Head-to-Head Model Comparison

Mistral Saba vs GPT-4.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia e Confronta Qualsiasi Modello

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

Mistral SabaProfile

GPT-4.5Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Mistral Saba

Modello Multilingue Globale

Mistral Saba di Mistral AI è un modello di IA progettato per la traduzione multilingue ad alta precisione e la comunicazione aziendale globale, con finestra di contesto da 33K, velocità di 80 token/s, indice di intelligenza di 40/100 e tari

Intelligence Index40 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

38%

OpenAI

GPT-4.5

Motore di Ragionamento Profondo

GPT-4.5 di OpenAI è un modello di IA progettato per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, con finestra di contesto da 128K, velocità di 45 token/s, indice di intelligenza di 46/100 e tariffa di $75

Intelligence Index46 / 100

Input Token:

$75.00 / 1M

Output Token:

$150.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

42%

Differenze Chiave a Colpo d’Occhio

Mistral Saba is 301.2x cheaper for API usage (input + output tokens combined).
GPT-4.5 has a larger context window (128K vs 33K tokens).
Mistral Saba is faster at 80 tok/s vs 45 tok/s.
GPT-4.5 leads on SWE-bench coding (42.0% vs 38.0%).

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

Feature	Mistral Saba	GPT-4.5
Intelligence Index Composite reasoning & knowledge score (0-100)	40	46
Provider	Mistral AI	OpenAI
Input Price Cost per million input tokens	$0.20 / 1M	$75.00 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$150.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$43.13 / 1M
Context Window Maximum input + output tokens	32.8K tokens~49 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	80 tok/s	45 tok/s
SWE-bench Verified Real-world software engineering	38%	42%
GPQA Diamond Graduate-level reasoning	46.5%	66.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$18.75 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

GPT-4.5

+4.0% SWE-bench lead

Cost

Mistral Saba

301.2x cheaper monthly runtime

Speed

Mistral Saba

+78% faster generation

Context

GPT-4.5

128K tokens window

Punteggi Benchmark Verificati

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

Mistral SabaGPT-4.5

SWE-bench Verified (Autonomous Coding)

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

38%vs42%

GPQA Diamond (Doctorate-Level Science & Math)

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

46.5%vs66.5%

MMLU-Pro (Multi-task Language Understanding)

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

65.8%vs81.5%

LMSYS Chatbot Arena Elo

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

1260ptsvs1332pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

5.2%vs9.4%

Terminal-Bench (Agentic Coding & Terminal Use)

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

24.5%vs25%

SciCode (Scientific Computing)

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

24%vs34.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

41%vs56%

AIME (Advanced Math Competition)

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

48%vs60%

Long-Context Reasoning

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

59%vs68%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs45 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 Saba0.46s

GPT-4.51.60s

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

Mistral Saba6.71s

GPT-4.512.71s

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 Saba Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

GPT-4.5 Monthly API Bill

$275.63 / month

$75/1M in · $150/1M out

Mistral Saba saves approximately $274.71 / month (100%) under this workload.301.2x Savings

Mistral Saba · Strengths & Key Advantages

Finestra di contesto da 33K con capacità massima di output fino a 8K token (SWE-bench Verified 38% · GPQA Diamond 46.5%)
Throughput elevato di 80 token/secondo con latenza al primo token (TTFT) di soli 460 ms
Economia API competitiva ($0.2/$0.6 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
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

GPT-4.5 · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 16K token (SWE-bench Verified 42% · GPQA Diamond 66.5%)
Throughput elevato di 45 token/secondo con latenza al primo token (TTFT) di soli 1600 ms
Economia API competitiva ($75/$150 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 Saba	GPT-4.5
Cost per Task Estimated USD to finish one task	$0.18	$17.85
Output Tokens / Task Tokens generated per task	24K	55K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Saba	GPT-4.5
Official API Model ID (SDK)	mistral-saba-latest	gpt-4-5
API Protocol	Mistral AI REST API (OpenAI-Compatible)	OpenAI Responses API & Chat Completions v1
Release Date	2025-02	2025-02
Knowledge Cutoff	January 2025	October 2023
Architecture	Multilingual Dense Transformer	Large dense pre-reasoning transformer
Parameters	24B	Confidential
Enterprise Rate Limits (RPM / TPM)	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.2/1M in) across full 33K window	Flat rate ($75/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Automatic prefix caching at $18.75/1M (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	API Prompt Engineering & RAG Distillation	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)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Mistral AI Production Safety & Alignment Verified	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision
Output Modalities	text	text

Mistral Saba vs GPT-4.5: Risposte Rapide (FAQ)

Which is more intelligent, Mistral Saba or GPT-4.5?

GPT-4.5 is more intelligent. It scores 46 compared with 40 on the composite Intelligence Index.

Which model is faster?

Mistral Saba is faster, generating 80 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

Mistral Saba is cheaper at $0.14 per 1M tokens blended, compared with $43.13 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Saba has lower latency, with a time to first token of 0.46s compared with 1.60s.

Which model has a larger context window?

GPT-4.5 has a larger context window, supporting 128K tokens compared with 32.8K tokens.

Verdetto

Sia Mistral Saba (Ideale per la traduzione multilingue ad alta precisione e la comunicazione aziendale globale, sistemi RAG aziendali e integrazioni API scalabili.) sia GPT-4.5 (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GPT-4.5 guida nei benchmark di ragionamento e programmazione, Mistral Saba garantisce una generazione più rapida e Mistral Saba offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Mistral Saba o GPT-4.5?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT-4.5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Mistral Saba e GPT-4.5?+

Mistral Saba costa $0.20 / $0.60 per milione di token di input/output, mentre GPT-4.5 costa $75.00 / $150.00. Nel costo combinato, Mistral Saba risulta più economico.

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

Mistral Saba supporta 32.8K token di contesto contro 128K token di GPT-4.5, mentre Mistral Saba 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 Saba vs GPT-4.5

Cambia e Confronta Qualsiasi Modello

Mistral Saba

GPT-4.5

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Mistral Saba · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Mistral Saba vs GPT-4.5: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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