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DeepSeek Chat vs Qwen3.5 9B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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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 von DeepSeek und Qwen3.5 9B von Alibaba gehören zu den führenden KI-Modellen. DeepSeek Chat bietet ein Kontextfenster von 1M Tokens bei API-Preisen von $0.28/$0.42 pro 1M Tokens, während Qwen3.5 9B 262.1K Tokens Kontext zu $0.00/$0.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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DeepSeek

DeepSeek Chat

Open-Weights-Basismodell

DeepSeek Chat von DeepSeek ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 1M Kontextfenster, 75 Token/s Durchsatz, einem Intelligenz-Index von 50/100 und $0.28/$0.42 pro 1M Token.

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

Open-Weights-Basismodell

Qwen3.5 9B von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 262K Kontextfenster, 165 Token/s Durchsatz, einem Intelligenz-Index von 70/100 und kostenlosem Open-Weights-Zuga

Intelligence Index70 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

  • 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

  • 1M Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 54% · GPQA Diamond 65%)
  • Hohe Generierungsgeschwindigkeit von 75 Token/Sekunde bei nur 520 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (671B Total (37B )) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Qwen3.5 9B · Strengths & Key Advantages

  • 262K Kontextfenster und bis zu 8K maximale Output-Token (MMLU-Pro 68% · Arena Elo 1230)
  • Hohe Generierungsgeschwindigkeit von 165 Token/Sekunde bei nur 220 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (9B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl DeepSeek Chat (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen3.5 9B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen3.5 9B führt bei komplexem Reasoning und Coding, Qwen3.5 9B liefert den höheren Token-Durchsatz und Qwen3.5 9B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – DeepSeek Chat oder Qwen3.5 9B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen3.5 9B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von DeepSeek Chat und Qwen3.5 9B?+

DeepSeek Chat kostet $0.28 / $0.42 pro 1M Input-/Output-Tokens, Qwen3.5 9B liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Qwen3.5 9B günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

DeepSeek Chat bietet 1M Tokens Kontext gegenüber 262.1K Tokens bei Qwen3.5 9B. Bei der Generierungsgeschwindigkeit liegt Qwen3.5 9B vorn.

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.

Offizielle Quellen: Offizielle API-Dokumentation von DeepSeek und Alibaba, Systemkarten und unabhängige Benchmark-Auswertungen (2026).