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Head-to-Head Model Comparison

Qwen-Omni Turbo vs Nemotron VoiceChat

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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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

Alibaba 的 Qwen-Omni Turbo 与 NVIDIA 的 Nemotron VoiceChat 是当前备受关注的两款主力 AI 模型。Qwen-Omni Turbo 提供 32.8K Token 上下文窗口，API 定价为每百万 Token 输入 $0.30 / 输出 $1.20；而 Nemotron VoiceChat 提供 128K Token 上下文窗口，定价为每百万 Token 输入 $0.50 / 输出 $1.50。以下为两者的基准测试、延迟与成本全面对比。

模型直接对比 (VS)

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Popular Matchups:
Alibaba

Qwen-Omni Turbo

原生语音与音频大模型

Alibaba 推出的 Qwen-Omni Turbo 是一款专注于超自然语音合成、实时音频理解与低延迟语音交互的大模型，具备 33K 上下文窗口、90 Token/秒生成速度与 74/100 综合智能指数（每百万 Token $0.3 输入 / $1.2 输出）。

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

原生语音与音频大模型

NVIDIA 推出的 Nemotron VoiceChat 是一款专注于超自然语音合成、实时音频理解与低延迟语音交互的大模型，具备 128K 上下文窗口、90 Token/秒生成速度与 49/100 综合智能指数（每百万 Token $0.5 输入 / $1.5 输出）。

Intelligence Index49 / 100
Input Token:

$0.50 / 1M

Output Token:

$1.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

36%

核心差异一览

  • 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

各项指标谁更胜一筹？

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

权威基准测试得分对比

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

延迟与响应速度

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

实时 Token 成本模拟器

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

  • 提供 33K 上下文窗口与最高 8K 输出容量，核心基准表现优异（MMLU-Pro 69% · Arena Elo 1235）
  • 高达 90 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 380 毫秒
  • 极具竞争力的 API 定价（每百万 Token $0.3 输入 / $1.2 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Nemotron VoiceChat · Strengths & Key Advantages

  • 提供 128K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 36% · GPQA Diamond 52%）
  • 高达 90 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 220 毫秒
  • 开放模型权重 (Confidential (~14B))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

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

技术规格参数

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: 常见问题解答 (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.

综合结论

综合来看，Qwen-Omni Turbo（适合：专注于超自然语音合成、实时音频理解与低延迟语音交互的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Nemotron VoiceChat（适合：专注于超自然语音合成、实时音频理解与低延迟语音交互的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Qwen-Omni Turbo 在综合智能与编程基准测试中表现更强，Qwen-Omni Turbo 拥有更快的 Token 生成速度，而 Qwen-Omni Turbo 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面，Qwen-Omni Turbo 和 Nemotron VoiceChat 哪个更强？+

在综合智能指数与编程基准评测中，Qwen-Omni Turbo 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Qwen-Omni Turbo 与 Nemotron VoiceChat 的 API 价格哪个更便宜？+

Qwen-Omni Turbo 的 API 价格为输入 $0.30 / 输出 $1.20（每百万 Token），Nemotron VoiceChat 为输入 $0.50 / 输出 $1.50。综合混合成本来看，Qwen-Omni Turbo 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度？+

Qwen-Omni Turbo 支持 32.8K Token 上下文窗口，而 Nemotron VoiceChat 支持 128K Token。在流式生成速度方面，Qwen-Omni Turbo 的吞吐表现更快。

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.

数据来源: Alibaba 与 NVIDIA 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。