Head-to-Head Model Comparison

Llama 3.3 70B vs GPT-4o mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Llama 3.3 70BProfile

GPT-4o miniProfile

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Popular Matchups:

Llama 3.3 70B

Open-Weights-Basismodell

Llama 3.3 70B von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 280 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und kostenlosem Open-Weights-Zuga

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

OpenAI

GPT-4o mini

Kompaktes Effizienz-Modell

GPT-4o mini von OpenAI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 128K Kontextfenster, 140 Token/s Durchsatz, einem Intelligenz-Index von 30/100 und $0.15/$0.6 pro 1M Token.

Intelligence Index30 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

24.5%

Wichtigste Unterschiede auf einen Blick

Llama 3.3 70B is 5.8x cheaper for API usage (input + output tokens combined).
Llama 3.3 70B is faster at 280 tok/s vs 140 tok/s.
Llama 3.3 70B leads on SWE-bench coding (39.5% vs 24.5%).

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

Feature	Llama 3.3 70B	GPT-4o mini
Intelligence Index Composite reasoning & knowledge score (0-100)	38	30
Provider	Meta	OpenAI
Input Price Cost per million input tokens	$0.00 / 1M	$0.15 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.07 / 1M	$0.14 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	16K
Throughput Speed Tokens generated per second	280 tok/s	140 tok/s
SWE-bench Verified Real-world software engineering	39.5%	24.5%
GPQA Diamond Graduate-level reasoning	48.9%	40.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.07 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Llama 3.3 70B

+15.0% SWE-bench lead

Cost

Llama 3.3 70B

5.8x cheaper monthly runtime

Speed

Llama 3.3 70B

+100% faster generation

Context

Tied

128K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Llama 3.3 70BGPT-4o mini

SWE-bench Verified (Autonomous Coding)

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

39.5%vs24.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.9%vs40.2%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs58.5%

LMSYS Chatbot Arena Elo

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

1285ptsvs1228pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

7.8%vs6.4%

Terminal-Bench (Agentic Coding & Terminal Use)

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

26.1%vs16.2%

SciCode (Scientific Computing)

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

23.7%vs14.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

42.5%vs28.3%

AIME (Advanced Math Competition)

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

56.2%vs46.2%

Long-Context Reasoning

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

63.9%vs52.7%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

280 tok/s vs140 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 3.3 70B0.25s

GPT-4o mini0.31s

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

Llama 3.3 70B2.04s

GPT-4o mini3.88s

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 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

GPT-4o mini Monthly API Bill

$0.88 / month

$0.15/1M in · $0.6/1M out

Llama 3.3 70B saves approximately $0.73 / month (83%) under this workload.5.8x Savings

Llama 3.3 70B · Strengths & Key Advantages

128K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Hohe Generierungsgeschwindigkeit von 280 Token/Sekunde bei nur 250 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

GPT-4o mini · Strengths & Key Advantages

128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 24.5% · GPQA Diamond 40.2%)
Hohe Generierungsgeschwindigkeit von 140 Token/Sekunde bei nur 310 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.15/$0.6 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.

Metric	Llama 3.3 70B	GPT-4o mini
Cost per Task Estimated USD to finish one task	$0.21	$0.16
Output Tokens / Task Tokens generated per task	45K	45K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.3 70B	GPT-4o mini
Official API Model ID (SDK)	meta-llama/Llama-3.3-70B	gpt-4o-mini
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2024-12	2024-07
Knowledge Cutoff	December 2023	October 2023
Architecture	Dense Autoregressive Transformer	Small Multimodal Dense
Parameters	70B	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($0.15/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix caching at $0.075/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
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)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision
Output Modalities	text	text

Llama 3.3 70B vs GPT-4o mini: Schnelle Antworten (FAQ)

Which is more intelligent, Llama 3.3 70B or GPT-4o mini?

Llama 3.3 70B is more intelligent. It scores 38 compared with 30 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 140 tokens per second.

Which model is cheaper to run?

Llama 3.3 70B 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?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.31s.

Fazit

Sowohl Llama 3.3 70B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-4o mini (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 3.3 70B führt bei komplexem Reasoning und Coding, Llama 3.3 70B liefert den höheren Token-Durchsatz und Llama 3.3 70B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Llama 3.3 70B oder GPT-4o mini?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 3.3 70B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Llama 3.3 70B und GPT-4o mini?+

Llama 3.3 70B kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, GPT-4o mini liegt bei $0.15 / $0.60. Beim gewichteten Gesamtpreis ist Llama 3.3 70B günstiger.

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

Llama 3.3 70B bietet 128K Tokens Kontext gegenüber 128K Tokens bei GPT-4o mini. Bei der Generierungsgeschwindigkeit liegt Llama 3.3 70B 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 3.3 70B vs GPT-4o mini

Beliebige Modelle wechseln & vergleichen

Llama 3.3 70B

GPT-4o mini

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4o mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Llama 3.3 70B vs GPT-4o mini: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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