mistral Medium 3 Monthly API Bill

$2.55 / month

$0.4/1M in · $2/1M out

Mistral Small 3.2 saves approximately $2.06 / month (81%) under this workload.5.2x Savings

Mistral Small 3.2 · Strengths & Key Advantages

128K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (SWE-bench Verified 34% · GPQA Diamond 48.5%)
초당 100 토큰의 빠른 처리 속도와 400ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (24B)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

Mistral Medium 3 · Strengths & Key Advantages

131K 컨텍스트 윈도우 및 최대 16K 출력 토큰 생성 지원 (SWE-bench Verified 40.5% · GPQA Diamond 57.5%)
초당 85 토큰의 빠른 처리 속도와 480ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (Confidential (~70B))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.

Metric	Mistral Small 3.2	Mistral Medium 3
Cost per Task Estimated USD to finish one task	$0.09	$0.35
Output Tokens / Task Tokens generated per task	25K	35K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Small 3.2	Mistral Medium 3
Official API Model ID (SDK)	mistral-small-3-2-latest	mistral-medium-3-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-06	2025-05
Knowledge Cutoff	April 2025	March 2025
Architecture	Compact Vision-Language Transformer	Enterprise Multimodal Transformer
Parameters	24B	Confidential (~70B)
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 ($0.1/1M in) across full 128K window	Flat rate ($0.4/1M in) across full 131K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Cached input at $0.1/1M 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) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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
Output Modalities	text	text

Mistral Small 3.2 vs Mistral Medium 3: 핵심 요약 문답 (FAQ)

Which is more intelligent, Mistral Small 3.2 or Mistral Medium 3?

Mistral Medium 3 is more intelligent. It scores 47 compared with 43 on the composite Intelligence Index.

Which model is faster?

Mistral Small 3.2 is faster, generating 100 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Mistral Small 3.2 is cheaper at $0.09 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Small 3.2 has lower latency, with a time to first token of 0.40s compared with 0.48s.

Which model has a larger context window?

Mistral Medium 3 has a larger context window, supporting 131.1K tokens compared with 128K tokens.

종합 평가

Mistral Small 3.2(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Mistral Medium 3(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 Mistral Medium 3가 앞서며, 토큰 생성 속도에서는 Mistral Small 3.2가 빠르고, 대규모 API 운영 비용 면에서는 Mistral Small 3.2가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Mistral Small 3.2와 Mistral Medium 3 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Mistral Medium 3가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Mistral Small 3.2와 Mistral Medium 3의 API 요금 차이는 얼마인가요?+

Mistral Small 3.2는 100만 토큰당 입력 $0.10 / 출력 $0.30이며, Mistral Medium 3는 입력 $0.40 / 출력 $2.00입니다. 혼합 비용 기준으로는 Mistral Small 3.2가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Mistral Small 3.2가 128K 토큰, Mistral Medium 3가 131.1K 토큰를 지원하며, 출력 속도 면에서는 Mistral Small 3.2가 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Mistral Small 3.2 vs Mistral Medium 3

원하는 AI 모델 선택 및 실시간 비교

Mistral Small 3.2

Mistral Medium 3

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

Mistral Small 3.2 · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Medium 3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

Mistral Small 3.2 vs Mistral Medium 3: 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

Related reading