GetInfoUs
Head-to-Head Model Comparison

Qwen-Omni Turbo vs Nemotron VoiceChat

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial
Partilhar
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Qwen-Omni Turbo vs Nemotron VoiceChat: Benchmarks, Pricing & Speed Comparison

Qwen-Omni Turbo da Alibaba e Nemotron VoiceChat da NVIDIA são dois modelos de IA de destaque. O Qwen-Omni Turbo oferece uma janela de contexto de 32.8K tokens a $0.30/$1.20 por milhão de tokens, enquanto o Nemotron VoiceChat oferece 128K tokens de contexto a $0.50/$1.50 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.

Comparação Direta (VS)

Troque e Compare Qualquer Modelo

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

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
Alibaba

Qwen-Omni Turbo

Modelo Nativo de Voz e Áudio

O Qwen-Omni Turbo da Alibaba é um modelo de IA projetado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, oferecendo janela de contexto de 33K, velocidade de 90 tokens/s, índice de inteligência de 74/100 e

Intelligence Index74 / 100
Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

NVIDIA

Nemotron VoiceChat

Modelo Nativo de Voz e Áudio

O Nemotron VoiceChat da NVIDIA é um modelo de IA projetado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, oferecendo janela de contexto de 128K, velocidade de 90 tokens/s, índice de inteligência de 49/10

Intelligence Index49 / 100
Input Token:

$0.50 / 1M

Output Token:

$1.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

36%

Principais Diferenças em Resumo

  • Qwen-Omni Turbo is 1.4x cheaper for API usage (input + output tokens combined).
  • Nemotron VoiceChat has a larger context window (128K vs 33K tokens).
  • Nemotron VoiceChat is faster at 90 tok/s vs 90 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen-Omni TurboNemotron VoiceChat
Intelligence Index
Composite reasoning & knowledge score (0-100)
7449
Provider
AlibabaNVIDIA
Input Price
Cost per million input tokens
$0.30 / 1M$0.50 / 1M
Output Price
Cost per million output tokens
$1.20 / 1M$1.50 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.20 / 1M$0.33 / 1M
Context Window
Maximum input + output tokens
32.8K tokens~49 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
90 tok/s90 tok/s
SWE-bench Verified
Real-world software engineering
N/A36%
GPQA Diamond
Graduate-level reasoning
N/A52%
Cached Input Price
Prompt caching discount per 1M tokens
$0.03 / 1M$0.12 / 1M

Quem vence em cada categoria?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Qwen-Omni Turbo
1.4x cheaper monthly runtime
Speed
Nemotron VoiceChat
+0% faster generation
Context
Nemotron VoiceChat
128K tokens window

Pontuações de Benchmarks Verificadas

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

Qwen-Omni TurboNemotron VoiceChat
SWE-bench Verified (Autonomous Coding)

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

N/Avs36%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs52%
MMLU-Pro (Multi-task Language Understanding)

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

69%vs74%
LMSYS Chatbot Arena Elo

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

1235ptsvs1320pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs90 tok/s

Latência e Tempo de Resposta

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
Qwen-Omni Turbo0.38s
Nemotron VoiceChat0.22s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen-Omni Turbo5.94s
Nemotron VoiceChat5.78s

Simulador Interativo de Custo de Tokens

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Qwen-Omni Turbo Monthly API Bill
$1.57 / month
$0.3/1M in · $1.2/1M out
Nemotron VoiceChat Monthly API Bill
$2.28 / month
$0.5/1M in · $1.5/1M out
Qwen-Omni Turbo saves approximately $0.71 / month (31%) under this workload.1.4x Savings

Qwen-Omni Turbo · Strengths & Key Advantages

  • Janela de contexto de 33K com limite máximo de saída de 8K tokens (MMLU-Pro 69% · Arena Elo 1235)
  • Taxa de transferência rápida de 90 tokens/segundo com latência inicial (TTFT) de 380 ms
  • Economia de API competitiva ($0.3/$1.2 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
  • Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
  • Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Nemotron VoiceChat · Strengths & Key Advantages

  • Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 36% · GPQA Diamond 52%)
  • Taxa de transferência rápida de 90 tokens/segundo com latência inicial (TTFT) de 220 ms
  • Pesos abertos (Confidential (~14B)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
  • Requer servidores GPU com memória VRAM adequada para execução local em precisão total
  • Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Token Use & Task Economics

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

MetricQwen-Omni TurboNemotron VoiceChat
Cost per Task
Estimated USD to finish one task
$0.06$0.25
Output Tokens / Task
Tokens generated per task
12K20K
Reasoning Tokens / Task
Thinking tokens burned per task
2K0K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

FeatureQwen-Omni TurboNemotron VoiceChat
Official API Model ID (SDK)qwen-omni-turbo-instructnemotron-voicechat
API ProtocolAlibaba REST API (OpenAI-Compatible)OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-012026-03
Knowledge CutoffNovember 2024January 2026
ArchitectureEnd-to-End Omni-Modal Multimodal TransformerFull-Duplex Speech-to-Speech Transformer
ParametersConfidentialConfidential (~14B)
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.3/1M in) across full 33K windowFlat rate ($0.5/1M in) across full 128K window
Prompt Cache Policy & TTLCached input at $0.03/1M tokensCached input at $0.12/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 9 GB VRAM (4-bit Quantized) · 30 GB VRAM (Native BF16)
Safety & Frontier ClassificationAlibaba Production Safety & Alignment VerifiedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, vision, audiotext, vision, audio
Output Modalitiestext, audiotext, audio

Qwen-Omni Turbo vs Nemotron VoiceChat: Respostas Rápidas (FAQ)

Which is more intelligent, Qwen-Omni Turbo or Nemotron VoiceChat?

Qwen-Omni Turbo is more intelligent. It scores 74 compared with 49 on the composite Intelligence Index.

Which model is cheaper to run?

Qwen-Omni Turbo is cheaper at $0.20 per 1M tokens blended, compared with $0.33 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron VoiceChat has lower latency, with a time to first token of 0.22s compared with 0.38s.

Which model has a larger context window?

Nemotron VoiceChat has a larger context window, supporting 128K tokens compared with 32.8K tokens.

Veredito

Tanto Qwen-Omni Turbo (Indicado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, arquiteturas RAG corporativas e integração escalável via API.) quanto Nemotron VoiceChat (Indicado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Qwen-Omni Turbo lidera em inteligência geral e programação, Qwen-Omni Turbo oferece geração mais rápida e Qwen-Omni Turbo apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Qwen-Omni Turbo ou Nemotron VoiceChat?+

Nas avaliações de inteligência e código, Qwen-Omni Turbo leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Qwen-Omni Turbo ou Nemotron VoiceChat?+

O Qwen-Omni Turbo custa $0.30 / $1.20 por 1M de tokens (entrada/saída) e o Nemotron VoiceChat custa $0.50 / $1.50. No custo combinado, Qwen-Omni Turbo é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Qwen-Omni Turbo suporta 32.8K tokens de contexto contra 128K tokens do Nemotron VoiceChat, enquanto Qwen-Omni Turbo entrega maior velocidade de geração.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Fontes oficiais: Documentação oficial de API da Alibaba e NVIDIA e benchmarks independentes (2026).