GPT Realtime Whisper vs Whisper Large v3 Turbo
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI 的 GPT Realtime Whisper 与 OpenAI 的 Whisper Large v3 Turbo 是当前备受关注的两款主力 AI 模型。GPT Realtime Whisper 提供 448 Token 上下文窗口，API 定价为每百万 Token 输入 $0.01 / 输出 $0.00；而 Whisper Large v3 Turbo 提供 448 Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。
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GPT Realtime Whisper
原生语音与音频大模型OpenAI 推出的 GPT Realtime Whisper 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 0K 上下文窗口、140 Token/秒生成速度与 44/100 综合智能指数（每百万 Token $0.006 输入 / $0 输出）。
$0.01 / 1M
$0.00 / 1M
448 tokens
36%
Whisper Large v3 Turbo
原生语音与音频大模型OpenAI 推出的 Whisper Large v3 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 0K 上下文窗口、180 Token/秒生成速度与 42/100 综合智能指数（每百万 Token $0.0006 输入 / $0 输出）。
$0.00 / 1M
$0.00 / 1M
448 tokens
36%
核心差异一览
- Whisper Large v3 Turbo is 10.0x cheaper for API usage (input + output tokens combined).
- Whisper Large v3 Turbo is faster at 180 tok/s vs 140 tok/s.
- Whisper Large v3 Turbo leads on SWE-bench coding (36.0% vs 36.0%).
| Feature | GPT Realtime Whisper | Whisper Large v3 Turbo |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 42 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.01 / 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 | 448 tokens~1 A4 pages of text | 448 tokens~1 A4 pages of text |
Max Output Tokens Per single request | 0K | 0K |
Throughput Speed Tokens generated per second | 140 tok/s | 180 tok/s |
SWE-bench Verified Real-world software engineering | 36% | 36% |
GPQA Diamond Graduate-level reasoning | 46% | 46% |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
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.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT Realtime Whisper · Strengths & Key Advantages
- 提供 0K 上下文窗口与最高 0K 输出容量，核心基准表现优异（SWE-bench Verified 36% · GPQA Diamond 46%）
- 高达 140 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 150 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.006 输入 / $0 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
Whisper Large v3 Turbo · Strengths & Key Advantages
- 提供 0K 上下文窗口与最高 0K 输出容量，核心基准表现优异（SWE-bench Verified 36% · GPQA Diamond 46%）
- 高达 180 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 120 毫秒
- 开放模型权重 (809M)，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.
| Metric | GPT Realtime Whisper | Whisper Large v3 Turbo |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $0.03 |
Output Tokens / Task Tokens generated per task | 3K | 3K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT Realtime Whisper | Whisper Large v3 Turbo |
|---|---|---|
| Official API Model ID (SDK) | gpt-realtime-whisper | whisper-large-v3-turbo |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-05 | 2024-10 |
| Knowledge Cutoff | February 2026 | 2024 (audio corpus) |
| Architecture | Streaming speech-to-text transformer | Distilled encoder-decoder speech transformer |
| Parameters | Confidential | 809M |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.006/1M in) across full 0K window | Flat rate ($0.0006/1M in) across full 0K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0/1M (no extra cache-write fee) | Automatic prefix caching at $0/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Audio (~25–32 tokens/sec) | Text BPE (~4 chars/token) · Audio (~25–32 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | audio | audio |
| Output Modalities | text | text |
GPT Realtime Whisper vs Whisper Large v3 Turbo: 常见问题解答 (FAQ)
Which is more intelligent, GPT Realtime Whisper or Whisper Large v3 Turbo?
GPT Realtime Whisper is more intelligent. It scores 44 compared with 42 on the composite Intelligence Index.
Which model is faster?
Whisper Large v3 Turbo is faster, generating 180 tokens per second compared with 140 tokens per second.
Which model has lower latency?
Whisper Large v3 Turbo has lower latency, with a time to first token of 0.12s compared with 0.15s.
综合结论
综合来看，GPT Realtime Whisper（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 Whisper Large v3 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。）各具优势：GPT Realtime Whisper 在综合智能与编程基准测试中表现更强，Whisper Large v3 Turbo 拥有更快的 Token 生成速度，而 Whisper Large v3 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，GPT Realtime Whisper 和 Whisper Large v3 Turbo 哪个更强？+
在综合智能指数与编程基准评测中，GPT Realtime Whisper 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。
GPT Realtime Whisper 与 Whisper Large v3 Turbo 的 API 价格哪个更便宜？+
GPT Realtime Whisper 的 API 价格为输入 $0.01 / 输出 $0.00（每百万 Token），Whisper Large v3 Turbo 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Whisper Large v3 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？+
GPT Realtime Whisper 支持 448 Token 上下文窗口，而 Whisper Large v3 Turbo 支持 448 Token。在流式生成速度方面，Whisper Large v3 Turbo 的吞吐表现更快。
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.
数据来源: OpenAI 与 OpenAI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。