Head-to-Head Model Comparison

GPT-OSS 120B vs Llama 4 Behemoth

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale

Changez et Comparez les Modèles

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

GPT-OSS 120BProfile

Llama 4 BehemothProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:

OpenAI

GPT-OSS 120B

Modèle de Base à Poids Ouverts

GPT-OSS 120B de OpenAI est un modèle d’IA conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, offrant une fenêtre de contexte de 131K, une vitesse de 95 tokens/s, un indice d’intelligence

Intelligence Index56 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

45.5%

Llama 4 Behemoth

Modèle de Base à Poids Ouverts

Llama 4 Behemoth de Meta est un modèle d’IA conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, offrant une fenêtre de contexte de 10M, une vitesse de 45 tokens/s, un indice d’intelligence

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

10M tokens

SWE-bench Verified:

75%

Différences Clés en un Coup d’Œil

Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
Llama 4 Behemoth has a larger context window (10M vs 131K tokens).
GPT-OSS 120B is faster at 95 tok/s vs 45 tok/s.
Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 45.5%).

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

Caractéristique	GPT-OSS 120B	Llama 4 Behemoth
Intelligence Index Composite reasoning & knowledge score (0-100)	56	93
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	10M tokens~15,000 A4 pages of text
Max Output Tokens Per single request	33K	64K
Throughput Speed Tokens generated per second	95 tok/s	45 tok/s
SWE-bench Verified Real-world software engineering	45.5%	75%
GPQA Diamond Graduate-level reasoning	68.5%	82%
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.00 / 1M

Qui gagne où ?

At-a-glance decision framework

Code

Llama 4 Behemoth

+29.5% SWE-bench lead

Coût

Llama 4 Behemoth

1.0x cheaper monthly runtime

Vitesse

GPT-OSS 120B

+111% faster generation

Contexte

Llama 4 Behemoth

10M tokens window

Scores de Benchmarks Vérifiés

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

GPT-OSS 120BLlama 4 Behemoth

SWE-bench Verified (Autonomous Coding)

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

45.5%vs75%

GPQA Diamond (Doctorate-Level Science & Math)

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

68.5%vs82%

MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs88.5%

LMSYS Chatbot Arena Elo

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

1330ptsvs1490pts

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%vsN/A

AIME (Advanced Math Competition)

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

84.5%vsN/A

Long-Context Reasoning

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

71%vs95%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs45 tok/s

Latence et Temps de Réponse

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 4 Behemoth1.20s

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

GPT-OSS 120B5.74s

Llama 4 Behemoth12.31s

Simulateur Interactif de Coût de Tokens

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 4 Behemoth Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 4 Behemoth saves approximately $0.79 / month (100%) under this workload.1.0x Savings

GPT-OSS 120B · Strengths & Key Advantages

Fenêtre de contexte de 131K et capacité de sortie maximale de 33K tokens (SWE-bench Verified 45.5% · GPQA Diamond 68.5%)
Débit rapide de 95 tokens/seconde avec une latence au premier token (TTFT) de 480 ms
Poids ouverts (117B (5.1B MoE)) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs

Llama 4 Behemoth · Strengths & Key Advantages

Fenêtre de contexte de 10M et capacité de sortie maximale de 64K tokens (SWE-bench Verified 75% · GPQA Diamond 82%)
Débit rapide de 45 tokens/seconde avec une latence au premier token (TTFT) de 1200 ms
Poids ouverts (2T total / 288B (16-of-2 MoE)) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs

Token Use & Task Economics

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

Metric	GPT-OSS 120B	Llama 4 Behemoth
Cost per Task Estimated USD to finish one task	$0.14	$0.00
Output Tokens / Task Tokens generated per task	45K	35K
Reasoning Tokens / Task Thinking tokens burned per task	22K	15K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

Caractéristique	GPT-OSS 120B	Llama 4 Behemoth
Official API Model ID (SDK)	gpt-oss-120b	meta-llama/Llama-4-Behemoth
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2026-05
Knowledge Cutoff	June 2024	February 2026
Architecture	Sparse MoE transformer with reasoning	Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE)
Parameters	117B (5.1B active MoE)	2T total / 288B active (16-of-2 MoE)
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)
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) · 1240 GB VRAM (4-bit Quantized) · 4300 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
Output Modalities	text	text

GPT-OSS 120B vs Llama 4 Behemoth: Réponses Rapides (FAQ)

Which is more intelligent, GPT-OSS 120B or Llama 4 Behemoth?

Llama 4 Behemoth is more intelligent. It scores 93 compared with 56 on the composite Intelligence Index.

Which model is faster?

GPT-OSS 120B is faster, generating 95 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

Llama 4 Behemoth 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 1.20s.

Which model has a larger context window?

Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 131.1K tokens.

Verdict

Choose GPT-OSS 120B for conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, l’intégration rag et les pipelines de production évolutifs., or choose Llama 4 Behemoth for conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, l’intégration rag et les pipelines de production évolutifs..

Questions fréquentes

Which is cheaper: GPT-OSS 120B or Llama 4 Behemoth?+

GPT-OSS 120B costs $0.15/1M input and $0.6/1M output tokens, while Llama 4 Behemoth costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: GPT-OSS 120B or Llama 4 Behemoth?+

GPT-OSS 120B supports 131K tokens whereas Llama 4 Behemoth supports 10M tokens.

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 4 Behemoth

Changez et Comparez les Modèles

GPT-OSS 120B

Llama 4 Behemoth

Qui gagne où ?

Scores de Benchmarks Vérifiés

Latence et Temps de Réponse

Simulateur Interactif de Coût de Tokens

GPT-OSS 120B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 4 Behemoth · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spécifications Techniques

GPT-OSS 120B vs Llama 4 Behemoth: Réponses Rapides (FAQ)

Verdict

Questions fréquentes

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