Head-to-Head Model Comparison

Mistral Saba vs GPT-4.1

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-4.1Profile

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-4.1

Modello Ammiraglia di Frontiera

GPT-4.1 di OpenAI è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1.0M, velocità di 110 token/s, indice di intelligenza di 48/100 e

Intelligence Index48 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

54.6%

Differenze Chiave a Colpo d’Occhio

Mistral Saba is 12.0x cheaper for API usage (input + output tokens combined).
GPT-4.1 has a larger context window (1M vs 33K tokens).
GPT-4.1 is faster at 110 tok/s vs 80 tok/s.
GPT-4.1 leads on SWE-bench coding (54.6% vs 38.0%).

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

Feature	Mistral Saba	GPT-4.1
Intelligence Index Composite reasoning & knowledge score (0-100)	40	48
Provider	Mistral AI	OpenAI
Input Price Cost per million input tokens	$0.20 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$8.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$1.55 / 1M
Context Window Maximum input + output tokens	32.8K tokens~49 A4 pages of text	1.05M tokens~1,571 A4 pages of text
Max Output Tokens Per single request	8K	33K
Throughput Speed Tokens generated per second	80 tok/s	110 tok/s
SWE-bench Verified Real-world software engineering	38%	54.6%
GPQA Diamond Graduate-level reasoning	46.5%	65%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.50 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

GPT-4.1

+16.6% SWE-bench lead

Cost

Mistral Saba

12.0x cheaper monthly runtime

Speed

GPT-4.1

+38% faster generation

Context

GPT-4.1

1.05M tokens window

Punteggi Benchmark Verificati

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

Mistral SabaGPT-4.1

SWE-bench Verified (Autonomous Coding)

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

38%vs54.6%

GPQA Diamond (Doctorate-Level Science & Math)

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

46.5%vs65%

MMLU-Pro (Multi-task Language Understanding)

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

65.8%vs81%

LMSYS Chatbot Arena Elo

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

1260ptsvs1338pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

5.2%vs9.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

24.5%vs30%

SciCode (Scientific Computing)

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

24%vs36.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

41%vs58.5%

AIME (Advanced Math Competition)

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

48%vs55.5%

Long-Context Reasoning

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

59%vs70%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs110 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-4.10.48s

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

Mistral Saba6.71s

GPT-4.15.03s

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-4.1 Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

Mistral Saba saves approximately $10.04 / month (92%) under this workload.12.0x 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-4.1 · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 33K token (SWE-bench Verified 54.6% · GPQA Diamond 65%)
Throughput elevato di 110 token/secondo con latenza al primo token (TTFT) di soli 480 ms
Economia API competitiva ($2/$8 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-4.1
Cost per Task Estimated USD to finish one task	$0.18	$0.72
Output Tokens / Task Tokens generated per task	24K	52K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Saba	GPT-4.1
Official API Model ID (SDK)	mistral-saba-latest	gpt-4-1
API Protocol	Mistral AI REST API (OpenAI-Compatible)	OpenAI Responses API & Chat Completions v1
Release Date	2025-02	2025-04
Knowledge Cutoff	January 2025	June 2024
Architecture	Multilingual Dense Transformer	Dense multimodal 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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.2/1M in) across full 33K window	Flat rate ($2/1M in) across full 1048K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Automatic prefix caching at $0.5/1M (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-4.1: Risposte Rapide (FAQ)

Which is more intelligent, Mistral Saba or GPT-4.1?

GPT-4.1 is more intelligent. It scores 48 compared with 40 on the composite Intelligence Index.

Which model is faster?

GPT-4.1 is faster, generating 110 tokens per second compared with 80 tokens per second.

Which model is cheaper to run?

Mistral Saba is cheaper at $0.14 per 1M tokens blended, compared with $1.55 (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 0.48s.

Which model has a larger context window?

GPT-4.1 has a larger context window, supporting 1.05M 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-4.1 (Ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GPT-4.1 guida nei benchmark di ragionamento e programmazione, GPT-4.1 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-4.1?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT-4.1. 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-4.1?+

Mistral Saba costa $0.20 / $0.60 per milione di token di input/output, mentre GPT-4.1 costa $2.00 / $8.00. 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 1.05M token di GPT-4.1, mentre GPT-4.1 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-4.1

Cambia e Confronta Qualsiasi Modello

Mistral Saba

GPT-4.1

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-4.1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Mistral Saba vs GPT-4.1: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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