Whisper Large v3 vs Gemini 3.5 Live Translate
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI 的 Whisper Large v3 与 Google 的 Gemini 3.5 Live Translate 是当前备受关注的两款主力 AI 模型。Whisper Large v3 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 Gemini 3.5 Live Translate 提供 131.1K Token 上下文窗口，定价为每百万 Token 输入 $5.00 / 输出 $20.00。以下为两者的基准测试、延迟与成本全面对比。
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Whisper Large v3
原生语音与音频大模型OpenAI 推出的 Whisper Large v3 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、55 Token/秒生成速度与 45/100 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%
Gemini 3.5 Live Translate
原生语音与音频大模型Google 推出的 Gemini 3.5 Live Translate 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 131K 上下文窗口、130 Token/秒生成速度与 84/100 综合智能指数（每百万 Token $5 输入 / $20 输出）。
$5.00 / 1M
$20.00 / 1M
131.1K tokens
N/A
核心差异一览
- Whisper Large v3 is 3000.0x cheaper for API usage (input + output tokens combined).
- Gemini 3.5 Live Translate has a larger context window (131K vs 0K tokens).
- Gemini 3.5 Live Translate is faster at 130 tok/s vs 55 tok/s.
| Feature | Whisper Large v3 | Gemini 3.5 Live Translate |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 45 | 84 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.01 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $3.70 / 1M |
Context Window Maximum input + output tokens | 448 tokens~1 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 0K | 16K |
Throughput Speed Tokens generated per second | 55 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 36% | N/A |
GPQA Diamond Graduate-level reasoning | 46% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
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.
Whisper Large v3 · 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 55 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 350 毫秒
- 开放模型权重 (1.55B)，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
Gemini 3.5 Live Translate · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 16K 输出容量，核心基准表现优异（Arena Elo 1420）
- 高达 130 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 300 毫秒
- 极具竞争力的 API 定价（每百万 Token $5 输入 / $20 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
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Whisper Large v3 | Gemini 3.5 Live Translate |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.85 |
Output Tokens / Task Tokens generated per task | 3K | 8K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Whisper Large v3 | Gemini 3.5 Live Translate |
|---|---|---|
| Official API Model ID (SDK) | whisper-large-v3 | models/gemini-3-5-live-translate |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2023-11 | 2026-06 |
| Knowledge Cutoff | 2023 (audio corpus) | February 2026 |
| Architecture | Encoder-decoder speech transformer | Low-latency neural speech-to-speech cross-lingual transformer |
| Parameters | 1.55B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.006/1M in) across full 0K window | Flat rate ($5/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $1/1M with configurable TTL |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | audio | audio |
| Output Modalities | text | audio, text |
Whisper Large v3 vs Gemini 3.5 Live Translate: 常见问题解答 (FAQ)
Which is more intelligent, Whisper Large v3 or Gemini 3.5 Live Translate?
Gemini 3.5 Live Translate is more intelligent. It scores 84 compared with 45 on the composite Intelligence Index.
Which model is faster?
Gemini 3.5 Live Translate is faster, generating 130 tokens per second compared with 55 tokens per second.
Which model is cheaper to run?
Whisper Large v3 is cheaper at $0.00 per 1M tokens blended, compared with $3.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.5 Live Translate has lower latency, with a time to first token of 0.30s compared with 0.35s.
Which model has a larger context window?
Gemini 3.5 Live Translate has a larger context window, supporting 131.1K tokens compared with 448 tokens.
综合结论
综合来看，Whisper Large v3（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 Gemini 3.5 Live Translate（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：Gemini 3.5 Live Translate 在综合智能与编程基准测试中表现更强，Gemini 3.5 Live Translate 拥有更快的 Token 生成速度，而 Whisper Large v3 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，Whisper Large v3 和 Gemini 3.5 Live Translate 哪个更强？+
在综合智能指数与编程基准评测中，Gemini 3.5 Live Translate 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。
Whisper Large v3 与 Gemini 3.5 Live Translate 的 API 价格哪个更便宜？+
Whisper Large v3 的 API 价格为输入 $0.01 / 输出 $0.00（每百万 Token），Gemini 3.5 Live Translate 为输入 $5.00 / 输出 $20.00。综合混合成本来看，Whisper Large v3 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？+
Whisper Large v3 支持 448 Token 上下文窗口，而 Gemini 3.5 Live Translate 支持 131.1K Token。在流式生成速度方面，Gemini 3.5 Live Translate 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 Google 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。