Head-to-Head Model Comparison

GPT-OSS 120B vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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GPT-OSS 120BProfile

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GPT-OSS 120B

Open-Weights-Basismodell

GPT-OSS 120B von OpenAI ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 95 Token/s Durchsatz, einem Intelligenz-Index von 56/100 und $0.15/$0.6 pro 1M Token.

Intelligence Index56 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

45.5%

Llama 5

Open-Weights-Basismodell

Llama 5 von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 5M Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und kostenlosem Open-Weights-Zugang.

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Wichtigste Unterschiede auf einen Blick

Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
Llama 5 has a larger context window (5M vs 131K tokens).
GPT-OSS 120B is faster at 95 tok/s vs 65 tok/s.
Llama 5 leads on SWE-bench coding (74.0% vs 45.5%).

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

Feature	GPT-OSS 120B	Llama 5
Intelligence Index Composite reasoning & knowledge score (0-100)	56	94
Provider	OpenAI	Meta
Input Price Cost per million input tokens	$0.15 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.10 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	131.1K tokens~197 A4 pages of text	5M tokens~7,500 A4 pages of text
Max Output Tokens Per single request	33K	128K
Throughput Speed Tokens generated per second	95 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	45.5%	74%
GPQA Diamond Graduate-level reasoning	68.5%	84%
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.00 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Llama 5

+28.5% SWE-bench lead

Cost

Llama 5

1.0x cheaper monthly runtime

Speed

GPT-OSS 120B

+46% faster generation

Context

Llama 5

5M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-OSS 120BLlama 5

SWE-bench Verified (Autonomous Coding)

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

45.5%vs74%

GPQA Diamond (Doctorate-Level Science & Math)

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

68.5%vs84%

MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs87%

LMSYS Chatbot Arena Elo

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

1330ptsvs1485pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

9.8%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

30.5%vsN/A

SciCode (Scientific Computing)

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

33%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

58.5%vs68%

AIME (Advanced Math Competition)

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

84.5%vs88%

Long-Context Reasoning

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

71%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs65 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

GPT-OSS 120B0.48s

Llama 50.85s

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

GPT-OSS 120B5.74s

Llama 58.54s

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)

GPT-OSS 120B Monthly API Bill

$0.79 / month

$0.15/1M in · $0.6/1M out

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 5 saves approximately $0.79 / month (100%) under this workload.1.0x Savings

GPT-OSS 120B · Strengths & Key Advantages

131K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 45.5% · GPQA Diamond 68.5%)
Hohe Generierungsgeschwindigkeit von 95 Token/Sekunde bei nur 480 ms Erstlatenz (TTFT)
Offene Modellgewichte (117B (5.1B 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

Llama 5 · Strengths & Key Advantages

5M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 74% · GPQA Diamond 84%)
Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
Offene Modellgewichte (600B+ total (~40-80B )) 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	GPT-OSS 120B	Llama 5
Cost per Task Estimated USD to finish one task	$0.14	$0.00
Output Tokens / Task Tokens generated per task	45K	40K
Reasoning Tokens / Task Thinking tokens burned per task	22K	20K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-OSS 120B	Llama 5
Official API Model ID (SDK)	gpt-oss-120b	meta-llama/Llama-5
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2026-04
Knowledge Cutoff	June 2024	January 2026
Architecture	Sparse MoE transformer with reasoning	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
Parameters	117B (5.1B active MoE)	600B+ total (~40-80B active)
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 ($0.15/1M in) across full 131K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix caching at $0.015/1M (no extra cache-write fee)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG	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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 73 GB VRAM (4-bit Quantized) · 252 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)
Safety & Frontier Classification	OpenAI Preparedness Framework Audited & System Card Published	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, video, audio
Output Modalities	text	text

GPT-OSS 120B vs Llama 5: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-OSS 120B or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 56 on the composite Intelligence Index.

Which model is faster?

GPT-OSS 120B is faster, generating 95 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-OSS 120B has lower latency, with a time to first token of 0.48s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 131.1K tokens.

Fazit

Sowohl GPT-OSS 120B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 5 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 5 führt bei komplexem Reasoning und Coding, GPT-OSS 120B liefert den höheren Token-Durchsatz und Llama 5 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-OSS 120B oder Llama 5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 5 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-OSS 120B und Llama 5?+

GPT-OSS 120B kostet $0.15 / $0.60 pro 1M Input-/Output-Tokens, Llama 5 liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Llama 5 günstiger.

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

GPT-OSS 120B bietet 131.1K Tokens Kontext gegenüber 5M Tokens bei Llama 5. Bei der Generierungsgeschwindigkeit liegt GPT-OSS 120B 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)

GPT-OSS 120B vs Llama 5

Beliebige Modelle wechseln & vergleichen

GPT-OSS 120B

Llama 5

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

GPT-OSS 120B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

GPT-OSS 120B vs Llama 5: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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