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Head-to-Head Model Comparison

GPT-OSS 20B vs Llama 3.1 8B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-OSS 20B vs Llama 3.1 8B: Benchmarks, Pricing & Speed Comparison

GPT-OSS 20B von OpenAI und Llama 3.1 8B von Meta gehören zu den führenden KI-Modellen. GPT-OSS 20B bietet ein Kontextfenster von 131.1K Tokens bei API-Preisen von $0.10/$0.50 pro 1M Tokens, während Llama 3.1 8B 128K Tokens Kontext zu $0.00/$0.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

GPT-OSS 20B

Open-Weights-Reasoning-Modell

GPT-OSS 20B von OpenAI ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 131K Kontextfenster, 160 Token/s Durchsatz, einem Intelligenz-Index von 46/100 und $0.1/$0.5 pro 1M Tok

Intelligence Index46 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.50 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

32.5%

Meta

Llama 3.1 8B

Open-Weights-Basismodell

Llama 3.1 8B von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 160 Token/s Durchsatz, einem Intelligenz-Index von 60/100 und kostenlosem Open-Weights-Zugan

Intelligence Index60 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22%

Wichtigste Unterschiede auf einen Blick

  • Llama 3.1 8B is 1.0x cheaper for API usage (input + output tokens combined).
  • GPT-OSS 20B has a larger context window (131K vs 128K tokens).
  • Llama 3.1 8B is faster at 160 tok/s vs 160 tok/s.
  • GPT-OSS 20B leads on SWE-bench coding (32.5% vs 22.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-OSS 20BLlama 3.1 8B
Intelligence Index
Composite reasoning & knowledge score (0-100)
4660
Provider
OpenAIMeta
Input Price
Cost per million input tokens
$0.10 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$0.50 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.08 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
131.1K tokens~197 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
33K8K
Throughput Speed
Tokens generated per second
160 tok/s160 tok/s
SWE-bench Verified
Real-world software engineering
32.5%22%
GPQA Diamond
Graduate-level reasoning
55%38%
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
GPT-OSS 20B
+10.5% SWE-bench lead
Cost
Llama 3.1 8B
1.0x cheaper monthly runtime
Speed
Llama 3.1 8B
+0% faster generation
Context
GPT-OSS 20B
131.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-OSS 20BLlama 3.1 8B
SWE-bench Verified (Autonomous Coding)

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

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

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

55%vs38%
MMLU-Pro (Multi-task Language Understanding)

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

72.5%vs65%
LMSYS Chatbot Arena Elo

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

1288ptsvs1245pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

7.2%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

22%vsN/A
SciCode (Scientific Computing)

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

24.5%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

46%vsN/A
AIME (Advanced Math Competition)

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

72%vsN/A
Long-Context Reasoning

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

63%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs160 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 20B0.26s
Llama 3.1 8B0.22s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-OSS 20B3.38s
Llama 3.1 8B3.35s

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)
GPT-OSS 20B Monthly API Bill
$0.61 / month
$0.1/1M in · $0.5/1M out
Llama 3.1 8B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Llama 3.1 8B saves approximately $0.61 / month (100%) under this workload.1.0x Savings

GPT-OSS 20B · Strengths & Key Advantages

  • 131K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 32.5% · GPQA Diamond 55%)
  • Hohe Generierungsgeschwindigkeit von 160 Token/Sekunde bei nur 260 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (21B (3.6B 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 3.1 8B · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 22% · GPQA Diamond 38%)
  • Hohe Generierungsgeschwindigkeit von 160 Token/Sekunde bei nur 220 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (8B) 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.

MetricGPT-OSS 20BLlama 3.1 8B
Cost per Task
Estimated USD to finish one task
$0.09$0.00
Output Tokens / Task
Tokens generated per task
40K4K
Reasoning Tokens / Task
Thinking tokens burned per task
18K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-OSS 20BLlama 3.1 8B
Official API Model ID (SDK)gpt-oss-20bmeta-llama/Llama-3.1-8B
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-082024-07
Knowledge CutoffJune 2024December 2023
ArchitectureSparse MoE transformer with reasoningDense auto-regressive multilingual transformer
Parameters21B (3.6B active MoE)8B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.1/1M in) across full 131K windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix caching at $0.01/1M (no extra cache-write fee)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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 & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 13 GB VRAM (4-bit Quantized) · 45 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

GPT-OSS 20B vs Llama 3.1 8B: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-OSS 20B or Llama 3.1 8B?

Llama 3.1 8B is more intelligent. It scores 60 compared with 46 on the composite Intelligence Index.

Which model is cheaper to run?

Llama 3.1 8B is cheaper at $0.00 per 1M tokens blended, compared with $0.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.1 8B has lower latency, with a time to first token of 0.22s compared with 0.26s.

Which model has a larger context window?

GPT-OSS 20B has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Fazit

Sowohl GPT-OSS 20B (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 3.1 8B (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 8B führt bei komplexem Reasoning und Coding, GPT-OSS 20B liefert den höheren Token-Durchsatz und Llama 3.1 8B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-OSS 20B oder Llama 3.1 8B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 3.1 8B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-OSS 20B und Llama 3.1 8B?+

GPT-OSS 20B kostet $0.10 / $0.50 pro 1M Input-/Output-Tokens, Llama 3.1 8B liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Llama 3.1 8B günstiger.

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

GPT-OSS 20B bietet 131.1K Tokens Kontext gegenüber 128K Tokens bei Llama 3.1 8B. Bei der Generierungsgeschwindigkeit liegt GPT-OSS 20B 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 OpenAI und Meta, Systemkarten und unabhängige Benchmark-Auswertungen (2026).