DeepSeek Chat vs Qwen3.5 9B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek Chat by DeepSeek and Qwen3.5 9B by Alibaba represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Qwen3.5 9B holds a notable pricing advantage for token ingestion. On output generation, Qwen3.5 9B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, DeepSeek Chat provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Qwen3.5 9B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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DeepSeek Chat
Modello a Pesi ApertiDeepSeek Chat di DeepSeek è un modello di intelligenza artificiale con finestra di contesto da 1M token e tariffazione API di $0.28/1M token in input e $0.42/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.28 / 1M
$0.42 / 1M
1M tokens
54%
Qwen3.5 9B
Modello a Pesi ApertiQwen3.5 9B di Alibaba è un modello di intelligenza artificiale con finestra di contesto da 262K token e tariffazione API di $0/1M token in input e $0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$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
- Architettura Multi-Head Latent Attention (MLA) Sparse MoE avanzata di DeepSeek con supporto fino a 1M token di contesto
- Ottimo rapporto qualità-prezzo ($0.28/1M input, $0.42/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Qwen3.5 9B · Strengths & Key Advantages
- Architettura Compact Dense Autoregressive Transformer avanzata di Alibaba con supporto fino a 262K token di contesto
- Ottimo rapporto qualità-prezzo ($0/1M input, $0/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
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
Verdict & Model Selection Recommendation: Choose DeepSeek Chat if your priority is Ultra-low-cost conversational agents, large document querying, and automated customer support. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3.5 9B if your workflow prioritizes Local edge compute, embedded smart devices, and private offline mobile chat. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does DeepSeek Chat compare to Qwen3.5 9B in API token pricing and cost per token?+
DeepSeek Chat costs $0.28 per 1M input tokens and $0.42 per 1M output tokens, whereas Qwen3.5 9B costs $0.00 input and $0.00 output. Overall, Qwen3.5 9B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: DeepSeek Chat or Qwen3.5 9B?+
DeepSeek Chat supports a context window of 1,000K tokens (~750k words), while Qwen3.5 9B supports 262K tokens (~197k words). Therefore, DeepSeek Chat can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: DeepSeek Chat or Qwen3.5 9B?+
DeepSeek Chat scores 54% on SWE-bench Verified, while Qwen3.5 9B scores competitively for software generation. For complex autonomous debugging and agentic engineering, DeepSeek Chat provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between DeepSeek Chat and Qwen3.5 9B?+
DeepSeek Chat is optimized for low-latency direct generation. In contrast, Qwen3.5 9B focuses on standard fast inference.
How does inference speed (tokens per second) compare between DeepSeek Chat and Qwen3.5 9B?+
DeepSeek Chat generates approximately 75 tokens per second, while Qwen3.5 9B delivers 165 tokens per second. In latency-sensitive production environments, Qwen3.5 9B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek Chat and Qwen3.5 9B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.5 9B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek Chat. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs