Head-to-Head Model Comparison

Mistral Large 2 vs Mistral Medium 3.5

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 Large 2Profile

Mistral Medium 3.5Profile

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Popular Matchups:

Mistral AI

Mistral Large 2

Modello Base a Pesi Aperti

Mistral Large 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 75 token/s, indice di intelligenza di 46/

Intelligence Index46 / 100

Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38%

Mistral AI

Mistral Medium 3.5

Modello Base a Pesi Aperti

Mistral Medium 3.5 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 262K, velocità di 90 token/s, indice di intelligenza di

Intelligence Index55 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

54%

Differenze Chiave a Colpo d’Occhio

Mistral Medium 3.5 is 3.9x cheaper for API usage (input + output tokens combined).
Mistral Medium 3.5 has a larger context window (262K vs 128K tokens).
Mistral Medium 3.5 is faster at 90 tok/s vs 75 tok/s.
Mistral Medium 3.5 leads on SWE-bench coding (54.0% vs 38.0%).

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

Feature	Mistral Large 2	Mistral Medium 3.5
Intelligence Index Composite reasoning & knowledge score (0-100)	46	55
Provider	Mistral AI	Mistral AI
Input Price Cost per million input tokens	$2.00 / 1M	$0.40 / 1M
Output Price Cost per million output tokens	$6.00 / 1M	$2.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.70 / 1M	$0.35 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	8K	33K
Throughput Speed Tokens generated per second	75 tok/s	90 tok/s
SWE-bench Verified Real-world software engineering	38%	54%
GPQA Diamond Graduate-level reasoning	54%	68%
Cached Input Price Prompt caching discount per 1M tokens	$1.00 / 1M	$0.10 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Mistral Medium 3.5

+16.0% SWE-bench lead

Cost

Mistral Medium 3.5

3.9x cheaper monthly runtime

Speed

Mistral Medium 3.5

+20% faster generation

Context

Mistral Medium 3.5

262.1K tokens window

Punteggi Benchmark Verificati

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

Mistral Large 2Mistral Medium 3.5

SWE-bench Verified (Autonomous Coding)

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

38%vs54%

GPQA Diamond (Doctorate-Level Science & Math)

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

54%vs68%

MMLU-Pro (Multi-task Language Understanding)

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

76.5%vs84.5%

LMSYS Chatbot Arena Elo

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

1282ptsvs1370pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs46%

AIME (Advanced Math Competition)

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

N/Avs78%

Long-Context Reasoning

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

N/Avs85%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs90 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 Large 20.52s

Mistral Medium 3.50.45s

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

Mistral Large 27.19s

Mistral Medium 3.56.01s

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 Large 2 Monthly API Bill

$9.90 / month

$2/1M in · $6/1M out

Mistral Medium 3.5 Monthly API Bill

$2.55 / month

$0.4/1M in · $2/1M out

Mistral Medium 3.5 saves approximately $7.35 / month (74%) under this workload.3.9x Savings

Mistral Large 2 · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 38% · GPQA Diamond 54%)
Throughput elevato di 75 token/secondo con latenza al primo token (TTFT) di soli 520 ms
Pesi aperti (123B) 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
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Mistral Medium 3.5 · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 33K token (SWE-bench Verified 54% · GPQA Diamond 68%)
Throughput elevato di 90 token/secondo con latenza al primo token (TTFT) di soli 450 ms
Pesi aperti (Confidential (~80B MoE)) 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 Large 2	Mistral Medium 3.5
Cost per Task Estimated USD to finish one task	$0.20	$0.38
Output Tokens / Task Tokens generated per task	24K	38K
Reasoning Tokens / Task Thinking tokens burned per task	0K	18K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Large 2	Mistral Medium 3.5
Official API Model ID (SDK)	mistral-large-2-latest	mistral-medium-3-5-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-07	2026-04
Knowledge Cutoff	July 2024	February 2026
Architecture	123B Dense Frontier Transformer	Enterprise Hybrid Reasoning MoE
Parameters	123B	Confidential (~80B MoE)
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 ($2/1M in) across full 128K window	Flat rate ($0.4/1M in) across full 262K window
Prompt Cache Policy & TTL	Cached input at $1/1M 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)	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) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 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	text, vision
Output Modalities	text	text

Mistral Large 2 vs Mistral Medium 3.5: Risposte Rapide (FAQ)

Which is more intelligent, Mistral Large 2 or Mistral Medium 3.5?

Mistral Medium 3.5 is more intelligent. It scores 55 compared with 46 on the composite Intelligence Index.

Which model is faster?

Mistral Medium 3.5 is faster, generating 90 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Mistral Medium 3.5 is cheaper at $0.35 per 1M tokens blended, compared with $1.70 (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 0.52s.

Which model has a larger context window?

Mistral Medium 3.5 has a larger context window, supporting 262.1K tokens compared with 128K tokens.

Verdetto

Sia Mistral Large 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 3.5 (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 3.5 guida nei benchmark di ragionamento e programmazione, Mistral Medium 3.5 garantisce una generazione più rapida e Mistral Medium 3.5 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Mistral Large 2 o Mistral Medium 3.5?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mistral Medium 3.5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Mistral Large 2 e Mistral Medium 3.5?+

Mistral Large 2 costa $2.00 / $6.00 per milione di token di input/output, mentre Mistral Medium 3.5 costa $0.40 / $2.00. Nel costo combinato, Mistral Medium 3.5 risulta più economico.

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

Mistral Large 2 supporta 128K token di contesto contro 262.1K token di Mistral Medium 3.5, mentre Mistral Medium 3.5 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 Large 2 vs Mistral Medium 3.5

Cambia e Confronta Qualsiasi Modello

Mistral Large 2

Mistral Medium 3.5

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Mistral Large 2 · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Medium 3.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Mistral Large 2 vs Mistral Medium 3.5: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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