DeepSeek Chat vs Qwen3.5 9B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek Chat di DeepSeek e Qwen3.5 9B di Alibaba sono due modelli di IA di riferimento. DeepSeek Chat offre una finestra di contesto di 1M token a $0.28/$0.42 per milione di token, mentre Qwen3.5 9B mette a disposizione 262.1K token di contesto a $0.00/$0.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
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DeepSeek Chat
Modello Base a Pesi ApertiDeepSeek Chat di DeepSeek è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 1M, velocità di 75 token/s, indice di intelligenza di 50/100 e
$0.28 / 1M
$0.42 / 1M
1M tokens
54%
Qwen3.5 9B
Modello Base a Pesi ApertiQwen3.5 9B di Alibaba è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 262K, velocità di 165 token/s, indice di intelligenza di 70/100 e t
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Differenze Chiave a Colpo d’Occhio
- 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.
| Feature | DeepSeek Chat | Qwen3.5 9B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 50 | 70 |
Provider | DeepSeek | Alibaba |
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 text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 75 tok/s | 165 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek Chat · Strengths & Key Advantages
- Finestra di contesto da 1M con capacità massima di output fino a 16K token (SWE-bench Verified 54% · GPQA Diamond 65%)
- Throughput elevato di 75 token/secondo con latenza al primo token (TTFT) di soli 520 ms
- Pesi aperti (671B Total (37B )) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Qwen3.5 9B · Strengths & Key Advantages
- Finestra di contesto da 262K con capacità massima di output fino a 8K token (MMLU-Pro 68% · Arena Elo 1230)
- Throughput elevato di 165 token/secondo con latenza al primo token (TTFT) di soli 220 ms
- Pesi aperti (9B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek Chat | Qwen3.5 9B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.15 | $0.00 |
Output Tokens / Task Tokens generated per task | 35K | 10K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek Chat | Qwen3.5 9B |
|---|---|---|
| Official API Model ID (SDK) | deepseek-chat | qwen3-5-9b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-12 | 2026-02 |
| Knowledge Cutoff | September 2025 | December 2025 |
| Architecture | Multi-Head Latent Attention (MLA) Sparse MoE | Compact Dense Autoregressive Transformer |
| Parameters | 671B 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 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 ($0.28/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.028/1M (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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / 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 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 | text |
| Output Modalities | text | text |
DeepSeek Chat vs Qwen3.5 9B: Risposte Rapide (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.
Verdetto
Sia DeepSeek Chat (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Qwen3.5 9B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Qwen3.5 9B guida nei benchmark di ragionamento e programmazione, Qwen3.5 9B garantisce una generazione più rapida e Qwen3.5 9B offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, DeepSeek Chat o Qwen3.5 9B?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Qwen3.5 9B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra DeepSeek Chat e Qwen3.5 9B?+
DeepSeek Chat costa $0.28 / $0.42 per milione di token di input/output, mentre Qwen3.5 9B costa $0.00 / $0.00. Nel costo combinato, Qwen3.5 9B risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
DeepSeek Chat supporta 1M token di contesto contro 262.1K token di Qwen3.5 9B, mentre Qwen3.5 9B offre una velocità di generazione superiore.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Fonti ufficiali: Documentazione API ufficiale di DeepSeek e Alibaba, schede di sistema e benchmark indipendenti (2026).