Head-to-Head Model Comparison

Mixtral 8x7B vs Mistral 7B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Mixtral 8x7B vs Mistral 7B: Benchmarks, Pricing & Speed Comparison

Mixtral 8x7B di Mistral AI e Mistral 7B di Mistral AI sono due modelli di IA di riferimento. Mixtral 8x7B offre una finestra di contesto di 32.8K token a $0.20/$0.20 per milione di token, mentre Mistral 7B mette a disposizione 32.8K token di contesto a $0.06/$0.06 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

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Mistral AI

Mixtral 8x7B

Modello Base a Pesi Aperti

Mixtral 8x7B 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 33K, velocità di 130 token/s, indice di intelligenza di 37/100

Intelligence Index37 / 100
Input Token:

$0.20 / 1M

Output Token:

$0.20 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

Mistral AI

Mistral 7B

Inferenza Ultra-Rapida

Mistral 7B 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 33K, velocità di 200 token/s, indice di intelligenza di 32/100 e tariffa

Intelligence Index32 / 100
Input Token:

$0.06 / 1M

Output Token:

$0.06 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

  • Mistral 7B is 3.3x cheaper for API usage (input + output tokens combined).
  • Mistral 7B is faster at 200 tok/s vs 130 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMixtral 8x7BMistral 7B
Intelligence Index
Composite reasoning & knowledge score (0-100)
3732
Provider
Mistral AIMistral AI
Input Price
Cost per million input tokens
$0.20 / 1M$0.06 / 1M
Output Price
Cost per million output tokens
$0.20 / 1M$0.06 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.10 / 1M$0.03 / 1M
Context Window
Maximum input + output tokens
32.8K tokens~49 A4 pages of text32.8K tokens~49 A4 pages of text
Max Output Tokens
Per single request
4K4K
Throughput Speed
Tokens generated per second
130 tok/s200 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
GPQA Diamond
Graduate-level reasoning
38%N/A
Cached Input Price
Prompt caching discount per 1M tokens
$0.05 / 1M$0.01 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Mistral 7B
3.3x cheaper monthly runtime
Speed
Mistral 7B
+54% faster generation
Context
Tied
32.8K tokens window

Punteggi Benchmark Verificati

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

Mixtral 8x7BMistral 7B
GPQA Diamond (Doctorate-Level Science & Math)

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

38%vsN/A
MMLU-Pro (Multi-task Language Understanding)

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

66.5%vs56%
LMSYS Chatbot Arena Elo

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

1195ptsvs1120pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 tok/s vs200 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
Mixtral 8x7B0.38s
Mistral 7B0.24s
Est. End-to-End Response (500 tokens)TTFT + generation time
Mixtral 8x7B4.23s
Mistral 7B2.74s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Mixtral 8x7B Monthly API Bill
$0.56 / month
$0.2/1M in · $0.2/1M out
Mistral 7B Monthly API Bill
$0.17 / month
$0.06/1M in · $0.06/1M out
Mistral 7B saves approximately $0.39 / month (70%) under this workload.3.3x Savings

Mixtral 8x7B · Strengths & Key Advantages

  • Finestra di contesto da 33K con capacità massima di output fino a 4K token (GPQA Diamond 38% · MMLU-Pro 66.5%)
  • Throughput elevato di 130 token/secondo con latenza al primo token (TTFT) di soli 380 ms
  • Pesi aperti (46.7B (12.9B )) 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

Mistral 7B · Strengths & Key Advantages

  • Finestra di contesto da 33K con capacità massima di output fino a 4K token (MMLU-Pro 56% · Arena Elo 1120)
  • Throughput elevato di 200 token/secondo con latenza al primo token (TTFT) di soli 240 ms
  • Pesi aperti (7.3B) 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

Token Use & Task Economics

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

MetricMixtral 8x7BMistral 7B
Cost per Task
Estimated USD to finish one task
$0.20$0.20
Output Tokens / Task
Tokens generated per task
24K24K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureMixtral 8x7BMistral 7B
Official API Model ID (SDK)mixtral-8x7b-latestmistral-7b-latest
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2023-122023-09
Knowledge CutoffLate 2023Mid 2023
ArchitectureSparse Mixture of Experts (MoE, 2 active of 8)Sliding Window Attention (SWA) Transformer
Parameters46.7B (12.9B active)7.3B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.2/1M in) across full 33K windowFlat rate ($0.06/1M in) across full 33K window
Prompt Cache Policy & TTLCached input at $0.05/1M tokensCached input at $0.015/1M tokens
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Text-only completion (No GUI action loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaPrompt-based JSON extraction only
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 29 GB VRAM (4-bit Quantized) · 100 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 16 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Mixtral 8x7B vs Mistral 7B: Risposte Rapide (FAQ)

Which is more intelligent, Mixtral 8x7B or Mistral 7B?

Mixtral 8x7B is more intelligent. It scores 37 compared with 32 on the composite Intelligence Index.

Which model is faster?

Mistral 7B is faster, generating 200 tokens per second compared with 130 tokens per second.

Which model is cheaper to run?

Mistral 7B is cheaper at $0.03 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral 7B has lower latency, with a time to first token of 0.24s compared with 0.38s.

Verdetto

Sia Mixtral 8x7B (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 Mistral 7B (Ideale per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Mixtral 8x7B guida nei benchmark di ragionamento e programmazione, Mistral 7B garantisce una generazione più rapida e Mistral 7B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Mixtral 8x7B o Mistral 7B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mixtral 8x7B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Mixtral 8x7B e Mistral 7B?+

Mixtral 8x7B costa $0.20 / $0.20 per milione di token di input/output, mentre Mistral 7B costa $0.06 / $0.06. Nel costo combinato, Mistral 7B risulta più economico.

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

Mixtral 8x7B supporta 32.8K token di contesto contro 32.8K token di Mistral 7B, mentre Mistral 7B 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.

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Fonti ufficiali: Documentazione API ufficiale di Mistral AI e Mistral AI, schede di sistema e benchmark indipendenti (2026).