Head-to-Head Model Comparison

Qwen3.5 122B-A10B vs Qwen3-Next 80B-A3B (Thinking)

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

Cambia y Compara Cualquier Modelo

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Qwen3.5 122B-A10BProfile

Qwen3-Next 80B-A3B (Thinking)Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3.5 122B-A10B

IA Multimodal Nativa

Qwen3.5 122B-A10B de Alibaba es un modelo de IA diseñado para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, con ventana de contexto de 262K, velocidad de 78 tokens/s, índice de inteligencia de 82/100 y tar

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen3-Next 80B-A3B (Thinking)

Modelo Base de Pesos Abiertos

Qwen3-Next 80B-A3B (Thinking) de Alibaba es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 131K, velocidad de 95 tokens/s, índice de inteligenc

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

46%

Diferencias Clave de un Vistazo

Both models cost the same under this workload — choose on capability, not price.
Qwen3.5 122B-A10B has a larger context window (262K vs 131K tokens).
Qwen3-Next 80B-A3B (Thinking) is faster at 95 tok/s vs 78 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	Qwen3.5 122B-A10B	Qwen3-Next 80B-A3B (Thinking)
Intelligence Index Composite reasoning & knowledge score (0-100)	82	82
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	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	16K	16K
Throughput Speed Tokens generated per second	78 tok/s	95 tok/s
SWE-bench Verified Real-world software engineering	N/A	46%
GPQA Diamond Graduate-level reasoning	65.5%	68.5%

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Tied

Identical monthly runtime cost

Velocidad

Qwen3-Next 80B-A3B (Thinking)

+22% faster generation

Contexto

Qwen3.5 122B-A10B

262.1K tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Qwen3.5 122B-A10BQwen3-Next 80B-A3B (Thinking)

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs46%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

65.5%vs68.5%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

80.5%vs80%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1350ptsvs1345pts

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs82%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

78 tok/s vs95 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Qwen3.5 122B-A10B0.51s

Qwen3-Next 80B-A3B (Thinking)0.54s

Est. End-to-End Response (500 tokens)TTFT + generation time

Qwen3.5 122B-A10B6.92s

Qwen3-Next 80B-A3B (Thinking)5.80s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Qwen3.5 122B-A10B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3-Next 80B-A3B (Thinking) Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Qwen3.5 122B-A10B · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 16K tokens de salida máxima (GPQA Diamond 65.5% · MMLU-Pro 80.5%)
Rendimiento ágil de 78 tokens/segundo con latencia inicial (TTFT) de solo 510 ms
Pesos abiertos (122B total (10B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Qwen3-Next 80B-A3B (Thinking) · Strengths & Key Advantages

Ventana de contexto de 131K con hasta 16K tokens de salida máxima (SWE-bench Verified 46% · GPQA Diamond 68.5%)
Rendimiento ágil de 95 tokens/segundo con latencia inicial (TTFT) de solo 540 ms
Pesos abiertos (80B total (3B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Qwen3.5 122B-A10B	Qwen3-Next 80B-A3B (Thinking)
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	18K	20K
Reasoning Tokens / Task Thinking tokens burned per task	5K	12K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Qwen3.5 122B-A10B	Qwen3-Next 80B-A3B (Thinking)
Official API Model ID (SDK)	qwen3-5-122b-a10b-instruct	qwen3-next-80b-a3b-thinking-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2025-09
Knowledge Cutoff	December 2025	August 2025
Architecture	Efficient Open Multimodal MoE Transformer	Deliberate Reasoning Sparse Mixture-of-Experts
Parameters	122B total (10B active MoE)	80B total (3B active MoE)
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)	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) · 76 GB VRAM (4-bit Quantized) · 262 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 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 122B-A10B vs Qwen3-Next 80B-A3B (Thinking): Respuestas Rápidas (FAQ)

Which model is faster?

Qwen3-Next 80B-A3B (Thinking) is faster, generating 95 tokens per second compared with 78 tokens per second.

Which model has lower latency?

Qwen3.5 122B-A10B has lower latency, with a time to first token of 0.51s compared with 0.54s.

Which model has a larger context window?

Qwen3.5 122B-A10B has a larger context window, supporting 262.1K tokens compared with 131.1K tokens.

Veredicto

Tanto Qwen3.5 122B-A10B (Ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas RAG empresariales y automatización mediante API.) como Qwen3-Next 80B-A3B (Thinking) (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen3.5 122B-A10B lidera en inteligencia general y programación, Qwen3-Next 80B-A3B (Thinking) ofrece mayor velocidad de generación de tokens y Qwen3.5 122B-A10B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Qwen3.5 122B-A10B o Qwen3-Next 80B-A3B (Thinking)?+

En las pruebas de inteligencia compuesta y resolución de código, Qwen3.5 122B-A10B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Qwen3.5 122B-A10B frente a Qwen3-Next 80B-A3B (Thinking)?+

Qwen3.5 122B-A10B cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Qwen3-Next 80B-A3B (Thinking) cuesta $0.00 / $0.00. En costo combinado, Qwen3.5 122B-A10B es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Qwen3.5 122B-A10B cuenta con 262.1K tokens de contexto frente a 131.1K tokens de Qwen3-Next 80B-A3B (Thinking). En velocidad de inferencia, Qwen3-Next 80B-A3B (Thinking) ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Qwen3.5 122B-A10B vs Qwen3-Next 80B-A3B (Thinking)

Cambia y Compara Cualquier Modelo

Qwen3.5 122B-A10B

Qwen3-Next 80B-A3B (Thinking)

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Qwen3.5 122B-A10B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3-Next 80B-A3B (Thinking) · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Qwen3.5 122B-A10B vs Qwen3-Next 80B-A3B (Thinking): Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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