GLM-4.6 vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GLM-4.6 by Zhipu AI against Claude Opus 5.5 by Anthropic 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.
GLM-4.6
오픈 웨이트 파운데이션 모델Zhipu AI의 GLM-4.6 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 205K 컨텍스트 윈도우, 초당 70 토큰의 생성 속도, 종합 지능 지수 82/100점(무료 오픈 웨이트 제공)을 제공합니다.
$0.00 / 1M
$0.00 / 1M
204.8K tokens
48%
Claude Opus 5.5
플래그십 프론티어 모델Anthropic의 Claude Opus 5.5 모델은 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 78 토큰의 생성 속도, 종합 지능 지수 94/100점(1M 토큰당 입력 $4 / 출력 $20)을 제공합니다.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
한눈에 보는 핵심 차이점
- GLM-4.6 is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 205K tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 70 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 48.0%).
| Feature | GLM-4.6 | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 82 | 94 |
Provider | Zhipu AI | Anthropic |
Input Price Cost per million input tokens | $0.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 204.8K tokens~307 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 | 70 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 48% | 84.6% |
GPQA Diamond Graduate-level reasoning | 63.5% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 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.
GLM-4.6 · Strengths & Key Advantages
- 205K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (SWE-bench Verified 48% · GPQA Diamond 63.5%)
- 초당 70 토큰의 빠른 처리 속도와 560ms에 불과한 첫 토큰 지연 시간(TTFT)
- 오픈 웨이트 아키텍처 (150B MoE)를 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
Claude Opus 5.5 · Strengths & Key Advantages
- 1M 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
- 초당 78 토큰의 빠른 처리 속도와 1400ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $4 / 출력 $20)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-4.6 | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $3.85 |
Output Tokens / Task Tokens generated per task | 18K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 42K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.6 | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | glm-4-6 | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-09 | 2026-09 |
| Knowledge Cutoff | July 2025 | June 2026 |
| Architecture | Advanced Mixture-of-Experts Enterprise Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 150B MoE | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| 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 Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| 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) · 93 GB VRAM (4-bit Quantized) · 323 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) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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-4.6 vs Claude Opus 5.5: 핵심 요약 문답 (FAQ)
Which is more intelligent, GLM-4.6 or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 82 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 70 tokens per second.
Which model is cheaper to run?
GLM-4.6 is cheaper at $0.00 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GLM-4.6 has lower latency, with a time to first token of 0.56s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 204.8K tokens.
종합 평가
Choose GLM-4.6 for 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Claude Opus 5.5 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-4.6 or Claude Opus 5.5?+
GLM-4.6 costs $0/1M input and $0/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: GLM-4.6 or Claude Opus 5.5?+
GLM-4.6 supports 205K tokens whereas Claude Opus 5.5 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 & Anthropic Official Documentation