GPT-6 Sol vs Aya Expanse 8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-6 Sol by OpenAI against Aya Expanse 8B by Cohere across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
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%
Aya Expanse 8B
오픈 웨이트 모델Cohere의 Aya Expanse 8B 모델은 8K 토큰 컨텍스트 윈도우와 입력 $0.08/1M 토큰, 출력 $0.16/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.08 / 1M
$0.16 / 1M
8.2K tokens
24%
한눈에 보는 핵심 차이점
- Aya Expanse 8B is 41.3x cheaper for API usage (input + output tokens combined).
- GPT-6 Sol has a larger context window (1M vs 8K tokens).
- Aya Expanse 8B is faster at 135 tok/s vs 116 tok/s.
- GPT-6 Sol leads on SWE-bench coding (82.1% vs 24.0%).
| Feature | GPT-6 Sol | Aya Expanse 8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 36 |
Provider | OpenAI | Cohere |
Input Price Cost per million input tokens | $2.00 / 1M | $0.08 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.16 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $0.05 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 128K | 4K |
Throughput Speed Tokens generated per second | 116 tok/s | 135 tok/s |
SWE-bench Verified Real-world software engineering | 82.1% | 24% |
GPQA Diamond Graduate-level reasoning | 80.6% | 40% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.02 / 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.
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
Aya Expanse 8B · Strengths & Key Advantages
- Cohere의 첨단 Compact Multilingual Dense Transformer 아키텍처와 최대 8K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.08/1M, 출력 $0.16/1M)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 | GPT-6 Sol | Aya Expanse 8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.00 | $0.04 |
Output Tokens / Task Tokens generated per task | 62K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Sol | Aya Expanse 8B |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-sol | aya-expanse-8b |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2024-10 |
| Knowledge Cutoff | April 2026 | August 2024 |
| Architecture | Cost-efficient frontier unified reasoning model | Compact Multilingual Dense Transformer |
| Parameters | Confidential | 8B |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 1000K window | Flat rate ($0.08/1M in) across full 8K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.1/1M (no extra cache-write fee) | Cached input at $0.02/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
GPT-6 Sol vs Aya Expanse 8B: 핵심 요약 문답 (FAQ)
Which is more intelligent, GPT-6 Sol or Aya Expanse 8B?
GPT-6 Sol is more intelligent. It scores 93 compared with 36 on the composite Intelligence Index.
Which model is faster?
Aya Expanse 8B is faster, generating 135 tokens per second compared with 116 tokens per second.
Which model is cheaper to run?
Aya Expanse 8B is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aya Expanse 8B has lower latency, with a time to first token of 0.30s compared with 1.16s.
Which model has a larger context window?
GPT-6 Sol has a larger context window, supporting 1M tokens compared with 8.2K tokens.
종합 평가
Choose GPT-6 Sol for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 openai 기반 프로덕션 파이프라인, or choose Aya Expanse 8B for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 cohere 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: GPT-6 Sol or Aya Expanse 8B?+
GPT-6 Sol costs $2/1M input and $10/1M output tokens, while Aya Expanse 8B costs $0.08/1M input and $0.16/1M output tokens.
Which model has a larger context window: GPT-6 Sol or Aya Expanse 8B?+
GPT-6 Sol supports 1M tokens whereas Aya Expanse 8B supports 8K 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: OpenAI & Cohere Official Documentation