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Head-to-Head Model Comparison

Mistral NeMo vs Mixtral 8x22B

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
Mistral NeMo vs Mixtral 8x22B: Benchmarks, Pricing & Speed Comparison

Mistral NeMo di Mistral AI e Mixtral 8x22B di Mistral AI sono due modelli di IA di riferimento. Mistral NeMo offre una finestra di contesto di 128K token a $0.15/$0.15 per milione di token, mentre Mixtral 8x22B mette a disposizione 65.5K token di contesto a $1.20/$1.20 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

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Mistral AI

Mistral NeMo

Modello Base a Pesi Aperti

Mistral NeMo 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 128K, velocità di 90 token/s, indice di intelligenza di 39/100

Intelligence Index39 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.15 / 1M

Context Window:

128K tokens

SWE-bench Verified:

29%

Mistral AI

Mixtral 8x22B

Modello Base a Pesi Aperti

Mixtral 8x22B 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 66K, velocità di 55 token/s, indice di intelligenza di 44/100

Intelligence Index44 / 100
Input Token:

$1.20 / 1M

Output Token:

$1.20 / 1M

Context Window:

65.5K tokens

SWE-bench Verified:

36%

Differenze Chiave a Colpo d’Occhio

  • Mistral NeMo is 8.7x cheaper for API usage (input + output tokens combined).
  • Mistral NeMo has a larger context window (128K vs 66K tokens).
  • Mistral NeMo is faster at 90 tok/s vs 55 tok/s.
  • Mixtral 8x22B leads on SWE-bench coding (36.0% vs 29.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMistral NeMoMixtral 8x22B
Intelligence Index
Composite reasoning & knowledge score (0-100)
3944
Provider
Mistral AIMistral AI
Input Price
Cost per million input tokens
$0.15 / 1M$1.20 / 1M
Output Price
Cost per million output tokens
$0.15 / 1M$1.20 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.08 / 1M$0.78 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text65.5K tokens~98 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
90 tok/s55 tok/s
SWE-bench Verified
Real-world software engineering
29%36%
GPQA Diamond
Graduate-level reasoning
44.5%51.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.05 / 1M$0.60 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
Mixtral 8x22B
+7.0% SWE-bench lead
Cost
Mistral NeMo
8.7x cheaper monthly runtime
Speed
Mistral NeMo
+64% faster generation
Context
Mistral NeMo
128K tokens window

Punteggi Benchmark Verificati

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

Mistral NeMoMixtral 8x22B
SWE-bench Verified (Autonomous Coding)

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

29%vs36%
GPQA Diamond (Doctorate-Level Science & Math)

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

44.5%vs51.2%
MMLU-Pro (Multi-task Language Understanding)

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

68.2%vs74%
LMSYS Chatbot Arena Elo

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

1220ptsvs1260pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

25%vs31%
AIME (Advanced Math Competition)

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

50%vs62%
Long-Context Reasoning

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

66%vs68%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs55 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 NeMo0.40s
Mixtral 8x22B0.70s
Est. End-to-End Response (500 tokens)TTFT + generation time
Mistral NeMo5.96s
Mixtral 8x22B9.79s

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)
Mistral NeMo Monthly API Bill
$0.43 / month
$0.15/1M in · $0.15/1M out
Mixtral 8x22B Monthly API Bill
$3.78 / month
$1.2/1M in · $1.2/1M out
Mistral NeMo saves approximately $3.34 / month (88%) under this workload.8.7x Savings

Mistral NeMo · Strengths & Key Advantages

  • Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 29% · GPQA Diamond 44.5%)
  • Throughput elevato di 90 token/secondo con latenza al primo token (TTFT) di soli 400 ms
  • Pesi aperti (12B) 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

Mixtral 8x22B · Strengths & Key Advantages

  • Finestra di contesto da 66K con capacità massima di output fino a 8K token (SWE-bench Verified 36% · GPQA Diamond 51.2%)
  • Throughput elevato di 55 token/secondo con latenza al primo token (TTFT) di soli 700 ms
  • Pesi aperti (141B total (39B 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

Token Use & Task Economics

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

MetricMistral NeMoMixtral 8x22B
Cost per Task
Estimated USD to finish one task
$0.08$0.45
Output Tokens / Task
Tokens generated per task
25K35K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureMistral NeMoMixtral 8x22B
Official API Model ID (SDK)mistral-nemo-latestmixtral-8x22b-latest
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-072024-04
Knowledge CutoffJuly 2024March 2024
ArchitectureDense Transformer with Tekken TokenizerSparse Mixture-of-Experts (8x22B)
Parameters12B141B total (39B active MoE)
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 ($0.15/1M in) across full 128K windowFlat rate ($1.2/1M in) across full 66K window
Prompt Cache Policy & TTLCached input at $0.05/1M tokensCached input at $0.6/1M tokens
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
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) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 87 GB VRAM (4-bit Quantized) · 303 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

Mistral NeMo vs Mixtral 8x22B: Risposte Rapide (FAQ)

Which is more intelligent, Mistral NeMo or Mixtral 8x22B?

Mixtral 8x22B is more intelligent. It scores 44 compared with 39 on the composite Intelligence Index.

Which model is faster?

Mistral NeMo is faster, generating 90 tokens per second compared with 55 tokens per second.

Which model is cheaper to run?

Mistral NeMo is cheaper at $0.08 per 1M tokens blended, compared with $0.78 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral NeMo has lower latency, with a time to first token of 0.40s compared with 0.70s.

Which model has a larger context window?

Mistral NeMo has a larger context window, supporting 128K tokens compared with 65.5K tokens.

Verdetto

Sia Mistral NeMo (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 Mixtral 8x22B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Mixtral 8x22B guida nei benchmark di ragionamento e programmazione, Mistral NeMo garantisce una generazione più rapida e Mistral NeMo offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Mistral NeMo o Mixtral 8x22B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mixtral 8x22B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Mistral NeMo e Mixtral 8x22B?+

Mistral NeMo costa $0.15 / $0.15 per milione di token di input/output, mentre Mixtral 8x22B costa $1.20 / $1.20. Nel costo combinato, Mistral NeMo risulta più economico.

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

Mistral NeMo supporta 128K token di contesto contro 65.5K token di Mixtral 8x22B, mentre Mistral NeMo 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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).