Qwen3.5 35B-A3B vs DeepSeek Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Qwen3.5 35B-A3B by Alibaba and DeepSeek Chat by DeepSeek 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 35B-A3B holds a notable pricing advantage for token ingestion. On output generation, Qwen3.5 35B-A3B 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 35B-A3B 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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Qwen3.5 35B-A3B
Açık Ağırlıklı ModelAlibaba tarafından geliştirilen Qwen3.5 35B-A3B, 262K token bağlam penceresi ve 1M girdi başına $0, 1M çıktı başına $0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
DeepSeek Chat
Açık Ağırlıklı ModelDeepSeek tarafından geliştirilen DeepSeek Chat, 1M token bağlam penceresi ve 1M girdi başına $0.28, 1M çıktı başına $0.42 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.28 / 1M
$0.42 / 1M
1M tokens
54%
Bir Bakışta Temel Farklar
- Qwen3.5 35B-A3B is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek Chat has a larger context window (1M vs 262K tokens).
- Qwen3.5 35B-A3B is faster at 125 tok/s vs 75 tok/s.
| Feature | Qwen3.5 35B-A3B | DeepSeek Chat |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 77 | 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 | 125 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 54% |
GPQA Diamond Graduate-level reasoning | 59% | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.03 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkEvaluations ongoing
Doğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.5 35B-A3B · Strengths & Key Advantages
- Alibaba imzalı gelişmiş Lightweight Multimodal MoE Transformer mimarisi ve 262K token bağlam kapasitesi
- Dengeli API maliyeti ($0/1M girdi, $0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
DeepSeek Chat · Strengths & Key Advantages
- DeepSeek imzalı gelişmiş Multi-Head Latent Attention (MLA) Sparse MoE mimarisi ve 1M token bağlam kapasitesi
- Dengeli API maliyeti ($0.28/1M girdi, $0.42/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.5 35B-A3B | DeepSeek Chat |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.15 |
Output Tokens / Task Tokens generated per task | 14K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.5 35B-A3B | DeepSeek Chat |
|---|---|---|
| Official API Model ID (SDK) | qwen3-5-35b-a3b-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 | Lightweight Multimodal MoE Transformer | Multi-Head Latent Attention (MLA) Sparse MoE |
| Parameters | 35B total (3B active MoE) | 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) · 22 GB VRAM (4-bit Quantized) · 75 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 35B-A3B vs DeepSeek Chat: Hızlı Yanıtlar (SSS)
Which is more intelligent, Qwen3.5 35B-A3B or DeepSeek Chat?
Qwen3.5 35B-A3B is more intelligent. It scores 77 compared with 50 on the composite Intelligence Index.
Which model is faster?
Qwen3.5 35B-A3B is faster, generating 125 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen3.5 35B-A3B 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 35B-A3B has lower latency, with a time to first token of 0.34s 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.
Sonuç
Verdict & Model Selection Recommendation: Choose Qwen3.5 35B-A3B if your priority is Local desktop AI assistants, edge vision appliances, and real-time screen inspection. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek Chat if your workflow prioritizes Ultra-low-cost conversational agents, large document querying, and automated customer support. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Sıkça Sorulan Sorular (SSS)
How does Qwen3.5 35B-A3B compare to DeepSeek Chat in API token pricing and cost per token?+
Qwen3.5 35B-A3B costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas DeepSeek Chat costs $0.28 input and $0.42 output. Overall, Qwen3.5 35B-A3B 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: Qwen3.5 35B-A3B or DeepSeek Chat?+
Qwen3.5 35B-A3B supports a context window of 262K tokens (~197k words), while DeepSeek Chat supports 1,000K tokens (~750k 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: Qwen3.5 35B-A3B or DeepSeek Chat?+
Qwen3.5 35B-A3B scores at the frontier of coding benchmarks, while DeepSeek Chat scores 54%. 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 Qwen3.5 35B-A3B and DeepSeek Chat?+
Qwen3.5 35B-A3B is optimized for low-latency direct generation. In contrast, DeepSeek Chat focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen3.5 35B-A3B and DeepSeek Chat?+
Qwen3.5 35B-A3B generates approximately 125 tokens per second, while DeepSeek Chat delivers 75 tokens per second. In latency-sensitive production environments, Qwen3.5 35B-A3B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.5 35B-A3B and DeepSeek Chat to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.5 35B-A3B 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.
Resmi kaynaklar: GetInfoUs Independent AI Benchmarks & Official API Specs