Head-to-Head Model Comparison

Mistral Medium 3.5 vs Claude Sonnet 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia e Confronta Qualsiasi Modello

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Mistral Medium 3.5Profile

Claude Sonnet 5.5Profile

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Popular Matchups:

Mistral AI

Mistral Medium 3.5

Modello Base a Pesi Aperti

Mistral Medium 3.5 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 262K, velocità di 90 token/s, indice di intelligenza di

Intelligence Index55 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

54%

Anthropic

Claude Sonnet 5.5

Modello Ammiraglia di Frontiera

Claude Sonnet 5.5 di Anthropic 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 1M, velocità di 105 token/s, indice di intelligenza d

Intelligence Index78 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.9%

Differenze Chiave a Colpo d’Occhio

Mistral Medium 3.5 is 4.8x cheaper for API usage (input + output tokens combined).
Claude Sonnet 5.5 has a larger context window (1M vs 262K tokens).
Claude Sonnet 5.5 is faster at 105 tok/s vs 90 tok/s.
Claude Sonnet 5.5 leads on SWE-bench coding (82.9% vs 54.0%).

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

Feature	Mistral Medium 3.5	Claude Sonnet 5.5
Intelligence Index Composite reasoning & knowledge score (0-100)	55	78
Provider	Mistral AI	Anthropic
Input Price Cost per million input tokens	$0.40 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$2.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.35 / 1M	$1.54 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	33K	128K
Throughput Speed Tokens generated per second	90 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	54%	82.9%
GPQA Diamond Graduate-level reasoning	68%	79.4%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.20 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Claude Sonnet 5.5

+28.9% SWE-bench lead

Cost

Mistral Medium 3.5

4.8x cheaper monthly runtime

Speed

Claude Sonnet 5.5

+17% faster generation

Context

Claude Sonnet 5.5

1M tokens window

Punteggi Benchmark Verificati

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

Mistral Medium 3.5Claude Sonnet 5.5

SWE-bench Verified (Autonomous Coding)

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

54%vs82.9%

GPQA Diamond (Doctorate-Level Science & Math)

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

68%vs79.4%

MMLU-Pro (Multi-task Language Understanding)

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

84.5%vs90.1%

LMSYS Chatbot Arena Elo

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

1370ptsvs1490pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

46%vs58.9%

AIME (Advanced Math Competition)

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

78%vsN/A

Long-Context Reasoning

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

85%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 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 Medium 3.50.45s

Claude Sonnet 5.50.55s

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

Mistral Medium 3.56.01s

Claude Sonnet 5.55.31s

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 Medium 3.5 Monthly API Bill

$2.55 / month

$0.4/1M in · $2/1M out

Claude Sonnet 5.5 Monthly API Bill

$12.30 / month

$2/1M in · $10/1M out

Mistral Medium 3.5 saves approximately $9.75 / month (79%) under this workload.4.8x Savings

Mistral Medium 3.5 · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 33K token (SWE-bench Verified 54% · GPQA Diamond 68%)
Throughput elevato di 90 token/secondo con latenza al primo token (TTFT) di soli 450 ms
Pesi aperti (Confidential (~80B MoE)) 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

Claude Sonnet 5.5 · Strengths & Key Advantages

Finestra di contesto da 1M con capacità massima di output fino a 128K token (SWE-bench Verified 82.9% · GPQA Diamond 79.4%)
Throughput elevato di 105 token/secondo con latenza al primo token (TTFT) di soli 550 ms
Economia API competitiva ($2/$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 Medium 3.5	Claude Sonnet 5.5
Cost per Task Estimated USD to finish one task	$0.38	$3.29
Output Tokens / Task Tokens generated per task	38K	72K
Reasoning Tokens / Task Thinking tokens burned per task	18K	37K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Medium 3.5	Claude Sonnet 5.5
Official API Model ID (SDK)	mistral-medium-3-5-latest	claude-sonnet-5-5-20260901
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date	2026-04	2026-09
Knowledge Cutoff	February 2026	June 2026
Architecture	Enterprise Hybrid Reasoning MoE	Adaptive Frontier Multimodal Transformer
Parameters	Confidential (~80B MoE)	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.4/1M in) across full 262K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Cached input at $0.1/1M tokens	$0.2/1M cached read (90% off); 5-min default & 1-hour extended TTL
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 Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Custom Model Tuning via Amazon Bedrock & Prompt Distillation
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) · 50 GB VRAM (4-bit Quantized) · 172 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)	Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
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 Medium 3.5 vs Claude Sonnet 5.5: Risposte Rapide (FAQ)

Which is more intelligent, Mistral Medium 3.5 or Claude Sonnet 5.5?

Claude Sonnet 5.5 is more intelligent. It scores 78 compared with 55 on the composite Intelligence Index.

Which model is faster?

Claude Sonnet 5.5 is faster, generating 105 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

Mistral Medium 3.5 is cheaper at $0.35 per 1M tokens blended, compared with $1.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Medium 3.5 has lower latency, with a time to first token of 0.45s compared with 0.55s.

Which model has a larger context window?

Claude Sonnet 5.5 has a larger context window, supporting 1M tokens compared with 262.1K tokens.

Verdetto

Sia Mistral Medium 3.5 (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 Claude Sonnet 5.5 (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: Claude Sonnet 5.5 guida nei benchmark di ragionamento e programmazione, Claude Sonnet 5.5 garantisce una generazione più rapida e Mistral Medium 3.5 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Mistral Medium 3.5 o Claude Sonnet 5.5?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Claude Sonnet 5.5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Mistral Medium 3.5 e Claude Sonnet 5.5?+

Mistral Medium 3.5 costa $0.40 / $2.00 per milione di token di input/output, mentre Claude Sonnet 5.5 costa $2.00 / $10.00. Nel costo combinato, Mistral Medium 3.5 risulta più economico.

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

Mistral Medium 3.5 supporta 262.1K token di contesto contro 1M token di Claude Sonnet 5.5, mentre Claude Sonnet 5.5 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 Medium 3.5 vs Claude Sonnet 5.5

Cambia e Confronta Qualsiasi Modello

Mistral Medium 3.5

Claude Sonnet 5.5

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Mistral Medium 3.5 · Strengths & Key Advantages

Considerations & Trade-offs

Claude Sonnet 5.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Mistral Medium 3.5 vs Claude Sonnet 5.5: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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