Whisper Large v3 vs Gemini 3.5 Live Translate
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Whisper Large v3 by OpenAI and Gemini 3.5 Live Translate by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Whisper Large v3 holds a notable pricing advantage for token ingestion. On output generation, Whisper Large v3 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3.5 Live Translate provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Gemini 3.5 Live Translate leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Whisper Large v3
Açık Ağırlıklı ModelOpenAI tarafından geliştirilen Whisper Large v3, 448 token bağlam penceresi ve 1M girdi başına $0.006, 1M çıktı başına $0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.01 / 1M
$0.00 / 1M
448 tokens
36%
Gemini 3.5 Live Translate
Amiral Gemisi Öncü ModelGoogle tarafından geliştirilen Gemini 3.5 Live Translate, 131K token bağlam penceresi ve 1M girdi başına $5, 1M çıktı başına $20 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$5.00 / 1M
$20.00 / 1M
131.1K tokens
N/A
Bir Bakışta Temel Farklar
- 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 |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkEvaluations ongoing
Doğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Whisper Large v3 · Strengths & Key Advantages
- OpenAI imzalı gelişmiş Encoder-decoder speech transformer mimarisi ve 448 token bağlam kapasitesi
- Dengeli API maliyeti ($0.006/1M girdi, $0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Gemini 3.5 Live Translate · Strengths & Key Advantages
- Google imzalı gelişmiş Low-latency neural speech-to-speech cross-lingual transformer mimarisi ve 131K token bağlam kapasitesi
- Dengeli API maliyeti ($5/1M girdi, $20/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
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 |
Teknik Özellikler
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: Hızlı Yanıtlar (SSS)
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.
Sonuç
Verdict & Model Selection Recommendation: Choose Whisper Large v3 if your priority is Batch transcription, subtitling and multilingual captioning. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.5 Live Translate if your workflow prioritizes Live international conferences, telemedicine interpretation, and cross-border travel headsets. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Sıkça Sorulan Sorular (SSS)
How does Whisper Large v3 compare to Gemini 3.5 Live Translate in API token pricing and cost per token?+
Whisper Large v3 costs $0.01 per 1M input tokens and $0.00 per 1M output tokens, whereas Gemini 3.5 Live Translate costs $5.00 input and $20.00 output. Overall, Whisper Large v3 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Whisper Large v3 or Gemini 3.5 Live Translate?+
Whisper Large v3 supports a context window of 448 tokens (~336 words), while Gemini 3.5 Live Translate supports 131K tokens (~98k words). Therefore, Gemini 3.5 Live Translate can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Whisper Large v3 or Gemini 3.5 Live Translate?+
Whisper Large v3 scores 36% on SWE-bench Verified, while Gemini 3.5 Live Translate scores competitively for software generation. For complex autonomous debugging and agentic engineering, Whisper Large v3 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Whisper Large v3 and Gemini 3.5 Live Translate?+
Whisper Large v3 is optimized for low-latency direct generation. In contrast, Gemini 3.5 Live Translate focuses on standard fast inference.
How does inference speed (tokens per second) compare between Whisper Large v3 and Gemini 3.5 Live Translate?+
Whisper Large v3 generates approximately 55 tokens per second, while Gemini 3.5 Live Translate delivers 130 tokens per second. In latency-sensitive production environments, Gemini 3.5 Live Translate provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Whisper Large v3 and Gemini 3.5 Live Translate to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Whisper Large v3 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3.5 Live Translate. This tiered routing pattern cuts overall API bills by up to 70%.
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.
Resmi kaynaklar: GetInfoUs Independent AI Benchmarks & Official API Specs