Phi-4 vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Phi-4 by Microsoft against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Phi-4
Açık Ağırlıklı ModelMicrosoft tarafından geliştirilen Phi-4, 16K token bağlam penceresi ve 1M girdi başına $0, 1M çıktı başına $0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.00 / 1M
$0.00 / 1M
16.4K tokens
N/A
GPT-6.1 Sol
Derin Akıl Yürütme ModeliOpenAI tarafından geliştirilen GPT-6.1 Sol, 1M token bağlam penceresi ve 1M girdi başına $2.0, 1M çıktı başına $10.0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Bir Bakışta Temel Farklar
- Phi-4 is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 16K tokens).
- Phi-4 is faster at 135 tok/s vs 120 tok/s.
| Feature | Phi-4 | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 94 |
Provider | Microsoft | 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 | 16.4K tokens~25 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 | 135 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | 55% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkEvaluations ongoing
Doğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Phi-4 · Strengths & Key Advantages
- Microsoft imzalı gelişmiş Synthetic-Data Dense Reasoning Transformer mimarisi ve 16K token bağlam kapasitesi
- Dengeli API maliyeti ($0/1M girdi, $0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
GPT-6.1 Sol · Strengths & Key Advantages
- OpenAI imzalı gelişmiş Near-Astra unified reasoning model mimarisi ve 1M token bağlam kapasitesi
- Dengeli API maliyeti ($2.0/1M girdi, $10.0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Phi-4 | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.05 |
Output Tokens / Task Tokens generated per task | 12K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 3K | 36K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Phi-4 | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | phi-4 | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-12 | 2026-09 |
| Knowledge Cutoff | June 2024 | April 2026 |
| Architecture | Synthetic-Data Dense Reasoning Transformer | Near-Astra unified reasoning model |
| Parameters | 14B | 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) · 9 GB VRAM (4-bit Quantized) · 30 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 |
Phi-4 vs GPT-6.1 Sol: Hızlı Yanıtlar (SSS)
Which is more intelligent, Phi-4 or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 80 on the composite Intelligence Index.
Which model is faster?
Phi-4 is faster, generating 135 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Phi-4 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?
Phi-4 has lower latency, with a time to first token of 0.27s 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 16.4K tokens.
Sonuç
Choose Phi-4 for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve microsoft tabanlı üretim sistemleri, or choose GPT-6.1 Sol for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve openai tabanlı üretim sistemleri.
Sıkça Sorulan Sorular (SSS)
Which is cheaper: Phi-4 or GPT-6.1 Sol?+
Phi-4 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: Phi-4 or GPT-6.1 Sol?+
Phi-4 supports 16K 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.
Resmi kaynaklar: Microsoft & OpenAI Official Documentation