Head-to-Head Model Comparison

Llama 3.1 Nemotron 70B Instruct vs Mistral Medium 3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Llama 3.1 Nemotron 70B InstructProfile

Mistral Medium 3Profile

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NVIDIA

Llama 3.1 Nemotron 70B Instruct

Open-Weights-Basismodell

Llama 3.1 Nemotron 70B Instruct von NVIDIA ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 80 Token/s Durchsatz, einem Intelligenz-Index von 47/100 und $0.35/$0.7

Intelligence Index47 / 100

Input Token:

$0.35 / 1M

Output Token:

$0.70 / 1M

Context Window:

128K tokens

SWE-bench Verified:

41.2%

Mistral AI

Mistral Medium 3

Open-Weights-Basismodell

Mistral Medium 3 von Mistral AI ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 85 Token/s Durchsatz, einem Intelligenz-Index von 47/100 und $0.4/$2 pro 1M Token.

Intelligence Index47 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.5%

Wichtigste Unterschiede auf einen Blick

Llama 3.1 Nemotron 70B Instruct is 2.0x cheaper for API usage (input + output tokens combined).
Mistral Medium 3 has a larger context window (131K vs 128K tokens).
Mistral Medium 3 is faster at 85 tok/s vs 80 tok/s.
Llama 3.1 Nemotron 70B Instruct leads on SWE-bench coding (41.2% vs 40.5%).

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

Feature	Llama 3.1 Nemotron 70B Instruct	Mistral Medium 3
Intelligence Index Composite reasoning & knowledge score (0-100)	47	47
Provider	NVIDIA	Mistral AI
Input Price Cost per million input tokens	$0.35 / 1M	$0.40 / 1M
Output Price Cost per million output tokens	$0.70 / 1M	$2.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.21 / 1M	$0.35 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	80 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	41.2%	40.5%
GPQA Diamond Graduate-level reasoning	58.5%	57.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.10 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Llama 3.1 Nemotron 70B Instruct

+0.7% SWE-bench lead

Cost

Llama 3.1 Nemotron 70B Instruct

2.0x cheaper monthly runtime

Speed

Mistral Medium 3

+6% faster generation

Context

Mistral Medium 3

131.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Llama 3.1 Nemotron 70B InstructMistral Medium 3

SWE-bench Verified (Autonomous Coding)

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

41.2%vs40.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

58.5%vs57.5%

MMLU-Pro (Multi-task Language Understanding)

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

79.2%vs79.5%

LMSYS Chatbot Arena Elo

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

1318ptsvs1310pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

38%vs36%

AIME (Advanced Math Competition)

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

68%vs68%

Long-Context Reasoning

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

75%vs77%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs85 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

Llama 3.1 Nemotron 70B Instruct0.45s

Mistral Medium 30.48s

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

Llama 3.1 Nemotron 70B Instruct6.70s

Mistral Medium 36.36s

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)

Llama 3.1 Nemotron 70B Instruct Monthly API Bill

$1.30 / month

$0.35/1M in · $0.7/1M out

Mistral Medium 3 Monthly API Bill

$2.55 / month

$0.4/1M in · $2/1M out

Llama 3.1 Nemotron 70B Instruct saves approximately $1.25 / month (49%) under this workload.2.0x Savings

Llama 3.1 Nemotron 70B Instruct · Strengths & Key Advantages

128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 41.2% · GPQA Diamond 58.5%)
Hohe Generierungsgeschwindigkeit von 80 Token/Sekunde bei nur 450 ms Erstlatenz (TTFT)
Offene Modellgewichte (70B) 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 Medium 3 · Strengths & Key Advantages

131K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 40.5% · GPQA Diamond 57.5%)
Hohe Generierungsgeschwindigkeit von 85 Token/Sekunde bei nur 480 ms Erstlatenz (TTFT)
Offene Modellgewichte (Confidential (~70B)) 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	Llama 3.1 Nemotron 70B Instruct	Mistral Medium 3
Cost per Task Estimated USD to finish one task	$0.28	$0.35
Output Tokens / Task Tokens generated per task	32K	35K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.1 Nemotron 70B Instruct	Mistral Medium 3
Official API Model ID (SDK)	llama-3-1-nemotron-70b	mistral-medium-3-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-04	2025-05
Knowledge Cutoff	December 2024	March 2025
Architecture	RLHF Aligned Llama 3.1 Derivative	Enterprise Multimodal Transformer
Parameters	70B	Confidential (~70B)
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 ($0.35/1M in) across full 128K window	Flat rate ($0.4/1M in) across full 131K window
Prompt Cache Policy & TTL	Cached input at $0.1/1M tokens	Cached input at $0.1/1M 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)	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) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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	text, vision
Output Modalities	text	text

Llama 3.1 Nemotron 70B Instruct vs Mistral Medium 3: Schnelle Antworten (FAQ)

Which model is faster?

Mistral Medium 3 is faster, generating 85 tokens per second compared with 80 tokens per second.

Which model is cheaper to run?

Llama 3.1 Nemotron 70B Instruct is cheaper at $0.21 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.1 Nemotron 70B Instruct has lower latency, with a time to first token of 0.45s compared with 0.48s.

Which model has a larger context window?

Mistral Medium 3 has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Fazit

Sowohl Llama 3.1 Nemotron 70B Instruct (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Mistral Medium 3 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 3.1 Nemotron 70B Instruct führt bei komplexem Reasoning und Coding, Mistral Medium 3 liefert den höheren Token-Durchsatz und Llama 3.1 Nemotron 70B Instruct ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Llama 3.1 Nemotron 70B Instruct oder Mistral Medium 3?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 3.1 Nemotron 70B Instruct vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Llama 3.1 Nemotron 70B Instruct und Mistral Medium 3?+

Llama 3.1 Nemotron 70B Instruct kostet $0.35 / $0.70 pro 1M Input-/Output-Tokens, Mistral Medium 3 liegt bei $0.40 / $2.00. Beim gewichteten Gesamtpreis ist Llama 3.1 Nemotron 70B Instruct günstiger.

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

Llama 3.1 Nemotron 70B Instruct bietet 128K Tokens Kontext gegenüber 131.1K Tokens bei Mistral Medium 3. Bei der Generierungsgeschwindigkeit liegt Mistral Medium 3 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)

Llama 3.1 Nemotron 70B Instruct vs Mistral Medium 3

Beliebige Modelle wechseln & vergleichen

Llama 3.1 Nemotron 70B Instruct

Mistral Medium 3

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Llama 3.1 Nemotron 70B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Medium 3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Llama 3.1 Nemotron 70B Instruct vs Mistral Medium 3: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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