DeepSeek V4.1-Flash vs GLM-5.2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek V4.1-Flash dari DeepSeek dan GLM-5.2 dari Zhipu AI adalah dua model AI unggulan. DeepSeek V4.1-Flash menawarkan jendela konteks 1M token dengan harga $0.30/$1.20 per 1 juta token, sedangkan GLM-5.2 menyediakan konteks 1M token seharga $0.00/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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DeepSeek V4.1-Flash
Model Dasar Bobot TerbukaDeepSeek V4.1-Flash dari DeepSeek adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 110 token/detik, indeks kecerdasan 58/100, serta tarif
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
GLM-5.2
Model Dasar Bobot TerbukaGLM-5.2 dari Zhipu AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 60 token/detik, indeks kecerdasan 91/100, serta akses gratis open-
$0.00 / 1M
$0.00 / 1M
1M tokens
67.5%
Perbedaan Utama Sekilas
- GLM-5.2 is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek V4.1-Flash is faster at 110 tok/s vs 60 tok/s.
- DeepSeek V4.1-Flash leads on SWE-bench coding (74.2% vs 67.5%).
| Feature | DeepSeek V4.1-Flash | GLM-5.2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 91 |
Provider | DeepSeek | Zhipu AI |
Input Price Cost per million input tokens | $0.30 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.18 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 384K | 32K |
Throughput Speed Tokens generated per second | 110 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 74.2% | 67.5% |
GPQA Diamond Graduate-level reasoning | 90.9% | 73.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor 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.
DeepSeek V4.1-Flash · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 384K token (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
- Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 460 ms
- Bobot terbuka (763B total (8B prefill / 16B decode )) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
GLM-5.2 · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 67.5% · GPQA Diamond 73.5%)
- Kecepatan generasi tinggi 60 token/detik dengan latensi token pertama (TTFT) hanya 650 ms
- Bobot terbuka (350B MoE) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- 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 | DeepSeek V4.1-Flash | GLM-5.2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.27 | $0.00 |
Output Tokens / Task Tokens generated per task | 58K | 30K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 12K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek V4.1-Flash | GLM-5.2 |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | glm-5-2 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-06 |
| Knowledge Cutoff | June 2026 | February 2026 |
| Architecture | Causal Encoder-Decoder (20 enc + 20 dec layers) | Massive Million-Token Open MoE Frontier Transformer |
| Parameters | 763B total (8B prefill / 16B decode active) | 350B MoE |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.3/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.006/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 217 GB VRAM (4-bit Quantized) · 753 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
DeepSeek V4.1-Flash vs GLM-5.2: Jawaban Cepat (FAQ)
Which is more intelligent, DeepSeek V4.1-Flash or GLM-5.2?
GLM-5.2 is more intelligent. It scores 91 compared with 58 on the composite Intelligence Index.
Which model is faster?
DeepSeek V4.1-Flash is faster, generating 110 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
GLM-5.2 is cheaper at $0.00 per 1M tokens blended, compared with $0.18 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek V4.1-Flash has lower latency, with a time to first token of 0.46s compared with 0.65s.
Kesimpulan
Baik DeepSeek V4.1-Flash (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GLM-5.2 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GLM-5.2 unggul dalam penalaran dan pemrograman, DeepSeek V4.1-Flash memberikan kecepatan generasi token lebih tinggi, dan GLM-5.2 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, DeepSeek V4.1-Flash atau GLM-5.2?+
Dalam indeks kecerdasan gabungan dan benchmark coding, GLM-5.2 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API DeepSeek V4.1-Flash vs GLM-5.2?+
DeepSeek V4.1-Flash mengenakan biaya $0.30 / $1.20 per 1M token input/output, sedangkan GLM-5.2 sebesar $0.00 / $0.00. Secara keseluruhan, GLM-5.2 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
DeepSeek V4.1-Flash mendukung konteks 1M token dibandingkan 1M token pada GLM-5.2, sementara DeepSeek V4.1-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.
Related reading
- DeepSeek V4.1-Flash vs Gemini 4 Argon: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- GLM-5.1 vs GLM-5.2: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- GPT-6 Luna vs DeepSeek V4.1-Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Grok 4.7 vs DeepSeek V4.1-Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Kimi K3 vs GLM-5.2: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mistral Medium 3.5 vs DeepSeek V4.1-Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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 DeepSeek & Zhipu AI serta evaluasi benchmark independen (2026).