GPT-4 vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-4 by OpenAI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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GPT-4
Modello Flagship di FrontieraGPT-4 di OpenAI è un modello di intelligenza artificiale con finestra di contesto da 8K token e tariffazione API di $30/1M token in input e $60/1M in output, ideale per coding, ragionamento e automazione aziendale.
$30.00 / 1M
$60.00 / 1M
8.2K tokens
21%
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
- GPT-6.1 Sol is 9.1x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 8K tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 35 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 21.0%).
| Feature | GPT-4 | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 94 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $30.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $60.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $17.25 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 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 | 35 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 21% | 82.3% |
GPQA Diamond Graduate-level reasoning | 35.8% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $7.50 / 1M | $0.10 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi 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.
GPT-4 · Strengths & Key Advantages
- Architettura Mixture-of-Experts (MoE) avanzata di OpenAI con supporto fino a 8K token di contesto
- Ottimo rapporto qualità-prezzo ($30/1M input, $60/1M 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 | GPT-4 | 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 | GPT-4 | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | gpt-4 | gpt-6-1-sol |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2023-03 | 2026-09 |
| Knowledge Cutoff | September 2021 | April 2026 |
| Architecture | Mixture-of-Experts (MoE) | Near-Astra unified reasoning model |
| Parameters | Confidential (est. 1.8T MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($30/1M in) across full 8K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $7.5/1M (no extra cache-write fee) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | Cloud-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 Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
GPT-4 vs GPT-6.1 Sol: Risposte Rapide (FAQ)
Which is more intelligent, GPT-4 or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 38 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 35 tokens per second.
Which model is cheaper to run?
GPT-6.1 Sol is cheaper at $1.47 per 1M tokens blended, compared with $17.25 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-6.1 Sol has lower latency, with a time to first token of 1.12s compared with 1.20s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 8.2K tokens.
Verdetto
Choose GPT-4 for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione openai, 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: GPT-4 or GPT-6.1 Sol?+
GPT-4 costs $30/1M input and $60/1M 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 or GPT-6.1 Sol?+
GPT-4 supports 8K 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: OpenAI & OpenAI Official Documentation