Head-to-Head Model Comparison

Llama 3.2 90B Vision 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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Llama 3.2 90B VisionProfile

GPT-6.1 SolProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

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

Popular Matchups:

Llama 3.2 90B Vision

Нативная мультимодальная ИИ

Llama 3.2 90B Vision от Meta — специализированная ИИ-модель для нативной мультимодальной обработки текста и изображений, а также анализа документов, обеспечивающая контекстное окно 128K, скорость 75 ток/с и индекс интеллекта 44/100 (бесплат

Intelligence Index44 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

28.5%

OpenAI

GPT-6.1 Sol

Флагманская передовая модель

GPT-6.1 Sol от OpenAI — специализированная ИИ-модель для передового корпоративного рассуждения, программирования и промышленных ИИ-систем, обеспечивающая контекстное окно 1M, скорость 120 ток/с и индекс интеллекта 94/100 (по цене $2/$10 за

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

Llama 3.2 90B Vision is 1.0x cheaper for API usage (input + output tokens combined).
GPT-6.1 Sol has a larger context window (1M vs 128K tokens).
GPT-6.1 Sol is faster at 120 tok/s vs 75 tok/s.
GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 28.5%).

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

Feature	Llama 3.2 90B Vision	GPT-6.1 Sol
Intelligence Index Composite reasoning & knowledge score (0-100)	44	94
Provider	Meta	OpenAI
Input Price Cost per million input tokens	$0.00 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$1.47 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	8K	128K
Throughput Speed Tokens generated per second	75 tok/s	120 tok/s
SWE-bench Verified Real-world software engineering	28.5%	82.3%
GPQA Diamond Graduate-level reasoning	48.2%	80.8%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.10 / 1M

Кто лидирует в каждой категории?

At-a-glance decision framework

Coding

GPT-6.1 Sol

+53.8% SWE-bench lead

Cost

Llama 3.2 90B Vision

1.0x cheaper monthly runtime

Speed

GPT-6.1 Sol

+60% faster generation

Context

GPT-6.1 Sol

1M tokens window

Результаты проверенных бенчмарков

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

Llama 3.2 90B VisionGPT-6.1 Sol

SWE-bench Verified (Autonomous Coding)

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

28.5%vs82.3%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.2%vs80.8%

MMLU-Pro (Multi-task Language Understanding)

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

73.5%vs89.9%

LMSYS Chatbot Arena Elo

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

1260ptsvs1519pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs18.1%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs63.6%

SciCode (Scientific Computing)

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

N/Avs54.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.7%

AIME (Advanced Math Competition)

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

N/Avs93.5%

Long-Context Reasoning

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

N/Avs91%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 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

Llama 3.2 90B Vision0.58s

GPT-6.1 Sol1.12s

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

Llama 3.2 90B Vision7.25s

GPT-6.1 Sol5.29s

Интерактивный калькулятор стоимости токенов

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)

Llama 3.2 90B Vision Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

GPT-6.1 Sol Monthly API Bill

$12.15 / month

$2/1M in · $10/1M out

Llama 3.2 90B Vision saves approximately $12.15 / month (100%) under this workload.1.0x Savings

Llama 3.2 90B Vision · Strengths & Key Advantages

Контекстное окно 128K и генерация до 8K выходных токенов за ответ (SWE-bench Verified 28.5% · GPQA Diamond 48.2%)
Высокая скорость генерации 75 токенов/сек при задержке до первого токена (TTFT) всего 580 мс
Открытые веса модели (90B) для локального хостинга и дообучения через vLLM, SGLang или Ollama

Considerations & Trade-offs

Для локального запуска в полной точности требуются GPU-серверы с достаточным объемом видеопамяти (VRAM)
В сверхсложных научных доказательствах специализированные тяжелые модели рассуждения могут показывать большую глубину

GPT-6.1 Sol · Strengths & Key Advantages

Контекстное окно 1M и генерация до 128K выходных токенов за ответ (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
Высокая скорость генерации 120 токенов/сек при задержке до первого токена (TTFT) всего 1120 мс
Выгодная тарификация API (по цене $2/$10 за 1M токенов) с поддержкой кэширования промптов и строгих JSON-схем

Considerations & Trade-offs

Проприетарная облачная модель без возможности скачивания весов для локального запуска в закрытом контуре
При больших объемах трафика рекомендуется использовать кэширование промптов и Batch API для снижения затрат

Token Use & Task Economics

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

Metric	Llama 3.2 90B Vision	GPT-6.1 Sol
Cost per Task Estimated USD to finish one task	$0.20	$2.05
Output Tokens / Task Tokens generated per task	24K	62K
Reasoning Tokens / Task Thinking tokens burned per task	0K	36K

Технические характеристики

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.2 90B Vision	GPT-6.1 Sol
Official API Model ID (SDK)	meta-llama/Llama-3.2-90B-Vision	gpt-6-1-sol
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2024-09	2026-09
Knowledge Cutoff	December 2023	April 2026
Architecture	Multimodal Adapter-Fused GQA Transformer	Near-Astra unified reasoning model
Parameters	90B	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix caching at $0.1/1M (no extra cache-write fee)
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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text 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 VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 56 GB VRAM (4-bit Quantized) · 194 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision	text, vision
Output Modalities	text	text

Llama 3.2 90B Vision vs GPT-6.1 Sol: Быстрые ответы (FAQ)

Which is more intelligent, Llama 3.2 90B Vision or GPT-6.1 Sol?

GPT-6.1 Sol is more intelligent. It scores 94 compared with 44 on the composite Intelligence Index.

Which model is faster?

GPT-6.1 Sol is faster, generating 120 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Llama 3.2 90B Vision is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.2 90B Vision has lower latency, with a time to first token of 0.58s compared with 1.12s.

Which model has a larger context window?

GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 128K tokens.

Вердикт

Choose Llama 3.2 90B Vision for оптимально для нативной мультимодальной обработки текста и изображений, а также анализа документов, корпоративных rag-пайплайнов и масштабируемых api-сервисов., or choose GPT-6.1 Sol for оптимально для передового корпоративного рассуждения, программирования и промышленных ии-систем, корпоративных rag-пайплайнов и масштабируемых api-сервисов..

Часто задаваемые вопросы (FAQ)

Which is cheaper: Llama 3.2 90B Vision or GPT-6.1 Sol?+

Llama 3.2 90B Vision costs $0/1M input and $0/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: Llama 3.2 90B Vision or GPT-6.1 Sol?+

Llama 3.2 90B Vision supports 128K 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.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Llama 3.2 90B Vision vs GPT-6.1 Sol

Выбирайте и сравнивайте любые модели

Llama 3.2 90B Vision

GPT-6.1 Sol

Кто лидирует в каждой категории?

Результаты проверенных бенчмарков

Задержка и скорость ответа

Интерактивный калькулятор стоимости токенов

Llama 3.2 90B Vision · Strengths & Key Advantages

Considerations & Trade-offs

GPT-6.1 Sol · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Технические характеристики

Llama 3.2 90B Vision vs GPT-6.1 Sol: Быстрые ответы (FAQ)

Вердикт

Часто задаваемые вопросы (FAQ)

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