Qwen3.5 35B-A3B vs DeepSeek Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
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
VLM eficientecompacto abierto multimodal MoE requiring merely 3B activo parámetros por token, enabling rápido local visión-idioma ejecución.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
DeepSeek Chat
Instrucción abiertaalto-eficiencia 1M-contexto instrucción-tuned conversación modelo based en el DeepSeek V3.2 core, que ofrece extreme precio disruption.
$0.28 / 1M
$0.42 / 1M
1M tokens
54%
Diferencias Clave de un Vistazo
- 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.
| Característica | 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.5 35B-A3B · Strengths & Key Advantages
- puede run smoothly en un único consumidor GPU (RTX 3090 / 4090 con INT4/INT8)
- Impressive multimodal response velocity exceeding 120 tokens/sec
- Solid OCR rendimiento en cotidiano documentos y web captures
Considerations & Trade-offs
- complejo matemático derivación en engineering schematics puede be inconsistent
- ventana de contexto recuperación degrades slightly past 180K tokens
DeepSeek Chat · Strengths & Key Advantages
- Groundbreaking 1M ventana de contexto un un astonishing $0.28 en / $0.42 out por millones tokens
- Multi-Head Latent Attention (MLA) arquitectura drastically reduces KV-cache memoria overhead
- de pesos abiertos availability para customized corporativo implementación
Considerations & Trade-offs
- texto-solo modelo sin nativo visual o audio comprensión
- carece de profundo multipaso razonamiento para competitivo mathematics
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (FAQ)
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.
Veredicto
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.
Preguntas frecuentes
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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs