Head-to-Head Model Comparison

Mistral Large 2 vs GPT-4o

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia e Confronta Qualsiasi Modello

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

Mistral Large 2Profile

GPT-4oProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

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%

OpenAI

GPT-4o

Modello Vocale e Audio Nativo

GPT-4o di OpenAI è un modello di IA progettato per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, con finestra di contesto da 128K, velocità di 105 token/s, indice di intelligenza di 41/100 e tariffa di $

Intelligence Index41 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38.8%

Differenze Chiave a Colpo d’Occhio

Mistral Large 2 is 1.5x cheaper for API usage (input + output tokens combined).
GPT-4o is faster at 105 tok/s vs 75 tok/s.
GPT-4o leads on SWE-bench coding (38.8% vs 38.0%).

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

Feature	Mistral Large 2	GPT-4o
Intelligence Index Composite reasoning & knowledge score (0-100)	46	41
Provider	Mistral AI	OpenAI
Input Price Cost per million input tokens	$2.00 / 1M	$2.50 / 1M
Output Price Cost per million output tokens	$6.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.70 / 1M	$2.38 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	75 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	38%	38.8%
GPQA Diamond Graduate-level reasoning	54%	53.6%
Cached Input Price Prompt caching discount per 1M tokens	$1.00 / 1M	$1.25 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

GPT-4o

+0.8% SWE-bench lead

Cost

Mistral Large 2

1.5x cheaper monthly runtime

Speed

GPT-4o

+40% faster generation

Context

Tied

128K tokens window

Punteggi Benchmark Verificati

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

Mistral Large 2GPT-4o

SWE-bench Verified (Autonomous Coding)

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

38%vs38.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

54%vs53.6%

MMLU-Pro (Multi-task Language Understanding)

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

76.5%vs72.6%

LMSYS Chatbot Arena Elo

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

1282ptsvs1286pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs4.1%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs18.5%

SciCode (Scientific Computing)

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

N/Avs27.3%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs43.9%

AIME (Advanced Math Competition)

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

N/Avs29.7%

Long-Context Reasoning

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

N/Avs58.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs105 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

GPT-4o0.41s

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

Mistral Large 27.19s

GPT-4o5.17s

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

GPT-4o Monthly API Bill

$14.63 / month

$2.5/1M in · $10/1M out

Mistral Large 2 saves approximately $4.72 / month (32%) under this workload.1.5x 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

GPT-4o · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 16K token (SWE-bench Verified 38.8% · GPQA Diamond 53.6%)
Throughput elevato di 105 token/secondo con latenza al primo token (TTFT) di soli 410 ms
Economia API competitiva ($2.5/$10 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Token Use & Task Economics

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

Metric	Mistral Large 2	GPT-4o
Cost per Task Estimated USD to finish one task	$0.20	$0.68
Output Tokens / Task Tokens generated per task	24K	24K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Large 2	GPT-4o
Official API Model ID (SDK)	mistral-large-2-latest	gpt-4o
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2024-07	2024-05
Knowledge Cutoff	July 2024	October 2023
Architecture	123B Dense Frontier Transformer	Omni-Multimodal Dense
Parameters	123B	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($2/1M in) across full 128K window	Flat rate ($2.5/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $1/1M tokens	Automatic prefix caching at $1.25/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
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)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision, audio
Output Modalities	text	text, audio

Mistral Large 2 vs GPT-4o: Risposte Rapide (FAQ)

Which is more intelligent, Mistral Large 2 or GPT-4o?

Mistral Large 2 is more intelligent. It scores 46 compared with 41 on the composite Intelligence Index.

Which model is faster?

GPT-4o is faster, generating 105 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Mistral Large 2 is cheaper at $1.70 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.52s.

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 GPT-4o (Ideale per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Mistral Large 2 guida nei benchmark di ragionamento e programmazione, GPT-4o garantisce una generazione più rapida e Mistral Large 2 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 GPT-4o?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mistral Large 2. 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 GPT-4o?+

Mistral Large 2 costa $2.00 / $6.00 per milione di token di input/output, mentre GPT-4o costa $2.50 / $10.00. Nel costo combinato, Mistral Large 2 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 128K token di GPT-4o, mentre GPT-4o 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 GPT-4o

Cambia e Confronta Qualsiasi Modello

Mistral Large 2

GPT-4o

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

GPT-4o · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Mistral Large 2 vs GPT-4o: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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