Head-to-Head Model Comparison

GPT-OSS 120B vs DeepSeek V4 Pro

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia e Confronta Qualsiasi Modello

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

GPT-OSS 120BProfile

DeepSeek V4 ProProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-OSS 120B

Modello Base a Pesi Aperti

GPT-OSS 120B di OpenAI 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 131K, velocità di 95 token/s, indice di intelligenza di 56/100 e t

Intelligence Index56 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

45.5%

DeepSeek

DeepSeek V4 Pro

IA Agentica e Strumenti Autonomi

DeepSeek V4 Pro di DeepSeek è un modello di IA progettato per i workflow agentici autonomi, il tool calling e l’automazione multi-step, con finestra di contesto da 1M, velocità di 80 token/s, indice di intelligenza di 53/100 e tariffa di $1

Intelligence Index53 / 100

Input Token:

$1.32 / 1M

Output Token:

$3.96 / 1M

Context Window:

1M tokens

SWE-bench Verified:

68.5%

Differenze Chiave a Colpo d’Occhio

GPT-OSS 120B is 7.3x cheaper for API usage (input + output tokens combined).
DeepSeek V4 Pro has a larger context window (1M vs 131K tokens).
GPT-OSS 120B is faster at 95 tok/s vs 80 tok/s.
DeepSeek V4 Pro leads on SWE-bench coding (68.5% vs 45.5%).

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

Feature	GPT-OSS 120B	DeepSeek V4 Pro
Intelligence Index Composite reasoning & knowledge score (0-100)	56	53
Provider	OpenAI	DeepSeek
Input Price Cost per million input tokens	$0.15 / 1M	$1.32 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$3.96 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.10 / 1M	$0.76 / 1M
Context Window Maximum input + output tokens	131.1K tokens~197 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	33K	128K
Throughput Speed Tokens generated per second	95 tok/s	80 tok/s
SWE-bench Verified Real-world software engineering	45.5%	68.5%
GPQA Diamond Graduate-level reasoning	68.5%	81.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.14 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

DeepSeek V4 Pro

+23.0% SWE-bench lead

Cost

GPT-OSS 120B

7.3x cheaper monthly runtime

Speed

GPT-OSS 120B

+19% faster generation

Context

DeepSeek V4 Pro

1M tokens window

Punteggi Benchmark Verificati

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

GPT-OSS 120BDeepSeek V4 Pro

SWE-bench Verified (Autonomous Coding)

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

45.5%vs68.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

68.5%vs81.2%

MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs88.9%

LMSYS Chatbot Arena Elo

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

1330ptsvs1438pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

9.8%vs15.6%

Terminal-Bench (Agentic Coding & Terminal Use)

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

30.5%vs46.5%

SciCode (Scientific Computing)

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

33%vs43%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

58.5%vs70.2%

AIME (Advanced Math Competition)

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

84.5%vs91%

Long-Context Reasoning

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

71%vs90%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs80 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 120B0.48s

DeepSeek V4 Pro0.58s

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

GPT-OSS 120B5.74s

DeepSeek V4 Pro6.83s

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 120B Monthly API Bill

$0.79 / month

$0.15/1M in · $0.6/1M out

DeepSeek V4 Pro Monthly API Bill

$5.75 / month

$1.32/1M in · $3.96/1M out

GPT-OSS 120B saves approximately $4.97 / month (86%) under this workload.7.3x Savings

GPT-OSS 120B · Strengths & Key Advantages

Finestra di contesto da 131K con capacità massima di output fino a 33K token (SWE-bench Verified 45.5% · GPQA Diamond 68.5%)
Throughput elevato di 95 token/secondo con latenza al primo token (TTFT) di soli 480 ms
Pesi aperti (117B (5.1B 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

DeepSeek V4 Pro · Strengths & Key Advantages

Finestra di contesto da 1M con capacità massima di output fino a 128K token (SWE-bench Verified 68.5% · GPQA Diamond 81.2%)
Throughput elevato di 80 token/secondo con latenza al primo token (TTFT) di soli 580 ms
Pesi aperti (MoE (Colossal)) 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 120B	DeepSeek V4 Pro
Cost per Task Estimated USD to finish one task	$0.14	$0.67
Output Tokens / Task Tokens generated per task	45K	52K
Reasoning Tokens / Task Thinking tokens burned per task	22K	25K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-OSS 120B	DeepSeek V4 Pro
Official API Model ID (SDK)	gpt-oss-120b	deepseek-reasoner
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2026-08
Knowledge Cutoff	June 2024	May 2026
Architecture	Sparse MoE transformer with reasoning	Next-Gen Sparse MoE with Agent RL Tuning
Parameters	117B (5.1B active MoE)	MoE (Colossal)
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	Flat rate ($1.32/1M in) across full 1000K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.015/1M (no extra cache-write fee)	Automatic prefix caching at $0.14/1M (no extra cache-write fee)
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) · 73 GB VRAM (4-bit Quantized) · 252 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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 120B vs DeepSeek V4 Pro: Risposte Rapide (FAQ)

Which is more intelligent, GPT-OSS 120B or DeepSeek V4 Pro?

GPT-OSS 120B is more intelligent. It scores 56 compared with 53 on the composite Intelligence Index.

Which model is faster?

GPT-OSS 120B is faster, generating 95 tokens per second compared with 80 tokens per second.

Which model is cheaper to run?

GPT-OSS 120B is cheaper at $0.10 per 1M tokens blended, compared with $0.76 (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 0.58s.

Which model has a larger context window?

DeepSeek V4 Pro has a larger context window, supporting 1M tokens compared with 131.1K tokens.

Verdetto

Sia GPT-OSS 120B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia DeepSeek V4 Pro (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GPT-OSS 120B guida nei benchmark di ragionamento e programmazione, GPT-OSS 120B garantisce una generazione più rapida e GPT-OSS 120B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GPT-OSS 120B o DeepSeek V4 Pro?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT-OSS 120B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GPT-OSS 120B e DeepSeek V4 Pro?+

GPT-OSS 120B costa $0.15 / $0.60 per milione di token di input/output, mentre DeepSeek V4 Pro costa $1.32 / $3.96. Nel costo combinato, GPT-OSS 120B risulta più economico.

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

GPT-OSS 120B supporta 131.1K token di contesto contro 1M token di DeepSeek V4 Pro, mentre GPT-OSS 120B 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 120B vs DeepSeek V4 Pro

Cambia e Confronta Qualsiasi Modello

GPT-OSS 120B

DeepSeek V4 Pro

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

GPT-OSS 120B · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek V4 Pro · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

GPT-OSS 120B vs DeepSeek V4 Pro: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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