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Head-to-Head Model Comparison

DeepSeek Chat vs Qwen3.5 9B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
DeepSeek Chat vs Qwen3.5 9B: Benchmarks, Pricing & Speed Comparison

DeepSeek Chat dari DeepSeek dan Qwen3.5 9B dari Alibaba adalah dua model AI unggulan. DeepSeek Chat menawarkan jendela konteks 1M token dengan harga $0.28/$0.42 per 1 juta token, sedangkan Qwen3.5 9B menyediakan konteks 262.1K token seharga $0.00/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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DeepSeek

DeepSeek Chat

Model Dasar Bobot Terbuka

DeepSeek 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/

Intelligence Index50 / 100
Input Token:

$0.28 / 1M

Output Token:

$0.42 / 1M

Context Window:

1M tokens

SWE-bench Verified:

54%

Alibaba

Qwen3.5 9B

Model Dasar Bobot Terbuka

Qwen3.5 9B dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 165 token/detik, indeks kecerdasan 70/100, serta akses gratis

Intelligence Index70 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

  • Qwen3.5 9B is 1.0x cheaper for API usage (input + output tokens combined).
  • DeepSeek Chat has a larger context window (1M vs 262K tokens).
  • Qwen3.5 9B is faster at 165 tok/s vs 75 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureDeepSeek ChatQwen3.5 9B
Intelligence Index
Composite reasoning & knowledge score (0-100)
5070
Provider
DeepSeekAlibaba
Input Price
Cost per million input tokens
$0.28 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$0.42 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.12 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text262.1K tokens~393 A4 pages of text
Max Output Tokens
Per single request
16K8K
Throughput Speed
Tokens generated per second
75 tok/s165 tok/s
SWE-bench Verified
Real-world software engineering
54%N/A
GPQA Diamond
Graduate-level reasoning
65%N/A
Cached Input Price
Prompt caching discount per 1M tokens
$0.03 / 1M$0.00 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Qwen3.5 9B
1.0x cheaper monthly runtime
Speed
Qwen3.5 9B
+120% faster generation
Context
DeepSeek Chat
1M tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

DeepSeek ChatQwen3.5 9B
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

54%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

65%vsN/A
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

79.5%vs68%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1335ptsvs1230pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

10.8%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

36%vsN/A
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

33.5%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

56.5%vsN/A
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

74%vsN/A
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

82.5%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs165 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
DeepSeek Chat0.52s
Qwen3.5 9B0.22s
Est. End-to-End Response (500 tokens)TTFT + generation time
DeepSeek Chat7.19s
Qwen3.5 9B3.25s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
DeepSeek Chat Monthly API Bill
$0.84 / month
$0.28/1M in · $0.42/1M out
Qwen3.5 9B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Qwen3.5 9B saves approximately $0.84 / month (100%) under this workload.1.0x Savings

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

Qwen3.5 9B · Strengths & Key Advantages

  • Jendela konteks 262K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 68% · Arena Elo 1230)
  • Kecepatan generasi tinggi 165 token/detik dengan latensi token pertama (TTFT) hanya 220 ms
  • Bobot terbuka (9B) 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.

MetricDeepSeek ChatQwen3.5 9B
Cost per Task
Estimated USD to finish one task
$0.15$0.00
Output Tokens / Task
Tokens generated per task
35K10K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureDeepSeek ChatQwen3.5 9B
Official API Model ID (SDK)deepseek-chatqwen3-5-9b-instruct
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-122026-02
Knowledge CutoffSeptember 2025December 2025
ArchitectureMulti-Head Latent Attention (MLA) Sparse MoECompact Dense Autoregressive Transformer
Parameters671B Total (37B Active)9B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.28/1M in) across full 1000K windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix caching at $0.028/1M (no extra cache-write fee)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 6 GB VRAM (4-bit Quantized) · 19 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

DeepSeek Chat vs Qwen3.5 9B: Jawaban Cepat (FAQ)

Which is more intelligent, DeepSeek Chat or Qwen3.5 9B?

Qwen3.5 9B is more intelligent. It scores 70 compared with 50 on the composite Intelligence Index.

Which model is faster?

Qwen3.5 9B is faster, generating 165 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Qwen3.5 9B 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 9B has lower latency, with a time to first token of 0.22s 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 DeepSeek Chat (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3.5 9B (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 9B unggul dalam penalaran dan pemrograman, Qwen3.5 9B memberikan kecepatan generasi token lebih tinggi, dan Qwen3.5 9B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, DeepSeek Chat atau Qwen3.5 9B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.5 9B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API DeepSeek Chat vs Qwen3.5 9B?+

DeepSeek Chat mengenakan biaya $0.28 / $0.42 per 1M token input/output, sedangkan Qwen3.5 9B sebesar $0.00 / $0.00. Secara keseluruhan, Qwen3.5 9B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

DeepSeek Chat mendukung konteks 1M token dibandingkan 262.1K token pada Qwen3.5 9B, sementara Qwen3.5 9B unggul dalam kecepatan output token per detik.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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 & Alibaba serta evaluasi benchmark independen (2026).