Head-to-Head Model Comparison

o3 vs Magistral Medium

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.

o3Profile

Magistral MediumProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

o3

Motore di Ragionamento Profondo

o3 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 200K, velocità di 75 token/s, indice di intelligenza di 56/100 e tariffa di $2/$8 pe

Intelligence Index56 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

69.1%

Mistral AI

Magistral Medium

Motore di Ragionamento Profondo

Magistral Medium di Mistral AI 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 50 token/s, indice di intelligenza di 51/100 e t

Intelligence Index51 / 100

Input Token:

$1.00 / 1M

Output Token:

$3.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

45.2%

Differenze Chiave a Colpo d’Occhio

Magistral Medium is 2.2x cheaper for API usage (input + output tokens combined).
o3 has a larger context window (200K vs 128K tokens).
o3 is faster at 75 tok/s vs 50 tok/s.
o3 leads on SWE-bench coding (69.1% vs 45.2%).

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

Feature	o3	Magistral Medium
Intelligence Index Composite reasoning & knowledge score (0-100)	56	51
Provider	OpenAI	Mistral AI
Input Price Cost per million input tokens	$2.00 / 1M	$1.00 / 1M
Output Price Cost per million output tokens	$8.00 / 1M	$3.50 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.55 / 1M	$0.72 / 1M
Context Window Maximum input + output tokens	200K tokens~300 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	100K	33K
Throughput Speed Tokens generated per second	75 tok/s	50 tok/s
SWE-bench Verified Real-world software engineering	69.1%	45.2%
GPQA Diamond Graduate-level reasoning	76.5%	67.8%
Cached Input Price Prompt caching discount per 1M tokens	$0.50 / 1M	$0.25 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

+23.9% SWE-bench lead

Cost

Magistral Medium

2.2x cheaper monthly runtime

Speed

+50% faster generation

Context

200K tokens window

Punteggi Benchmark Verificati

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

o3Magistral Medium

SWE-bench Verified (Autonomous Coding)

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

69.1%vs45.2%

GPQA Diamond (Doctorate-Level Science & Math)

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

76.5%vs67.8%

MMLU-Pro (Multi-task Language Understanding)

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

87%vs82.5%

LMSYS Chatbot Arena Elo

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

1345ptsvs1335pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

12.9%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

42%vs40%

SciCode (Scientific Computing)

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

42.5%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

70.5%vsN/A

AIME (Advanced Math Competition)

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

91.6%vs76.5%

Long-Context Reasoning

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

78.5%vs80%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs50 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

o32.80s

Magistral Medium0.85s

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

o39.47s

Magistral Medium10.85s

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)

o3 Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

Magistral Medium Monthly API Bill

$5.03 / month

$1/1M in · $3.5/1M out

Magistral Medium saves approximately $5.92 / month (54%) under this workload.2.2x Savings

o3 · Strengths & Key Advantages

Finestra di contesto da 200K con capacità massima di output fino a 100K token (SWE-bench Verified 69.1% · GPQA Diamond 76.5%)
Throughput elevato di 75 token/secondo con latenza al primo token (TTFT) di soli 2800 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

Magistral Medium · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 33K token (SWE-bench Verified 45.2% · GPQA Diamond 67.8%)
Throughput elevato di 50 token/secondo con latenza al primo token (TTFT) di soli 850 ms
Economia API competitiva ($1/$3.5 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	o3	Magistral Medium
Cost per Task Estimated USD to finish one task	$1.35	$0.65
Output Tokens / Task Tokens generated per task	48K	40K
Reasoning Tokens / Task Thinking tokens burned per task	26K	22K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	o3	Magistral Medium
Official API Model ID (SDK)	o3	magistral-medium-latest
API Protocol	OpenAI Responses API & Chat Completions v1	Mistral AI REST API (OpenAI-Compatible)
Release Date	2025-04	2025-03
Knowledge Cutoff	June 2024	January 2025
Architecture	Reasoning transformer with adjustable thinking effort	Reinforcement-Learned Reasoning Transformer
Parameters	Confidential	Confidential (~70B)
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($2/1M in) across full 200K window	Flat rate ($1/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.5/1M (no extra cache-write fee)	Cached input at $0.25/1M tokens
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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	Native Web Search tool + Hosted Code Interpreter & File Search RAG	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	API Prompt Engineering & RAG Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token)
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	OpenAI Preparedness Framework Audited & System Card Published	Mistral AI Production Safety & Alignment Verified
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, vision	text
Output Modalities	text	text

o3 vs Magistral Medium: Risposte Rapide (FAQ)

Which is more intelligent, o3 or Magistral Medium?

o3 is more intelligent. It scores 56 compared with 51 on the composite Intelligence Index.

Which model is faster?

o3 is faster, generating 75 tokens per second compared with 50 tokens per second.

Which model is cheaper to run?

Magistral Medium is cheaper at $0.72 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Magistral Medium has lower latency, with a time to first token of 0.85s compared with 2.80s.

Which model has a larger context window?

o3 has a larger context window, supporting 200K tokens compared with 128K tokens.

Verdetto

Sia o3 (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) sia Magistral Medium (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: o3 guida nei benchmark di ragionamento e programmazione, o3 garantisce una generazione più rapida e Magistral Medium offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, o3 o Magistral Medium?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale o3. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra o3 e Magistral Medium?+

o3 costa $2.00 / $8.00 per milione di token di input/output, mentre Magistral Medium costa $1.00 / $3.50. Nel costo combinato, Magistral Medium risulta più economico.

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

o3 supporta 200K token di contesto contro 128K token di Magistral Medium, mentre o3 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)

o3 vs Magistral Medium

Cambia e Confronta Qualsiasi Modello

o3

Magistral Medium

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

o3 · Strengths & Key Advantages

Considerations & Trade-offs

Magistral Medium · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

o3 vs Magistral Medium: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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