Gemini 3.5 Live Translate vs GPT Realtime Whisper
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 3.5 Live Translate by Google and GPT Realtime Whisper by OpenAI 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, GPT Realtime Whisper holds a notable pricing advantage for token ingestion. On output generation, GPT Realtime Whisper 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, GPT Realtime Whisper 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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Gemini 3.5 Live Translate
Flaggschiff-Frontier-ModellGemini 3.5 Live Translate von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 131K Token zu API-Preisen von $5/1M Input- und $20/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$5.00 / 1M
$20.00 / 1M
131.1K tokens
N/A
GPT Realtime Whisper
Hochleistungs-KI-ModellGPT Realtime Whisper von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 448 Token zu API-Preisen von $0.006/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.01 / 1M
$0.00 / 1M
448 tokens
36%
Wichtigste Unterschiede auf einen Blick
- GPT Realtime Whisper 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).
- GPT Realtime Whisper is faster at 140 tok/s vs 130 tok/s.
| Feature | Gemini 3.5 Live Translate | GPT Realtime Whisper |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 84 | 44 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $5.00 / 1M | $0.01 / 1M |
Output Price Cost per million output tokens | $20.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $3.70 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 448 tokens~1 A4 pages of text |
Max Output Tokens Per single request | 16K | 0K |
Throughput Speed Tokens generated per second | 130 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 36% |
GPQA Diamond Graduate-level reasoning | N/A | 46% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 1M | $0.00 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 3.5 Live Translate · Strengths & Key Advantages
- Fortschrittliche Low-latency neural speech-to-speech cross-lingual transformer-Architektur von Google mit 131K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($5/1M Input, $20/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
GPT Realtime Whisper · Strengths & Key Advantages
- Fortschrittliche Streaming speech-to-text transformer-Architektur von OpenAI mit 448 Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.006/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3.5 Live Translate | GPT Realtime Whisper |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.85 | $0.04 |
Output Tokens / Task Tokens generated per task | 8K | 3K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.5 Live Translate | GPT Realtime Whisper |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-5-live-translate | gpt-realtime-whisper |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-06 | 2026-05 |
| Knowledge Cutoff | February 2026 | February 2026 |
| Architecture | Low-latency neural speech-to-speech cross-lingual transformer | Streaming speech-to-text transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($5/1M in) across full 131K window | Flat rate ($0.006/1M in) across full 0K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $1/1M with configurable TTL | Automatic prefix caching at $0/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | 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 | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | audio | audio |
| Output Modalities | audio, text | text |
Gemini 3.5 Live Translate vs GPT Realtime Whisper: Schnelle Antworten (FAQ)
Which is more intelligent, Gemini 3.5 Live Translate or GPT Realtime Whisper?
Gemini 3.5 Live Translate is more intelligent. It scores 84 compared with 44 on the composite Intelligence Index.
Which model is faster?
GPT Realtime Whisper is faster, generating 140 tokens per second compared with 130 tokens per second.
Which model is cheaper to run?
GPT Realtime Whisper 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?
GPT Realtime Whisper has lower latency, with a time to first token of 0.15s compared with 0.30s.
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.
Fazit
Verdict & Model Selection Recommendation: Choose Gemini 3.5 Live Translate if your priority is Live international conferences, telemedicine interpretation, and cross-border travel headsets. Its performance in demanding tasks justifies its operational token cost. Choose GPT Realtime Whisper if your workflow prioritizes Live captions and real-time voice-agent transcription. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does Gemini 3.5 Live Translate compare to GPT Realtime Whisper in API token pricing and cost per token?+
Gemini 3.5 Live Translate costs $5.00 per 1M input tokens and $20.00 per 1M output tokens, whereas GPT Realtime Whisper costs $0.01 input and $0.00 output. Overall, GPT Realtime Whisper 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: Gemini 3.5 Live Translate or GPT Realtime Whisper?+
Gemini 3.5 Live Translate supports a context window of 131K tokens (~98k words), while GPT Realtime Whisper supports 448 tokens (~336 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: Gemini 3.5 Live Translate or GPT Realtime Whisper?+
Gemini 3.5 Live Translate scores at the frontier of coding benchmarks, while GPT Realtime Whisper scores 36%. For complex autonomous debugging and agentic engineering, GPT Realtime Whisper provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemini 3.5 Live Translate and GPT Realtime Whisper?+
Gemini 3.5 Live Translate is optimized for low-latency direct generation. In contrast, GPT Realtime Whisper focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemini 3.5 Live Translate and GPT Realtime Whisper?+
Gemini 3.5 Live Translate generates approximately 130 tokens per second, while GPT Realtime Whisper delivers 140 tokens per second. In latency-sensitive production environments, GPT Realtime Whisper provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 3.5 Live Translate and GPT Realtime Whisper to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT Realtime Whisper 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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs