GLM-4.7-Flash vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GLM-4.7-Flash by Zhipu AI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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GLM-4.7-Flash
경량 고효율 가성비 모델Zhipu AI의 GLM-4.7-Flash 모델은 극대화된 토큰당 비용 효율성과 빠른 프로덕션 추론 처리에 최적화된 AI 모델로, 200K 컨텍스트 윈도우, 초당 160 토큰의 생성 속도, 종합 지능 지수 74/100점(1M 토큰당 입력 $0.015 / 출력 $0.03)을 제공합니다.
$0.01 / 1M
$0.03 / 1M
200K tokens
N/A
GPT-6.1 Sol
플래그십 프론티어 모델OpenAI의 GPT-6.1 Sol 모델은 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 120 토큰의 생성 속도, 종합 지능 지수 94/100점(1M 토큰당 입력 $2 / 출력 $10)을 제공합니다.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
한눈에 보는 핵심 차이점
- GLM-4.7-Flash is 231.4x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 200K tokens).
- GLM-4.7-Flash is faster at 160 tok/s vs 120 tok/s.
| Feature | GLM-4.7-Flash | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 74 | 94 |
Provider | Zhipu AI | OpenAI |
Input Price Cost per million input tokens | $0.01 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.03 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 160 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 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.
GLM-4.7-Flash · Strengths & Key Advantages
- 200K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (MMLU-Pro 72% · Arena Elo 1270)
- 초당 160 토큰의 빠른 처리 속도와 240ms에 불과한 첫 토큰 지연 시간(TTFT)
- 오픈 웨이트 아키텍처를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
- 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
- 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다
GPT-6.1 Sol · Strengths & Key Advantages
- 1M 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
- 초당 120 토큰의 빠른 처리 속도와 1120ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $2 / 출력 $10)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
- 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
- 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.7-Flash | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $2.05 |
Output Tokens / Task Tokens generated per task | 12K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.7-Flash | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | glm-4-7-flash | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-01 | 2026-09 |
| Knowledge Cutoff | November 2025 | April 2026 |
| Architecture | Optimized High-Speed Lightweight Transformer | Near-Astra unified reasoning model |
| Parameters | Confidential | 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) | Flat rate ($0.015/1M in) across full 200K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.002/1M tokens | 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) · 43 GB VRAM (4-bit Quantized) · 151 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 |
GLM-4.7-Flash vs GPT-6.1 Sol: 핵심 요약 문답 (FAQ)
Which is more intelligent, GLM-4.7-Flash or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 74 on the composite Intelligence Index.
Which model is faster?
GLM-4.7-Flash is faster, generating 160 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
GLM-4.7-Flash is cheaper at $0.01 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GLM-4.7-Flash has lower latency, with a time to first token of 0.24s 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 200K tokens.
종합 평가
Choose GLM-4.7-Flash for 극대화된 토큰당 비용 효율성과 빠른 프로덕션 추론 처리, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose GPT-6.1 Sol for 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..
자주 묻는 질문 (FAQ)
Which is cheaper: GLM-4.7-Flash or GPT-6.1 Sol?+
GLM-4.7-Flash costs $0.015/1M input and $0.03/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: GLM-4.7-Flash or GPT-6.1 Sol?+
GLM-4.7-Flash supports 200K 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.
공식 출처: Zhipu AI & OpenAI Official Documentation