Head-to-Head Model Comparison

Mistral Small 3.2 vs Mistral Medium 3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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Mistral Small 3.2Profile

Mistral Medium 3Profile

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Mistral AI

Mistral Small 3.2

Modello Base a Pesi Aperti

Mistral Small 3.2 di Mistral AI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 100 token/s, indice di intelligenza di

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

Modello Base a Pesi Aperti

Mistral Medium 3 di Mistral AI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 131K, velocità di 85 token/s, indice di intelligenza di 47

Intelligence Index47 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.5%

Differenze Chiave a Colpo d’Occhio

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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 34% · GPQA Diamond 48.5%)
Throughput elevato di 100 token/secondo con latenza al primo token (TTFT) di soli 400 ms
Pesi aperti (24B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Mistral Medium 3 · Strengths & Key Advantages

Finestra di contesto da 131K con capacità massima di output fino a 16K token (SWE-bench Verified 40.5% · GPQA Diamond 57.5%)
Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 480 ms
Pesi aperti (Confidential (~70B)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

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: Risposte Rapide (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.

Verdetto

Sia Mistral Small 3.2 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Mistral Medium 3 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Mistral Medium 3 guida nei benchmark di ragionamento e programmazione, Mistral Small 3.2 garantisce una generazione più rapida e Mistral Small 3.2 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Mistral Small 3.2 o Mistral Medium 3?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mistral Medium 3. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Mistral Small 3.2 e Mistral Medium 3?+

Mistral Small 3.2 costa $0.10 / $0.30 per milione di token di input/output, mentre Mistral Medium 3 costa $0.40 / $2.00. Nel costo combinato, Mistral Small 3.2 risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Mistral Small 3.2 supporta 128K token di contesto contro 131.1K token di Mistral Medium 3, mentre Mistral Small 3.2 offre una velocità di generazione superiore.

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

Cambia e Confronta Qualsiasi Modello

Mistral Small 3.2

Mistral Medium 3

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Mistral Small 3.2 · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Medium 3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Mistral Small 3.2 vs Mistral Medium 3: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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