Head-to-Head Model Comparison

Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Nemotron 3 Nano Omni 30B A3B ReasoningProfile

Mistral Small 4Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Nemotron 3 Nano Omni 30B A3B Reasoning

Nativ Multimodale KI

Nemotron 3 Nano Omni 30B A3B Reasoning von NVIDIA ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 256K Kontextfenster, 105 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und

Intelligence Index48 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

256K tokens

SWE-bench Verified:

42%

Mistral AI

Mistral Small 4

Ultraschnelle Echtzeit-Inferenz

Mistral Small 4 von Mistral AI ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 256K Kontextfenster, 120 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und $0.1/$0.3 pro 1M Token.

Intelligence Index48 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

256K tokens

SWE-bench Verified:

44.5%

Wichtigste Unterschiede auf einen Blick

Mistral Small 4 is 2.0x cheaper for API usage (input + output tokens combined).
Mistral Small 4 is faster at 120 tok/s vs 105 tok/s.
Mistral Small 4 leads on SWE-bench coding (44.5% vs 42.0%).

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

Feature	Nemotron 3 Nano Omni 30B A3B Reasoning	Mistral Small 4
Intelligence Index Composite reasoning & knowledge score (0-100)	48	48
Provider	NVIDIA	Mistral AI
Input Price Cost per million input tokens	$0.20 / 1M	$0.10 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$0.30 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$0.07 / 1M
Context Window Maximum input + output tokens	256K tokens~384 A4 pages of text	256K tokens~384 A4 pages of text
Max Output Tokens Per single request	16K	16K
Throughput Speed Tokens generated per second	105 tok/s	120 tok/s
SWE-bench Verified Real-world software engineering	42%	44.5%
GPQA Diamond Graduate-level reasoning	56.5%	56%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.03 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Mistral Small 4

+2.5% SWE-bench lead

Cost

Mistral Small 4

2.0x cheaper monthly runtime

Speed

Mistral Small 4

+14% faster generation

Context

Tied

256K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Nemotron 3 Nano Omni 30B A3B ReasoningMistral Small 4

SWE-bench Verified (Autonomous Coding)

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

42%vs44.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

56.5%vs56%

MMLU-Pro (Multi-task Language Understanding)

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

77.2%vs78%

LMSYS Chatbot Arena Elo

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

1310ptsvs1315pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

36.5%vs38%

AIME (Advanced Math Competition)

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

66%vs66%

Long-Context Reasoning

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

78%vs80%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs120 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

Nemotron 3 Nano Omni 30B A3B Reasoning0.38s

Mistral Small 40.34s

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

Nemotron 3 Nano Omni 30B A3B Reasoning5.14s

Mistral Small 44.51s

Interaktiver Token-Kosten-Simulator

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)

Nemotron 3 Nano Omni 30B A3B Reasoning Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

Mistral Small 4 Monthly API Bill

$0.47 / month

$0.1/1M in · $0.3/1M out

Mistral Small 4 saves approximately $0.45 / month (49%) under this workload.2.0x Savings

Nemotron 3 Nano Omni 30B A3B Reasoning · Strengths & Key Advantages

256K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 42% · GPQA Diamond 56.5%)
Hohe Generierungsgeschwindigkeit von 105 Token/Sekunde bei nur 380 ms Erstlatenz (TTFT)
Offene Modellgewichte (30B (3B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Mistral Small 4 · Strengths & Key Advantages

256K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 44.5% · GPQA Diamond 56%)
Hohe Generierungsgeschwindigkeit von 120 Token/Sekunde bei nur 340 ms Erstlatenz (TTFT)
Offene Modellgewichte (24B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
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.

Metric	Nemotron 3 Nano Omni 30B A3B Reasoning	Mistral Small 4
Cost per Task Estimated USD to finish one task	$0.22	$0.12
Output Tokens / Task Tokens generated per task	28K	28K
Reasoning Tokens / Task Thinking tokens burned per task	12K	12K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Nemotron 3 Nano Omni 30B A3B Reasoning	Mistral Small 4
Official API Model ID (SDK)	nemotron-3-nano-omni-30b-a3b	mistral-small-4-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-04	2026-03
Knowledge Cutoff	February 2026	January 2026
Architecture	Omnimodal Sparse MoE (30B Total / 3B Active)	Next-Gen Compact Multimodal Transformer
Parameters	30B (3B active MoE)	24B
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (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 256K window	Flat rate ($0.1/1M in) across full 256K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Cached input at $0.03/1M tokens
Computer Use / GUI Agent	API / 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 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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full 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 Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)	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) · 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-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 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision, audio	text, vision
Output Modalities	text	text

Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4: Schnelle Antworten (FAQ)

Which model is faster?

Mistral Small 4 is faster, generating 120 tokens per second compared with 105 tokens per second.

Which model is cheaper to run?

Mistral Small 4 is cheaper at $0.07 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Small 4 has lower latency, with a time to first token of 0.34s compared with 0.38s.

Fazit

Sowohl Nemotron 3 Nano Omni 30B A3B Reasoning (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Mistral Small 4 (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Nemotron 3 Nano Omni 30B A3B Reasoning führt bei komplexem Reasoning und Coding, Mistral Small 4 liefert den höheren Token-Durchsatz und Mistral Small 4 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Nemotron 3 Nano Omni 30B A3B Reasoning oder Mistral Small 4?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Nemotron 3 Nano Omni 30B A3B Reasoning vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Nemotron 3 Nano Omni 30B A3B Reasoning und Mistral Small 4?+

Nemotron 3 Nano Omni 30B A3B Reasoning kostet $0.20 / $0.60 pro 1M Input-/Output-Tokens, Mistral Small 4 liegt bei $0.10 / $0.30. Beim gewichteten Gesamtpreis ist Mistral Small 4 günstiger.

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

Nemotron 3 Nano Omni 30B A3B Reasoning bietet 256K Tokens Kontext gegenüber 256K Tokens bei Mistral Small 4. Bei der Generierungsgeschwindigkeit liegt Mistral Small 4 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4

Beliebige Modelle wechseln & vergleichen

Nemotron 3 Nano Omni 30B A3B Reasoning

Mistral Small 4

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Nemotron 3 Nano Omni 30B A3B Reasoning · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Small 4 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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