GLM-4.5-Flash vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GLM-4.5-Flash by Zhipu AI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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GLM-4.5-Flash
Model Penalaran MendalamGLM-4.5-Flash dari Zhipu AI adalah model AI canggih dengan jendela konteks 131K token serta harga API $0.01/1M token input dan $0.02/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$0.01 / 1M
$0.02 / 1M
131.1K tokens
N/A
GPT-6.1 Sol
Model Penalaran MendalamGPT-6.1 Sol dari OpenAI adalah model AI canggih dengan jendela konteks 1M token serta harga API $2.0/1M token input dan $10.0/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Perbedaan Utama Sekilas
- GLM-4.5-Flash is 352.2x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 131K tokens).
- GLM-4.5-Flash is faster at 170 tok/s vs 120 tok/s.
| Feature | GLM-4.5-Flash | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 72 | 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 | $0.02 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 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 | 170 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 |
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.
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.
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-4.5-Flash · Strengths & Key Advantages
- Arsitektur Ultra-High-Speed Distilled Transformer mutakhir dari Zhipu AI dengan dukungan konteks 131K token
- Efisiensi biaya API yang kompetitif ($0.01/1M input, $0.02/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
GPT-6.1 Sol · Strengths & Key Advantages
- Arsitektur Near-Astra unified reasoning model mutakhir dari OpenAI dengan dukungan konteks 1M token
- Efisiensi biaya API yang kompetitif ($2.0/1M input, $10.0/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.5-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 | 10K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.5-Flash | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | glm-4-5-flash | gpt-6-1-sol |
| API Protocol | Zhipu AI REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-07 | 2026-09 |
| Knowledge Cutoff | May 2025 | April 2026 |
| Architecture | Ultra-High-Speed Distilled Transformer | Near-Astra unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.01/1M in) across full 131K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.001/1M 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 | API Prompt Engineering & RAG Distillation | 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 | 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 | OpenAI Preparedness Framework Audited & System Card Published |
| 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, vision |
| Output Modalities | text | text |
GLM-4.5-Flash vs GPT-6.1 Sol: Jawaban Cepat (FAQ)
Which is more intelligent, GLM-4.5-Flash or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 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 120 tokens per second.
Which model is cheaper to run?
GLM-4.5-Flash is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (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 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 131.1K tokens.
Kesimpulan
Choose GLM-4.5-Flash for otomasi perusahaan, pengembangan perangkat lunak, agen ai, dan alur kerja produksi berbasis zhipu ai, or choose GPT-6.1 Sol for otomasi perusahaan, pengembangan perangkat lunak, agen ai, dan alur kerja produksi berbasis openai.
Pertanyaan Umum (FAQ)
Which is cheaper: GLM-4.5-Flash or GPT-6.1 Sol?+
GLM-4.5-Flash costs $0.01/1M input and $0.02/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: GLM-4.5-Flash or GPT-6.1 Sol?+
GLM-4.5-Flash supports 131K 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.
Sumber resmi: Zhipu AI & OpenAI Official Documentation