Head-to-Head Model Comparison

GPT-4.1 vs GPT-6.1 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Проверено исследовательской группой GetInfoUsПоследняя проверка: October 2026Редакционная политика
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Compare GPT-4.1 by OpenAI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.

Прямое сравнение (VS)

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OpenAI

GPT-4.1

Флагманская модель ИИ

GPT-4.1 от OpenAI — передовая модель искусственного интеллекта с контекстным окном 1.0M токенов и стоимостью API $2/1M входящих и $8/1M исходящих токенов для программирования, аналитики и автоматизации.

Intelligence Index48 / 100
Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

54.6%

OpenAI

GPT-6.1 Sol

Модель глубокого рассуждения

GPT-6.1 Sol от OpenAI — передовая модель искусственного интеллекта с контекстным окном 1M токенов и стоимостью API $2.0/1M входящих и $10.0/1M исходящих токенов для программирования, аналитики и автоматизации.

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

Ключевые отличия кратко

  • GPT-4.1 is 1.1x cheaper for API usage (input + output tokens combined).
  • GPT-4.1 has a larger context window (1M vs 1M tokens).
  • GPT-6.1 Sol is faster at 120 tok/s vs 110 tok/s.
  • GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 54.6%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-4.1GPT-6.1 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
4894
Provider
OpenAIOpenAI
Input Price
Cost per million input tokens
$2.00 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$8.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.55 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,571 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
33K128K
Throughput Speed
Tokens generated per second
110 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
54.6%82.3%
GPQA Diamond
Graduate-level reasoning
65%80.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.10 / 1M

Кто лидирует в каждой категории?

At-a-glance decision framework
Coding
GPT-6.1 Sol
+27.7% SWE-bench lead
Cost
GPT-4.1
1.1x cheaper monthly runtime
Speed
GPT-6.1 Sol
+9% faster generation
Context
GPT-4.1
1.05M tokens window

Результаты проверенных бенчмарков

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

GPT-4.1GPT-6.1 Sol
SWE-bench Verified (Autonomous Coding)

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

54.6%vs82.3%
GPQA Diamond (Doctorate-Level Science & Math)

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

65%vs80.8%
MMLU-Pro (Multi-task Language Understanding)

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

81%vs89.9%
LMSYS Chatbot Arena Elo

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

1338ptsvs1519pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

9.8%vs18.1%
Terminal-Bench (Agentic Coding & Terminal Use)

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

30%vs63.6%
SciCode (Scientific Computing)

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

36.5%vs54.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

58.5%vs79.7%
AIME (Advanced Math Competition)

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

55.5%vs93.5%
Long-Context Reasoning

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

70%vs91%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs120 tok/s

Задержка и скорость ответа

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-4.10.48s
GPT-6.1 Sol1.12s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-4.15.03s
GPT-6.1 Sol5.29s

Интерактивный калькулятор стоимости токенов

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-4.1 Monthly API Bill
$10.95 / month
$2/1M in · $8/1M out
GPT-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
GPT-4.1 saves approximately $1.20 / month (10%) under this workload.1.1x Savings

GPT-4.1 · Strengths & Key Advantages

  • Современная архитектура Dense multimodal transformer от OpenAI с поддержкой до 1.0M токенов контекста
  • Оптимальная стоимость использования ($2/1M вход, $8/1M выход) и высокая скорость генерации
  • Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
  • При работе с максимальным контекстом требуется контроль расхода токенов
  • Для узкоспециализированных задач рекомендуется точная настройка системных промптов

GPT-6.1 Sol · Strengths & Key Advantages

  • Современная архитектура Near-Astra unified reasoning model от OpenAI с поддержкой до 1M токенов контекста
  • Оптимальная стоимость использования ($2.0/1M вход, $10.0/1M выход) и высокая скорость генерации
  • Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
  • При работе с максимальным контекстом требуется контроль расхода токенов
  • Для узкоспециализированных задач рекомендуется точная настройка системных промптов

Token Use & Task Economics

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

MetricGPT-4.1GPT-6.1 Sol
Cost per Task
Estimated USD to finish one task
$0.72$2.05
Output Tokens / Task
Tokens generated per task
52K62K
Reasoning Tokens / Task
Thinking tokens burned per task
0K36K

Технические характеристики

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-4.1GPT-6.1 Sol
Official API Model ID (SDK)gpt-4-1gpt-6-1-sol
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2025-042026-09
Knowledge CutoffJune 2024April 2026
ArchitectureDense multimodal transformerNear-Astra unified reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 1048K windowFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.5/1M (no extra cache-write fee)Automatic prefix caching at $0.1/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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 RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

GPT-4.1 vs GPT-6.1 Sol: Быстрые ответы (FAQ)

Which is more intelligent, GPT-4.1 or GPT-6.1 Sol?

GPT-6.1 Sol is more intelligent. It scores 94 compared with 48 on the composite Intelligence Index.

Which model is faster?

GPT-6.1 Sol is faster, generating 120 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

GPT-6.1 Sol is cheaper at $1.47 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4.1 has lower latency, with a time to first token of 0.48s compared with 1.12s.

Which model has a larger context window?

GPT-4.1 has a larger context window, supporting 1.05M tokens compared with 1M tokens.

Вердикт

Choose GPT-4.1 for корпоративной автоматизации, разработки по, ии-агентов и продуктовых решений на базе openai, or choose GPT-6.1 Sol for корпоративной автоматизации, разработки по, ии-агентов и продуктовых решений на базе openai.

Часто задаваемые вопросы (FAQ)

Which is cheaper: GPT-4.1 or GPT-6.1 Sol?+

GPT-4.1 costs $2/1M input and $8/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: GPT-4.1 or GPT-6.1 Sol?+

GPT-4.1 supports 1.0M tokens whereas GPT-6.1 Sol supports 1M 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.

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.

Официальные источники: OpenAI & OpenAI Official Documentation