Llama 2 (70B) vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 2 (70B) by Meta against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Llama 2 (70B)
Modello a Pesi ApertiLlama 2 (70B) di Meta è un modello di intelligenza artificiale con finestra di contesto da 4K token e tariffazione API di $0.4/1M token in input e $0.8/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.40 / 1M
$0.80 / 1M
4.1K tokens
N/A
GPT-6.1 Sol
Modello di Ragionamento ProfondoGPT-6.1 Sol di OpenAI è un modello di intelligenza artificiale con finestra di contesto da 1M token e tariffazione API di $2.0/1M token in input e $10.0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Differenze Chiave a Colpo d’Occhio
- Llama 2 (70B) is 8.3x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 4K tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 55 tok/s.
| Feature | Llama 2 (70B) | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 31 | 94 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $0.40 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.80 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.23 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 4.1K tokens~6 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 | 55 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 | $0.10 / 1M | $0.10 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 2 (70B) · Strengths & Key Advantages
- Architettura Grouped-Query Attention (GQA) Transformer avanzata di Meta con supporto fino a 4K token di contesto
- Ottimo rapporto qualità-prezzo ($0.4/1M input, $0.8/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
GPT-6.1 Sol · Strengths & Key Advantages
- Architettura Near-Astra unified reasoning model avanzata di OpenAI con supporto fino a 1M token di contesto
- Ottimo rapporto qualità-prezzo ($2.0/1M input, $10.0/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 2 (70B) | 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 |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 2 (70B) | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-2-(70B) | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2023-07 | 2026-09 |
| Knowledge Cutoff | September 2022 | April 2026 |
| Architecture | Grouped-Query Attention (GQA) Transformer | Near-Astra unified reasoning model |
| Parameters | 70B (also 7B, 13B) | 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) | Flat rate ($0.4/1M in) across full 4K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | 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) · 43 GB VRAM (4-bit Quantized) · 151 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 2 (70B) vs GPT-6.1 Sol: Risposte Rapide (FAQ)
Which is more intelligent, Llama 2 (70B) or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 31 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 55 tokens per second.
Which model is cheaper to run?
Llama 2 (70B) is cheaper at $0.23 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 2 (70B) has lower latency, with a time to first token of 0.60s 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 4.1K tokens.
Verdetto
Choose Llama 2 (70B) for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione meta, or choose GPT-6.1 Sol for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione openai.
Domande Frequenti (FAQ)
Which is cheaper: Llama 2 (70B) or GPT-6.1 Sol?+
Llama 2 (70B) costs $0.4/1M input and $0.8/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Llama 2 (70B) or GPT-6.1 Sol?+
Llama 2 (70B) supports 4K 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.
Fonti ufficiali: Meta & OpenAI Official Documentation