GPT Realtime Whisper vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT Realtime Whisper by OpenAI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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GPT Realtime Whisper
高性能 AI モデルOpenAI が提供する GPT Realtime Whisper は、最大 448 トークンのコンテキストウィンドウを備え、入力 $0.006/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.01 / 1M
$0.00 / 1M
448 tokens
36%
Gemini 4 Argon
高度な推論モデルGoogle が提供する Gemini 4 Argon は、最大 1M トークンのコンテキストウィンドウを備え、入力 $2/100万トークン・出力 $10/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
ひと目でわかる主な違い
- GPT Realtime Whisper is 1350.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 0K tokens).
- GPT Realtime Whisper is faster at 140 tok/s vs 115 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 36.0%).
| Feature | GPT Realtime Whisper | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 97 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.01 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 448 tokens~1 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 0K | 1000K |
Throughput Speed Tokens generated per second | 140 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 36% | 81.9% |
GPQA Diamond Graduate-level reasoning | 46% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT Realtime Whisper · Strengths & Key Advantages
- OpenAI の高度な Streaming speech-to-text transformer アーキテクチャと 448 トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.006/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Gemini 4 Argon · Strengths & Key Advantages
- Google の高度な Pathways Multimodal Sparse Mixture-of-Experts アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $2/1M、出力 $10/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT Realtime Whisper | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $2.45 |
Output Tokens / Task Tokens generated per task | 3K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT Realtime Whisper | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | gpt-realtime-whisper | models/gemini-4-argon |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-05 | 2026-09 |
| Knowledge Cutoff | February 2026 | June 2026 |
| Architecture | Streaming speech-to-text transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.006/1M in) across full 0K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 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 | OpenAI Preparedness Framework Audited & System Card Published | Google DeepMind Frontier Safety Framework Verified |
| 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 | text, vision, audio, video |
| Output Modalities | text | text |
GPT Realtime Whisper vs Gemini 4 Argon: よくある質問 (FAQ)
Which is more intelligent, GPT Realtime Whisper or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 44 on the composite Intelligence Index.
Which model is faster?
GPT Realtime Whisper is faster, generating 140 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
GPT Realtime Whisper is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (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 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 448 tokens.
総合評価
Choose GPT Realtime Whisper for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、openai エコシステムの統合, or choose Gemini 4 Argon for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、google エコシステムの統合.
よくある質問 (FAQ)
Which is cheaper: GPT Realtime Whisper or Gemini 4 Argon?+
GPT Realtime Whisper costs $0.006/1M input and $0/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: GPT Realtime Whisper or Gemini 4 Argon?+
GPT Realtime Whisper supports 0K tokens whereas Gemini 4 Argon supports 1M tokens.
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.
公的ソース: OpenAI & Google Official Documentation