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Ministral 8B vs Ministral 3B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Ministral 8B vs Ministral 3B: Benchmarks, Pricing & Speed Comparison

Ministral 8B von Mistral AI und Ministral 3B von Mistral AI gehören zu den führenden KI-Modellen. Ministral 8B bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.10/$0.10 pro 1M Tokens, während Ministral 3B 128K Tokens Kontext zu $0.04/$0.04 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Mistral AI

Ministral 8B

Kompaktes Effizienz-Modell

Ministral 8B von Mistral AI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 128K Kontextfenster, 110 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und $0.1/$0.1 pro 1M Token

Intelligence Index38 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

32.5%

Mistral AI

Ministral 3B

Kompaktes Effizienz-Modell

Ministral 3B von Mistral AI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 128K Kontextfenster, 180 Token/s Durchsatz, einem Intelligenz-Index von 33/100 und $0.04/$0.04 pro 1M Tok

Intelligence Index33 / 100
Input Token:

$0.04 / 1M

Output Token:

$0.04 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22.1%

Wichtigste Unterschiede auf einen Blick

  • Ministral 3B is 2.5x cheaper for API usage (input + output tokens combined).
  • Ministral 3B is faster at 180 tok/s vs 110 tok/s.
  • Ministral 8B leads on SWE-bench coding (32.5% vs 22.1%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMinistral 8BMinistral 3B
Intelligence Index
Composite reasoning & knowledge score (0-100)
3833
Provider
Mistral AIMistral AI
Input Price
Cost per million input tokens
$0.10 / 1M$0.04 / 1M
Output Price
Cost per million output tokens
$0.10 / 1M$0.04 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.07 / 1M$0.03 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
110 tok/s180 tok/s
SWE-bench Verified
Real-world software engineering
32.5%22.1%
GPQA Diamond
Graduate-level reasoning
41.5%32.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.05 / 1M$0.02 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Ministral 8B
+10.4% SWE-bench lead
Cost
Ministral 3B
2.5x cheaper monthly runtime
Speed
Ministral 3B
+64% faster generation
Context
Tied
128K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Ministral 8BMinistral 3B
SWE-bench Verified (Autonomous Coding)

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

32.5%vs22.1%
GPQA Diamond (Doctorate-Level Science & Math)

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

41.5%vs32.5%
MMLU-Pro (Multi-task Language Understanding)

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

60.1%vs49.8%
LMSYS Chatbot Arena Elo

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

1235ptsvs1180pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

4.2%vs2.4%
Terminal-Bench (Agentic Coding & Terminal Use)

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

21%vs14.8%
SciCode (Scientific Computing)

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

21.8%vs15.2%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

35.8%vs24.5%
AIME (Advanced Math Competition)

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

41.2%vs28.5%
Long-Context Reasoning

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

65.2%vs60.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs180 tok/s

Latenz & Antwortzeit

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
Ministral 8B0.33s
Ministral 3B0.21s
Est. End-to-End Response (500 tokens)TTFT + generation time
Ministral 8B4.88s
Ministral 3B2.99s

Interaktiver Token-Kosten-Simulator

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)
Ministral 8B Monthly API Bill
$0.32 / month
$0.1/1M in · $0.1/1M out
Ministral 3B Monthly API Bill
$0.13 / month
$0.04/1M in · $0.04/1M out
Ministral 3B saves approximately $0.19 / month (60%) under this workload.2.5x Savings

Ministral 8B · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 32.5% · GPQA Diamond 41.5%)
  • Hohe Generierungsgeschwindigkeit von 110 Token/Sekunde bei nur 330 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.1/$0.1 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Ministral 3B · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 22.1% · GPQA Diamond 32.5%)
  • Hohe Generierungsgeschwindigkeit von 180 Token/Sekunde bei nur 210 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.04/$0.04 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

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

MetricMinistral 8BMinistral 3B
Cost per Task
Estimated USD to finish one task
$0.08$0.03
Output Tokens / Task
Tokens generated per task
21K15K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureMinistral 8BMinistral 3B
Official API Model ID (SDK)ministral-8b-latestministral-3b-latest
API ProtocolMistral AI REST API (OpenAI-Compatible)Mistral AI REST API (OpenAI-Compatible)
Release Date2024-102024-10
Knowledge CutoffOctober 2024October 2024
ArchitectureCompact Dense Transformer with Interleaved Sliding WindowUltra-Compact Dense Transformer
Parameters8B3B
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.1/1M in) across full 128K windowFlat rate ($0.04/1M in) across full 128K window
Prompt Cache Policy & TTLCached input at $0.05/1M tokensCached input at $0.02/1M 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 SupportAPI Prompt Engineering & RAG DistillationAPI Prompt Engineering & RAG Distillation
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-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 ClassificationMistral AI Production Safety & Alignment VerifiedMistral AI Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext
Output Modalitiestexttext

Ministral 8B vs Ministral 3B: Schnelle Antworten (FAQ)

Which is more intelligent, Ministral 8B or Ministral 3B?

Ministral 8B is more intelligent. It scores 38 compared with 33 on the composite Intelligence Index.

Which model is faster?

Ministral 3B is faster, generating 180 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

Ministral 3B is cheaper at $0.03 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Ministral 3B has lower latency, with a time to first token of 0.21s compared with 0.33s.

Fazit

Sowohl Ministral 8B (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Ministral 3B (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Ministral 8B führt bei komplexem Reasoning und Coding, Ministral 3B liefert den höheren Token-Durchsatz und Ministral 3B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Ministral 8B oder Ministral 3B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Ministral 8B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Ministral 8B und Ministral 3B?+

Ministral 8B kostet $0.10 / $0.10 pro 1M Input-/Output-Tokens, Ministral 3B liegt bei $0.04 / $0.04. Beim gewichteten Gesamtpreis ist Ministral 3B günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Ministral 8B bietet 128K Tokens Kontext gegenüber 128K Tokens bei Ministral 3B. Bei der Generierungsgeschwindigkeit liegt Ministral 3B vorn.

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.

Offizielle Quellen: Offizielle API-Dokumentation von Mistral AI und Mistral AI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).