GLM-5-Turbo vs GLM-4.5-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-5-Turbo dari Zhipu AI dan GLM-4.5-Flash dari Zhipu AI adalah dua model AI unggulan. GLM-5-Turbo menawarkan jendela konteks 200K token dengan harga $0.60/$1.80 per 1 juta token, sedangkan GLM-4.5-Flash menyediakan konteks 131.1K token seharga $0.01/$0.02 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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GLM-5-Turbo
Inferensi Super CepatGLM-5-Turbo dari Zhipu AI adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 200K, kecepatan 95 token/detik, indeks kecerdasan 86/100, serta tarif
$0.60 / 1M
$1.80 / 1M
200K tokens
55%
GLM-4.5-Flash
Inferensi Super CepatGLM-4.5-Flash dari Zhipu AI adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 131K, kecepatan 170 token/detik, indeks kecerdasan 72/100, serta ta
$0.01 / 1M
$0.02 / 1M
131.1K tokens
N/A
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-5-Turbo · Strengths & Key Advantages
- Jendela konteks 200K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 55% · GPQA Diamond 65%)
- Kecepatan generasi tinggi 95 token/detik dengan latensi token pertama (TTFT) hanya 420 ms
- Skema harga API kompetitif (tarif $0.6/$1.8 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
GLM-4.5-Flash · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 68% · Arena Elo 1240)
- Kecepatan generasi tinggi 170 token/detik dengan latensi token pertama (TTFT) hanya 220 ms
- Skema harga API kompetitif (tarif $0.01/$0.02 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik GLM-5-Turbo (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GLM-4.5-Flash (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GLM-5-Turbo unggul dalam penalaran dan pemrograman, GLM-4.5-Flash memberikan kecepatan generasi token lebih tinggi, dan GLM-4.5-Flash lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, GLM-5-Turbo atau GLM-4.5-Flash?+
Dalam indeks kecerdasan gabungan dan benchmark coding, GLM-5-Turbo memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API GLM-5-Turbo vs GLM-4.5-Flash?+
GLM-5-Turbo mengenakan biaya $0.60 / $1.80 per 1M token input/output, sedangkan GLM-4.5-Flash sebesar $0.01 / $0.02. Secara keseluruhan, GLM-4.5-Flash lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
GLM-5-Turbo mendukung konteks 200K token dibandingkan 131.1K token pada GLM-4.5-Flash, sementara GLM-4.5-Flash unggul dalam kecepatan output token per detik.
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.
Sumber resmi: Dokumentasi API resmi Zhipu AI & Zhipu AI serta evaluasi benchmark independen (2026).