Qwen3.5 35B-A3B vs Qwen3.5 27B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Qwen3.5 35B-A3B by Alibaba and Qwen3.5 27B 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 262K 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.
Cambia y Compara Cualquier Modelo
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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
Qwen3.5 27B
27B abiertodenso 27B multimodal modelo proporcionando consistent, predictable latencia y robust visión-idioma comprensión para dedicado servers.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Diferencias Clave de un Vistazo
- Both models cost the same under this workload — choose on capability, not price.
- Qwen3.5 35B-A3B is faster at 125 tok/s vs 90 tok/s.
| Característica | Qwen3.5 35B-A3B | Qwen3.5 27B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 77 | 75 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 125 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 59% | 56.5% |
¿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.
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.
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
Qwen3.5 27B · Strengths & Key Advantages
- denso arquitectura ofrece simple servicio sin especializado MoE enrutamiento software
- Stable latencia bajo heavy multi-tenant concurrency
- Clean visión-idioma alignment para visual búsqueda y clasificación
Considerations & Trade-offs
- requiere más memoria por activo token en comparación con sparse 35B-A3B MoE
- menor pico razonamiento precisión que larger sibling modelos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.5 35B-A3B | Qwen3.5 27B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 14K | 13K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Qwen3.5 35B-A3B | Qwen3.5 27B |
|---|---|---|
| Official API Model ID (SDK) | qwen3-5-35b-a3b-instruct | qwen3-5-27b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-02 | 2026-02 |
| Knowledge Cutoff | December 2025 | December 2025 |
| Architecture | Lightweight Multimodal MoE Transformer | Dense Multimodal Autoregressive Transformer |
| Parameters | 35B total (3B active MoE) | 27B |
| 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) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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) · Vision (~258–765 tokens per image tile) |
| 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) · 17 GB VRAM (4-bit Quantized) · 58 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, vision |
| Output Modalities | text | text |
Qwen3.5 35B-A3B vs Qwen3.5 27B: Respuestas Rápidas (FAQ)
Which is more intelligent, Qwen3.5 35B-A3B or Qwen3.5 27B?
Qwen3.5 35B-A3B is more intelligent. It scores 77 compared with 75 on the composite Intelligence Index.
Which model is faster?
Qwen3.5 35B-A3B is faster, generating 125 tokens per second compared with 90 tokens per second.
Which model has lower latency?
Qwen3.5 35B-A3B has lower latency, with a time to first token of 0.34s compared with 0.41s.
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 Qwen3.5 27B if your workflow prioritizes Dedicated self-hosted API endpoints and vision classification pipelines. 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 Qwen3.5 27B in API token pricing and cost per token?+
Both Qwen3.5 35B-A3B and Qwen3.5 27B are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Qwen3.5 35B-A3B or Qwen3.5 27B?+
Both Qwen3.5 35B-A3B and Qwen3.5 27B offer an identical context window of 262K tokens (~197k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Qwen3.5 35B-A3B or Qwen3.5 27B?+
Qwen3.5 35B-A3B scores at the frontier of coding benchmarks, while Qwen3.5 27B scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen3.5 35B-A3B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen3.5 35B-A3B and Qwen3.5 27B?+
Qwen3.5 35B-A3B is optimized for low-latency direct generation. In contrast, Qwen3.5 27B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen3.5 35B-A3B and Qwen3.5 27B?+
Qwen3.5 35B-A3B generates approximately 125 tokens per second, while Qwen3.5 27B delivers 90 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 Qwen3.5 27B to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Qwen3.5 35B-A3B, and route deep architectural or complex reasoning queries to Qwen3.5 35B-A3B. This balanced routing strategy maximizes system throughput and answer quality.
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