Head-to-Head Model Comparison

GPT-OSS 20B vs Llama 3.1 8B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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GPT-OSS 20BProfile

Llama 3.1 8BProfile

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Popular Matchups:

OpenAI

GPT-OSS 20B

Ragionamento a Pesi Aperti

GPT-OSS 20B di OpenAI è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 131K, velocità di 160 token/s, indice di intelligenza di 46/100 e tariffa di

Intelligence Index46 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.50 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

32.5%

Llama 3.1 8B

Modello Base a Pesi Aperti

Llama 3.1 8B di Meta è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 160 token/s, indice di intelligenza di 60/100 e ta

Intelligence Index60 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22%

Differenze Chiave a Colpo d’Occhio

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 →

Feature	GPT-OSS 20B	Llama 3.1 8B
Intelligence Index Composite reasoning & knowledge score (0-100)	46	60
Provider	OpenAI	Meta
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 text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	33K	8K
Throughput Speed Tokens generated per second	160 tok/s	160 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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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 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

Finestra di contesto da 131K con capacità massima di output fino a 33K token (SWE-bench Verified 32.5% · GPQA Diamond 55%)
Throughput elevato di 160 token/secondo con latenza al primo token (TTFT) di soli 260 ms
Pesi aperti (21B (3.6B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Llama 3.1 8B · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 22% · GPQA Diamond 38%)
Throughput elevato di 160 token/secondo con latenza al primo token (TTFT) di soli 220 ms
Pesi aperti (8B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	GPT-OSS 20B	Llama 3.1 8B
Cost per Task Estimated USD to finish one task	$0.09	$0.00
Output Tokens / Task Tokens generated per task	40K	4K
Reasoning Tokens / Task Thinking tokens burned per task	18K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-OSS 20B	Llama 3.1 8B
Official API Model ID (SDK)	gpt-oss-20b	meta-llama/Llama-3.1-8B
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2024-07
Knowledge Cutoff	June 2024	December 2023
Architecture	Sparse MoE transformer with reasoning	Dense auto-regressive multilingual transformer
Parameters	21B (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 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 ($0.1/1M in) across full 131K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix caching at $0.01/1M (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)	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	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)
Quantization & Min GPU VRAM	BF16 / 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 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
Output Modalities	text	text

GPT-OSS 20B vs Llama 3.1 8B: Risposte Rapide (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.

Verdetto

Sia GPT-OSS 20B (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) sia Llama 3.1 8B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Llama 3.1 8B guida nei benchmark di ragionamento e programmazione, GPT-OSS 20B garantisce una generazione più rapida e Llama 3.1 8B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GPT-OSS 20B o Llama 3.1 8B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Llama 3.1 8B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GPT-OSS 20B e Llama 3.1 8B?+

GPT-OSS 20B costa $0.10 / $0.50 per milione di token di input/output, mentre Llama 3.1 8B costa $0.00 / $0.00. Nel costo combinato, Llama 3.1 8B risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

GPT-OSS 20B supporta 131.1K token di contesto contro 128K token di Llama 3.1 8B, mentre GPT-OSS 20B offre una velocità di generazione superiore.

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 20B vs Llama 3.1 8B

Cambia e Confronta Qualsiasi Modello

GPT-OSS 20B

Llama 3.1 8B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

GPT-OSS 20B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.1 8B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

GPT-OSS 20B vs Llama 3.1 8B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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