Gemini 2.5 Flash TTS vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 2.5 Flash TTS by Google against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Gemini 2.5 Flash TTS
高精度AI画像生成モデルGoogle の Gemini 2.5 Flash TTS は、高解像度なAI画像生成、ビジュアルデザイン、正確な文字レンダリング向けに設計されたAIモデルです。32K のコンテキストウィンドウ、毎秒 100 トークンの生成速度、知能指数 64/100（1Mトークンあたり入力$0.5・出力$10）を実現しています。
$0.50 / 1M
$10.00 / 1M
32K tokens
N/A
Gemini 4 Argon
ネイティブ・マルチモーダルAIGoogle の Gemini 4 Argon は、テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析向けに設計されたAIモデルです。1M のコンテキストウィンドウ、毎秒 115 トークンの生成速度、知能指数 97/100（1Mトークンあたり入力$2・出力$10）を実現しています。
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
ひと目でわかる主な違い
- Gemini 2.5 Flash TTS is 1.2x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 32K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 100 tok/s.
| Feature | Gemini 2.5 Flash TTS | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 64 | 97 |
Provider | ||
Input Price Cost per million input tokens | $0.50 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.10 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32K tokens~48 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 1000K |
Throughput Speed Tokens generated per second | 100 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 81.9% |
GPQA Diamond Graduate-level reasoning | N/A | 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 frameworkEvaluations ongoing
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.
Gemini 2.5 Flash TTS · Strengths & Key Advantages
- 32K のコンテキストウィンドウと最大 8K トークンの出力容量（Arena Elo 1300）
- 毎秒 100 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 400ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$0.5・出力$10）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Gemini 4 Argon · Strengths & Key Advantages
- 1M のコンテキストウィンドウと最大 1M トークンの出力容量（SWE-bench Verified 81.9% · GPQA Diamond 81.5%）
- 毎秒 115 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 850ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$2・出力$10）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 大量のトークンを処理する本番環境では、プロンプトキャッシュや Batch API の併用によるコスト最適化が推奨されます
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 2.5 Flash TTS | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.60 | $2.45 |
Output Tokens / Task Tokens generated per task | 5K | 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 | Gemini 2.5 Flash TTS | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-2-5-flash-tts | models/gemini-4-argon |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-09 | 2026-09 |
| Knowledge Cutoff | July 2025 | June 2026 |
| Architecture | Neural text-to-speech acoustic diffusion model | 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 ($0.5/1M in) across full 32K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | 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 | Google DeepMind Frontier Safety Framework Verified | 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 | text | text, vision, audio, video |
| Output Modalities | audio | text |
Gemini 2.5 Flash TTS vs Gemini 4 Argon: よくある質問 (FAQ)
Which is more intelligent, Gemini 2.5 Flash TTS or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 64 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Gemini 2.5 Flash TTS is cheaper at $1.10 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 2.5 Flash TTS has lower latency, with a time to first token of 0.40s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 32K tokens.
総合評価
Choose Gemini 2.5 Flash TTS for 高解像度なai画像生成、ビジュアルデザイン、正確な文字レンダリング、エンタープライズrag基盤、スケーラブルな本番api統合に最適。, or choose Gemini 4 Argon for テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析、エンタープライズrag基盤、スケーラブルな本番api統合に最適。.
よくある質問 (FAQ)
Which is cheaper: Gemini 2.5 Flash TTS or Gemini 4 Argon?+
Gemini 2.5 Flash TTS costs $0.5/1M input and $10/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemini 2.5 Flash TTS or Gemini 4 Argon?+
Gemini 2.5 Flash TTS supports 32K 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: Google & Google Official Documentation