Head-to-Head Model Comparison

Mistral Small 3.2 vs Mistral Medium 3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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Mistral Small 3.2Profile

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Popular Matchups:

Mistral AI

Mistral Small 3.2

Model Dasar Bobot Terbuka

Mistral Small 3.2 dari Mistral AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 100 token/detik, indeks kecerdasan 43/100, serta tar

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

Model Dasar Bobot Terbuka

Mistral Medium 3 dari Mistral AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 85 token/detik, indeks kecerdasan 47/100, serta tarif

Intelligence Index47 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.5%

Perbedaan Utama Sekilas

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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 34% · GPQA Diamond 48.5%)
Kecepatan generasi tinggi 100 token/detik dengan latensi token pertama (TTFT) hanya 400 ms
Bobot terbuka (24B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Mistral Medium 3 · Strengths & Key Advantages

Jendela konteks 131K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 40.5% · GPQA Diamond 57.5%)
Kecepatan generasi tinggi 85 token/detik dengan latensi token pertama (TTFT) hanya 480 ms
Bobot terbuka (Confidential (~70B)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Mistral Small 3.2 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Mistral Medium 3 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Mistral Medium 3 unggul dalam penalaran dan pemrograman, Mistral Small 3.2 memberikan kecepatan generasi token lebih tinggi, dan Mistral Small 3.2 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Mistral Small 3.2 atau Mistral Medium 3?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Mistral Medium 3 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Mistral Small 3.2 vs Mistral Medium 3?+

Mistral Small 3.2 mengenakan biaya $0.10 / $0.30 per 1M token input/output, sedangkan Mistral Medium 3 sebesar $0.40 / $2.00. Secara keseluruhan, Mistral Small 3.2 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Mistral Small 3.2 mendukung konteks 128K token dibandingkan 131.1K token pada Mistral Medium 3, sementara Mistral Small 3.2 unggul dalam kecepatan output token per detik.

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

Ganti & Bandingkan Model Apa Saja

Mistral Small 3.2

Mistral Medium 3

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Mistral Small 3.2 · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Medium 3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Mistral Small 3.2 vs Mistral Medium 3: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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