Llama 3.2 1B vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.2 1B by Meta against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Llama 3.2 1B
Базовая модель с открытыми весамиLlama 3.2 1B от Meta — специализированная ИИ-модель для самостоятельного развертывания на своих серверах, дообучения и суверенитета данных, обеспечивающая контекстное окно 128K, скорость 320 ток/с и индекс интеллекта 42/100 (бесплатным дост
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
GPT-6.1 Sol
Флагманская передовая модельGPT-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
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Ключевые отличия кратко
- Llama 3.2 1B is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 128K tokens).
- Llama 3.2 1B is faster at 320 tok/s vs 120 tok/s.
| Feature | Llama 3.2 1B | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 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 | 4K | 128K |
Throughput Speed Tokens generated per second | 320 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 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 frameworkEvaluations ongoing
Результаты проверенных бенчмарков
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.2 1B · Strengths & Key Advantages
- Контекстное окно 128K и генерация до 4K выходных токенов за ответ (MMLU-Pro 46% · Arena Elo 1110)
- Высокая скорость генерации 320 токенов/сек при задержке до первого токена (TTFT) всего 120 мс
- Открытые веса модели (1B) 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 1B | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.05 |
Output Tokens / Task Tokens generated per task | 2K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Технические характеристики
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.2 1B | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-1B | 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 | Pruned and distilled ultra-compact transformer | Near-Astra unified reasoning model |
| Parameters | 1B | 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) | 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) · 2 GB VRAM (4-bit Quantized) · 4 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 | text, vision |
| Output Modalities | text | text |
Llama 3.2 1B vs GPT-6.1 Sol: Быстрые ответы (FAQ)
Which is more intelligent, Llama 3.2 1B or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 42 on the composite Intelligence Index.
Which model is faster?
Llama 3.2 1B is faster, generating 320 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Llama 3.2 1B 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 1B has lower latency, with a time to first token of 0.12s 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 1B for оптимально для самостоятельного развертывания на своих серверах, дообучения и суверенитета данных, 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 1B or GPT-6.1 Sol?+
Llama 3.2 1B 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 1B or GPT-6.1 Sol?+
Llama 3.2 1B supports 128K tokens whereas GPT-6.1 Sol supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: Meta & OpenAI Official Documentation