Claude Fable 5.1 vs GPT-6 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude Fable 5.1 by Anthropic against GPT-6 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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Live comparison: no in-depth editorial review has been published for this pair yet.
Claude Fable 5.1
심층 추론 모델Anthropic의 Claude Fable 5.1 모델은 1M 토큰 컨텍스트 윈도우와 입력 $10/1M 토큰, 출력 $50/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$10.00 / 1M
$50.00 / 1M
1M tokens
83.8%
GPT-6 Sol
심층 추론 모델OpenAI의 GPT-6 Sol 모델은 1M 토큰 컨텍스트 윈도우와 입력 $2.0/1M 토큰, 출력 $10.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.1%
한눈에 보는 핵심 차이점
- GPT-6 Sol is 5.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Sol is faster at 116 tok/s vs 65 tok/s.
- Claude Fable 5.1 leads on SWE-bench coding (83.8% vs 82.1%).
| Feature | Claude Fable 5.1 | GPT-6 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 82 | 93 |
Provider | Anthropic | OpenAI |
Input Price Cost per million input tokens | $10.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $50.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $7.17 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 65 tok/s | 116 tok/s |
SWE-bench Verified Real-world software engineering | 83.8% | 82.1% |
GPQA Diamond Graduate-level reasoning | 81.6% | 80.6% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.10 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Fable 5.1 · Strengths & Key Advantages
- Anthropic의 첨단 Frontier Auditable Multimodal Transformer 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $10/1M, 출력 $50/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
GPT-6 Sol · Strengths & Key Advantages
- OpenAI의 첨단 Cost-efficient frontier unified reasoning model 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2.0/1M, 출력 $10.0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Fable 5.1 | GPT-6 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $10.97 | $2.00 |
Output Tokens / Task Tokens generated per task | 72K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 36K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Fable 5.1 | GPT-6 Sol |
|---|---|---|
| Official API Model ID (SDK) | claude-fable-5-1-20260901 | gpt-6-sol |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-09 | 2026-09 |
| Knowledge Cutoff | June 2026 | April 2026 |
| Architecture | Frontier Auditable Multimodal Transformer | Cost-efficient frontier unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 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) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | $0.25/1M cached read (90% off); 5-min default & 1-hour extended TTL | 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 Citations + Code Execution Tool + MCP Connectors | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | 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 |
Claude Fable 5.1 vs GPT-6 Sol: 핵심 요약 문답 (FAQ)
Which is more intelligent, Claude Fable 5.1 or GPT-6 Sol?
GPT-6 Sol is more intelligent. It scores 93 compared with 82 on the composite Intelligence Index.
Which model is faster?
GPT-6 Sol is faster, generating 116 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
GPT-6 Sol is cheaper at $1.47 per 1M tokens blended, compared with $7.17 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-6 Sol has lower latency, with a time to first token of 1.16s compared with 1.45s.
종합 평가
Choose Claude Fable 5.1 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 anthropic 기반 프로덕션 파이프라인, or choose GPT-6 Sol for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 openai 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: Claude Fable 5.1 or GPT-6 Sol?+
Claude Fable 5.1 costs $10/1M input and $50/1M output tokens, while GPT-6 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Claude Fable 5.1 or GPT-6 Sol?+
Claude Fable 5.1 supports 1M tokens whereas GPT-6 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.
공식 출처: Anthropic & OpenAI Official Documentation