GPT-4.1 vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-4.1 by OpenAI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
بدّل وقارن بين أي نموذجين
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Live comparison: no in-depth editorial review has been published for this pair yet.
GPT-4.1
نموذج رائد متقدمنموذج GPT-4.1 المقدم من OpenAI يوفر نافذة سياق تصل إلى 1.0M رمز، بتكلفة $2 لكل مليون رمز إدخال و$8 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$2.00 / 1M
$8.00 / 1M
1.05M tokens
54.6%
GPT-6.1 Sol
نموذج استدلال عميقنموذج GPT-6.1 Sol المقدم من OpenAI يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $2.0 لكل مليون رمز إدخال و$10.0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
أهم الفروقات في لمحة
- 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%).
| Feature | GPT-4.1 | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 94 |
Provider | OpenAI | OpenAI |
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 text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 110 tok/s | 120 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 |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-4.1 · Strengths & Key Advantages
- بنية Dense multimodal transformer متطورة من OpenAI مع سعة سياق تبلغ 1.0M رمز
- كفاءة عالية في التكلفة ($2/1M إدخال و$8/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
GPT-6.1 Sol · Strengths & Key Advantages
- بنية Near-Astra unified reasoning model متطورة من OpenAI مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($2.0/1M إدخال و$10.0/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-4.1 | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.72 | $2.05 |
Output Tokens / Task Tokens generated per task | 52K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-4.1 | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | gpt-4-1 | gpt-6-1-sol |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | June 2024 | April 2026 |
| Architecture | Dense multimodal transformer | Near-Astra unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 1048K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic 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 Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · Vision (~258–765 tokens per image tile) | 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, vision | text, vision |
| Output Modalities | text | text |
GPT-4.1 vs GPT-6.1 Sol: إجابات سريعة (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.
الحكم النهائي
Choose GPT-4.1 for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات openai الإنتاجية, or choose GPT-6.1 Sol for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات openai الإنتاجية.
الأسئلة الشائعة (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.
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.
المصادر الرسمية: OpenAI & OpenAI Official Documentation