Qwen3.5 35B-A3B vs DeepSeek Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.5 35B-A3B di Alibaba e DeepSeek Chat di DeepSeek sono due modelli di IA di riferimento. Qwen3.5 35B-A3B offre una finestra di contesto di 262.1K token a $0.00/$0.00 per milione di token, mentre DeepSeek Chat mette a disposizione 1M token di contesto a $0.28/$0.42 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
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Qwen3.5 35B-A3B
IA Multimodale NativaQwen3.5 35B-A3B di Alibaba è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 262K, velocità di 125 token/s, indice di intelligenza di 77/10
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
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%
Differenze Chiave a Colpo d’Occhio
- 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 |
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.
Qwen3.5 35B-A3B · Strengths & Key Advantages
- Finestra di contesto da 262K con capacità massima di output fino a 8K token (GPQA Diamond 59% · MMLU-Pro 76.5%)
- Throughput elevato di 125 token/secondo con latenza al primo token (TTFT) di soli 340 ms
- Pesi aperti (35B total (3B MoE)) 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
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
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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
Sia Qwen3.5 35B-A3B (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) 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.) eccellono in produzione: Qwen3.5 35B-A3B guida nei benchmark di ragionamento e programmazione, Qwen3.5 35B-A3B garantisce una generazione più rapida e Qwen3.5 35B-A3B offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Qwen3.5 35B-A3B o DeepSeek Chat?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Qwen3.5 35B-A3B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Qwen3.5 35B-A3B e DeepSeek Chat?+
Qwen3.5 35B-A3B costa $0.00 / $0.00 per milione di token di input/output, mentre DeepSeek Chat costa $0.28 / $0.42. Nel costo combinato, Qwen3.5 35B-A3B risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Qwen3.5 35B-A3B supporta 262.1K token di contesto contro 1M token di DeepSeek Chat, mentre Qwen3.5 35B-A3B 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.
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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 Alibaba e DeepSeek, schede di sistema e benchmark indipendenti (2026).