Qwen3.5 27B vs DeepSeek Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.5 27B dari Alibaba dan DeepSeek Chat dari DeepSeek adalah dua model AI unggulan. Qwen3.5 27B menawarkan jendela konteks 262.1K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan DeepSeek Chat menyediakan konteks 1M token seharga $0.28/$0.42 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Qwen3.5 27B
Model Dasar Bobot TerbukaQwen3.5 27B dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 90 token/detik, indeks kecerdasan 75/100, serta akses gratis
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
DeepSeek Chat
Model Dasar Bobot TerbukaDeepSeek Chat dari DeepSeek adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 75 token/detik, indeks kecerdasan 50/100, serta tarif $0.28/
$0.28 / 1M
$0.42 / 1M
1M tokens
54%
Perbedaan Utama Sekilas
- Qwen3.5 27B is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek Chat has a larger context window (1M vs 262K tokens).
- Qwen3.5 27B is faster at 90 tok/s vs 75 tok/s.
| Feature | Qwen3.5 27B | DeepSeek Chat |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 50 |
Provider | Alibaba | DeepSeek |
Input Price Cost per million input tokens | $0.00 / 1M | $0.28 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.42 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.12 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 90 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 54% |
GPQA Diamond Graduate-level reasoning | 56.5% | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.03 / 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.
Qwen3.5 27B · Strengths & Key Advantages
- Jendela konteks 262K dengan kapasitas output maksimum hingga 8K token (GPQA Diamond 56.5% · MMLU-Pro 74%)
- Kecepatan generasi tinggi 90 token/detik dengan latensi token pertama (TTFT) hanya 410 ms
- Bobot terbuka (27B) 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
DeepSeek Chat · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 54% · GPQA Diamond 65%)
- Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 520 ms
- Bobot terbuka (671B Total (37B )) 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 | Qwen3.5 27B | DeepSeek Chat |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.15 |
Output Tokens / Task Tokens generated per task | 13K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.5 27B | DeepSeek Chat |
|---|---|---|
| Official API Model ID (SDK) | qwen3-5-27b-instruct | deepseek-chat |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-02 | 2025-12 |
| Knowledge Cutoff | December 2025 | September 2025 |
| Architecture | Dense Multimodal Autoregressive Transformer | Multi-Head Latent Attention (MLA) Sparse MoE |
| Parameters | 27B | 671B Total (37B Active) |
| 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.28/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.028/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · 17 GB VRAM (4-bit Quantized) · 58 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 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 |
Qwen3.5 27B vs DeepSeek Chat: Jawaban Cepat (FAQ)
Which is more intelligent, Qwen3.5 27B or DeepSeek Chat?
Qwen3.5 27B is more intelligent. It scores 75 compared with 50 on the composite Intelligence Index.
Which model is faster?
Qwen3.5 27B is faster, generating 90 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen3.5 27B is cheaper at $0.00 per 1M tokens blended, compared with $0.12 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.5 27B has lower latency, with a time to first token of 0.41s compared with 0.52s.
Which model has a larger context window?
DeepSeek Chat has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Kesimpulan
Baik Qwen3.5 27B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek Chat (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3.5 27B unggul dalam penalaran dan pemrograman, Qwen3.5 27B memberikan kecepatan generasi token lebih tinggi, dan Qwen3.5 27B lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Qwen3.5 27B atau DeepSeek Chat?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.5 27B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Qwen3.5 27B vs DeepSeek Chat?+
Qwen3.5 27B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan DeepSeek Chat sebesar $0.28 / $0.42. Secara keseluruhan, Qwen3.5 27B lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Qwen3.5 27B mendukung konteks 262.1K token dibandingkan 1M token pada DeepSeek Chat, sementara Qwen3.5 27B 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 Chat vs Qwen3.5 9B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Qwen3.5 35B-A3B vs DeepSeek Chat: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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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 Alibaba & DeepSeek serta evaluasi benchmark independen (2026).