GLM-5-Turbo vs GLM-4.5-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-5-Turbo by Zhipu AI and GLM-4.5-Flash by Zhipu AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GLM-4.5-Flash holds a notable pricing advantage for token ingestion. On output generation, GLM-4.5-Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GLM-5-Turbo provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, GLM-4.5-Flash leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GLM-5-Turbo
심층 추론 모델Zhipu AI의 GLM-5-Turbo 모델은 200K 토큰 컨텍스트 윈도우와 입력 $0.6/1M 토큰, 출력 $1.8/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.60 / 1M
$1.80 / 1M
200K tokens
55%
GLM-4.5-Flash
심층 추론 모델Zhipu AI의 GLM-4.5-Flash 모델은 131K 토큰 컨텍스트 윈도우와 입력 $0.01/1M 토큰, 출력 $0.02/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.01 / 1M
$0.02 / 1M
131.1K tokens
N/A
한눈에 보는 핵심 차이점
- GLM-4.5-Flash is 75.7x cheaper for API usage (input + output tokens combined).
- GLM-5-Turbo has a larger context window (200K vs 131K tokens).
- GLM-4.5-Flash is faster at 170 tok/s vs 95 tok/s.
| Feature | GLM-5-Turbo | GLM-4.5-Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 72 |
Provider | Zhipu AI | Zhipu AI |
Input Price Cost per million input tokens | $0.60 / 1M | $0.01 / 1M |
Output Price Cost per million output tokens | $1.80 / 1M | $0.02 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.34 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 95 tok/s | 170 tok/s |
SWE-bench Verified Real-world software engineering | 55% | N/A |
GPQA Diamond Graduate-level reasoning | 65% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.00 / 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.
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-5-Turbo · Strengths & Key Advantages
- Zhipu AI의 첨단 Hosted Accelerated Frontier Transformer 아키텍처와 최대 200K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.6/1M, 출력 $1.8/1M)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
GLM-4.5-Flash · Strengths & Key Advantages
- Zhipu AI의 첨단 Ultra-High-Speed Distilled Transformer 아키텍처와 최대 131K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.01/1M, 출력 $0.02/1M)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 | GLM-5-Turbo | GLM-4.5-Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.16 | $0.01 |
Output Tokens / Task Tokens generated per task | 22K | 10K |
Reasoning Tokens / Task Thinking tokens burned per task | 7K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-5-Turbo | GLM-4.5-Flash |
|---|---|---|
| Official API Model ID (SDK) | glm-5-turbo | glm-4-5-flash |
| API Protocol | Zhipu AI REST API (OpenAI-Compatible) | Zhipu AI REST API (OpenAI-Compatible) |
| Release Date | 2026-03 | 2025-07 |
| Knowledge Cutoff | January 2026 | May 2025 |
| Architecture | Hosted Accelerated Frontier Transformer | Ultra-High-Speed Distilled Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.6/1M in) across full 200K window | Flat rate ($0.01/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Cached input at $0.001/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Zhipu AI Production Safety & Alignment Verified | Zhipu AI Production Safety & Alignment Verified |
| 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 | text |
| Output Modalities | text | text |
GLM-5-Turbo vs GLM-4.5-Flash: 핵심 요약 문답 (FAQ)
Which is more intelligent, GLM-5-Turbo or GLM-4.5-Flash?
GLM-5-Turbo is more intelligent. It scores 86 compared with 72 on the composite Intelligence Index.
Which model is faster?
GLM-4.5-Flash is faster, generating 170 tokens per second compared with 95 tokens per second.
Which model is cheaper to run?
GLM-4.5-Flash is cheaper at $0.00 per 1M tokens blended, compared with $0.34 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GLM-4.5-Flash has lower latency, with a time to first token of 0.22s compared with 0.42s.
Which model has a larger context window?
GLM-5-Turbo has a larger context window, supporting 200K tokens compared with 131.1K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose GLM-5-Turbo if your priority is High-throughput enterprise applications, code assistants, and automated customer workflows. Its performance in demanding tasks justifies its operational token cost. Choose GLM-4.5-Flash if your workflow prioritizes High-frequency classification, customer chatbots, translation, and automated web scrapers. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does GLM-5-Turbo compare to GLM-4.5-Flash in API token pricing and cost per token?+
GLM-5-Turbo costs $0.60 per 1M input tokens and $1.80 per 1M output tokens, whereas GLM-4.5-Flash costs $0.01 input and $0.02 output. Overall, GLM-4.5-Flash is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GLM-5-Turbo or GLM-4.5-Flash?+
GLM-5-Turbo supports a context window of 200K tokens (~150k words), while GLM-4.5-Flash supports 131K tokens (~98k words). Therefore, GLM-5-Turbo can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GLM-5-Turbo or GLM-4.5-Flash?+
GLM-5-Turbo scores 55% on SWE-bench Verified, while GLM-4.5-Flash scores competitively for software generation. For complex autonomous debugging and agentic engineering, GLM-5-Turbo provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GLM-5-Turbo and GLM-4.5-Flash?+
GLM-5-Turbo features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GLM-4.5-Flash provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-5-Turbo and GLM-4.5-Flash?+
GLM-5-Turbo generates approximately 95 tokens per second, while GLM-4.5-Flash delivers 170 tokens per second. In latency-sensitive production environments, GLM-4.5-Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-5-Turbo and GLM-4.5-Flash to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GLM-4.5-Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GLM-5-Turbo. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs