DeepSeek V4.1 Flash (Reasoning, Max Effort) vs. gpt-oss-120b (high)

Name: Inteligência
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Comparação entre DeepSeek V4.1 Flash (Reasoning, Max Effort) e gpt-oss-120b (high) em inteligência, preço, velocidade, janela de contexto e outras métricas.

Para mais detalhes sobre nossa metodologia, consulte a página de metodologia.

	DeepSeek	OpenAI
Inteligência
Intelligence Index	40	12
AA-Briefcase	1424	0
GDPval-AA v2	1632	745
AutomationBench-AA	69%	0%
Terminal-Bench v4.0	27%	0%
SciCode	52%	34%
Humanity's Last Exam	39%	20%
GDP.pdf	13%	4%
CritPt	14%	1%
AA-Omniscience	−5	−49
AA-LCR v1.1	84%	52%
Custo
Preço por 1M de tokens	$0.1842	$0.177
Preço de entrada por 1M de tokens	$0.30	$0.15
Preço de saída por 1M de tokens	$1.20	$0.595
Preço de acerto de cache por 1M de tokens	$0.006	$0.125
Custo por tarefa	$0.27	$0.11
Custo para executar o Intelligence Index	US$ 477	US$ 122
Uso de tokens
Tokens de saída por tarefa	89k	27k
Tokens de raciocínio por tarefa	63k	15k
Tokens de saída para executar o Intelligence Index	253M	84M
Desempenho
Velocidade de saída	190 tokens/s	192 tokens/s
Tempo até o primeiro token	1.00s	0.80s
Tempo até o primeiro token de resposta	11.52s	11.23s
Tempo de resposta de ponta a ponta	14.15s	13.83s
Tempo por tarefa	415.38s	137.98s
Especificações técnicas
Janela de contexto	1000k tokens~1.500 páginas A4 em fonte Arial tamanho 12	131k tokens~197 páginas A4 em fonte Arial tamanho 12
Data de lançamento	setembro de 2026	agosto de 2025
Data-limite do conhecimento		maio de 2024
Parâmetros totais	552B	117B
Parâmetros ativos	16B	5.1B
Raciocínio	Sim	Sim
Modalidade de entrada	Compatível com: texto e imagem	Compatível com: texto
Modalidade de saída	Compatível com: texto	Compatível com: texto
Pesos abertos	Sim	Sim
Licença	Mit	Apache 2.0
A licença permite uso comercial sem restrições	Sim	Sim

Destaques

Updated

Inteligência

Artificial Analysis Intelligence Index · Higher is better

Velocidade

Output tokens per second · Higher is better

Custo por tarefa

Weighted average cost (USD) per Intelligence Index task · Lower is better

InteligênciaUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Artificial Analysis Intelligence Index v4.3 includes: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Avaliações

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench v4.0New

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

IFBench

Instruction following

𝜏³-Banking

Agentic tool use

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Comparações do Intelligence Index

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Uso de tokens

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Custo

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Janela de contexto

Context Window

Context window: tokens limit · Higher is better

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Velocidade

Medida pela velocidade de saída (tokens por segundo)

Output Speed

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Latência

Medida pelo tempo (segundos) até o primeiro token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Tempo de resposta de ponta a ponta

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Tamanho do modelo (somente modelos de pesos abertos)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Perguntas frequentes

DeepSeek V4.1 Flash (Reasoning, Max Effort) é mais inteligente. DeepSeek V4.1 Flash (Reasoning, Max Effort) alcança 40, em comparação com gpt-oss-120b (high), que alcança 12 no Artificial Analysis Intelligence Index.

gpt-oss-120b (high) é mais rápido. gpt-oss-120b (high) gera 191.8 tokens por segundo, em comparação com DeepSeek V4.1 Flash (Reasoning, Max Effort), que gera 190.1 tokens por segundo.

Os dois modelos custam $0.18 por 1M de tokens (proporção de 7:2:1 entre tokens de acerto do cache, entrada e saída).

gpt-oss-120b (high) tem a menor latência. O tempo até o primeiro token do gpt-oss-120b (high) é de 0.80s, em comparação com 1.00s no DeepSeek V4.1 Flash (Reasoning, Max Effort).

DeepSeek V4.1 Flash (Reasoning, Max Effort) tem a maior janela de contexto. DeepSeek V4.1 Flash (Reasoning, Max Effort) aceita 1.0M tokens, em comparação com 130k tokens no gpt-oss-120b (high).

DeepSeek V4.1 Flash (Reasoning, Max Effort) vs. gpt-oss-120b (high)

Inteligência

Velocidade

Custo por tarefa

InteligênciaUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Avaliações

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Openness Index

Artificial Analysis Openness Index: Score

Comparações do Intelligence Index

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Uso de tokens

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Custo

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Janela de contexto

Context Window

Context Window for RAG

Context Window

Velocidade

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

Latência

Latency: Time To First Answer Token

Time to First Answer Token

Tempo de resposta de ponta a ponta

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

Tamanho do modelo (somente modelos de pesos abertos)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Perguntas frequentes

Qual é mais inteligente: DeepSeek V4.1 Flash (Reasoning, Max Effort) ou gpt-oss-120b (high)?

Qual é mais rápido: DeepSeek V4.1 Flash (Reasoning, Max Effort) ou gpt-oss-120b (high)?

Qual é mais barato: DeepSeek V4.1 Flash (Reasoning, Max Effort) ou gpt-oss-120b (high)?

Qual tem a menor latência: DeepSeek V4.1 Flash (Reasoning, Max Effort) ou gpt-oss-120b (high)?

Qual tem a maior janela de contexto: DeepSeek V4.1 Flash (Reasoning, Max Effort) ou gpt-oss-120b (high)?