Head-to-Head Model Comparison

Mistral Small 4 vs GPT-4.1 mini

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 Small 4Profile

GPT-4.1 miniProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Mistral Small 4

Inferenza Ultra-Rapida

Mistral Small 4 di Mistral AI è un modello di IA progettato per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, con finestra di contesto da 256K, velocità di 120 token/s, indice di intelligenza di 48/100 e ta

Intelligence Index48 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

256K tokens

SWE-bench Verified:

44.5%

OpenAI

GPT-4.1 mini

Modello Leggero ed Economico

GPT-4.1 mini di OpenAI è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 1.0M, velocità di 165 token/s, indice di intelligenza di 36/100 e tariffa di

Intelligence Index36 / 100

Input Token:

$0.40 / 1M

Output Token:

$1.60 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

36.5%

Differenze Chiave a Colpo d’Occhio

Mistral Small 4 is 4.7x cheaper for API usage (input + output tokens combined).
GPT-4.1 mini has a larger context window (1M vs 256K tokens).
GPT-4.1 mini is faster at 165 tok/s vs 120 tok/s.
Mistral Small 4 leads on SWE-bench coding (44.5% vs 36.5%).

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

Feature	Mistral Small 4	GPT-4.1 mini
Intelligence Index Composite reasoning & knowledge score (0-100)	48	36
Provider	Mistral AI	OpenAI
Input Price Cost per million input tokens	$0.10 / 1M	$0.40 / 1M
Output Price Cost per million output tokens	$0.30 / 1M	$1.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.07 / 1M	$0.31 / 1M
Context Window Maximum input + output tokens	256K tokens~384 A4 pages of text	1.05M tokens~1,571 A4 pages of text
Max Output Tokens Per single request	16K	33K
Throughput Speed Tokens generated per second	120 tok/s	165 tok/s
SWE-bench Verified Real-world software engineering	44.5%	36.5%
GPQA Diamond Graduate-level reasoning	56%	49.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.10 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Mistral Small 4

+8.0% SWE-bench lead

Cost

Mistral Small 4

4.7x cheaper monthly runtime

Speed

GPT-4.1 mini

+38% faster generation

Context

GPT-4.1 mini

1.05M tokens window

Punteggi Benchmark Verificati

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

Mistral Small 4GPT-4.1 mini

SWE-bench Verified (Autonomous Coding)

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

44.5%vs36.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

56%vs49.5%

MMLU-Pro (Multi-task Language Understanding)

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

78%vs68%

LMSYS Chatbot Arena Elo

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

1315ptsvs1258pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs6.2%

Terminal-Bench (Agentic Coding & Terminal Use)

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

38%vs19%

SciCode (Scientific Computing)

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

N/Avs24.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs38%

AIME (Advanced Math Competition)

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

66%vs42%

Long-Context Reasoning

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

80%vs57.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

120 tok/s vs165 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 40.34s

GPT-4.1 mini0.30s

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

Mistral Small 44.51s

GPT-4.1 mini3.33s

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 4 Monthly API Bill

$0.47 / month

$0.1/1M in · $0.3/1M out

GPT-4.1 mini Monthly API Bill

$2.19 / month

$0.4/1M in · $1.6/1M out

Mistral Small 4 saves approximately $1.73 / month (79%) under this workload.4.7x Savings

Mistral Small 4 · Strengths & Key Advantages

Finestra di contesto da 256K con capacità massima di output fino a 16K token (SWE-bench Verified 44.5% · GPQA Diamond 56%)
Throughput elevato di 120 token/secondo con latenza al primo token (TTFT) di soli 340 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

GPT-4.1 mini · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 33K token (SWE-bench Verified 36.5% · GPQA Diamond 49.5%)
Throughput elevato di 165 token/secondo con latenza al primo token (TTFT) di soli 300 ms
Economia API competitiva ($0.4/$1.6 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

Token Use & Task Economics

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

Metric	Mistral Small 4	GPT-4.1 mini
Cost per Task Estimated USD to finish one task	$0.12	$0.14
Output Tokens / Task Tokens generated per task	28K	48K
Reasoning Tokens / Task Thinking tokens burned per task	12K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Small 4	GPT-4.1 mini
Official API Model ID (SDK)	mistral-small-4-latest	gpt-4-1-mini
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2026-03	2025-04
Knowledge Cutoff	January 2026	June 2024
Architecture	Next-Gen Compact Multimodal Transformer	Dense multimodal transformer (small)
Parameters	24B	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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.1/1M in) across full 256K window	Flat rate ($0.4/1M in) across full 1048K window
Prompt Cache Policy & TTL	Cached input at $0.03/1M tokens	Automatic prefix caching at $0.1/1M (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) · 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)	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, vision	text, vision
Output Modalities	text	text

Mistral Small 4 vs GPT-4.1 mini: Risposte Rapide (FAQ)

Which is more intelligent, Mistral Small 4 or GPT-4.1 mini?

Mistral Small 4 is more intelligent. It scores 48 compared with 36 on the composite Intelligence Index.

Which model is faster?

GPT-4.1 mini is faster, generating 165 tokens per second compared with 120 tokens per second.

Which model is cheaper to run?

Mistral Small 4 is cheaper at $0.07 per 1M tokens blended, compared with $0.31 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4.1 mini has lower latency, with a time to first token of 0.30s compared with 0.34s.

Which model has a larger context window?

GPT-4.1 mini has a larger context window, supporting 1.05M tokens compared with 256K tokens.

Verdetto

Sia Mistral Small 4 (Ideale per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, sistemi RAG aziendali e integrazioni API scalabili.) sia GPT-4.1 mini (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Mistral Small 4 guida nei benchmark di ragionamento e programmazione, GPT-4.1 mini garantisce una generazione più rapida e Mistral Small 4 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Mistral Small 4 o GPT-4.1 mini?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mistral Small 4. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Mistral Small 4 e GPT-4.1 mini?+

Mistral Small 4 costa $0.10 / $0.30 per milione di token di input/output, mentre GPT-4.1 mini costa $0.40 / $1.60. Nel costo combinato, Mistral Small 4 risulta più economico.

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

Mistral Small 4 supporta 256K token di contesto contro 1.05M token di GPT-4.1 mini, mentre GPT-4.1 mini 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 4 vs GPT-4.1 mini

Cambia e Confronta Qualsiasi Modello

Mistral Small 4

GPT-4.1 mini

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Mistral Small 4 · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4.1 mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Mistral Small 4 vs GPT-4.1 mini: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

Related reading