o4-mini vs Gemini 3.8 Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o4-mini by OpenAI and Gemini 3.8 Flash 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, Gemini 3.8 Flash holds a notable pricing advantage for token ingestion. On output generation, Gemini 3.8 Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3.8 Flash provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 3.8 Flash 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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o4-mini
高度な推論モデルOpenAI が提供する o4-mini は、最大 200K トークンのコンテキストウィンドウを備え、入力 $1.1/100万トークン・出力 $4.4/100万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。
$1.10 / 1M
$4.40 / 1M
200K tokens
57.5%
Gemini 3.8 Flash
高度な推論モデルGoogle が提供する Gemini 3.8 Flash は、最大 1.0M トークンのコンテキストウィンドウを備え、入力 $0.75/100万トークン・出力 $3.75/100万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。
$0.75 / 1M
$3.75 / 1M
1.05M tokens
74.8%
ひと目でわかる主な違い
- Gemini 3.8 Flash is 1.3x cheaper for API usage (input + output tokens combined).
- Gemini 3.8 Flash has a larger context window (1M vs 200K tokens).
- Gemini 3.8 Flash is faster at 175 tok/s vs 105 tok/s.
- Gemini 3.8 Flash leads on SWE-bench coding (74.8% vs 57.5%).
| Feature | o4-mini | Gemini 3.8 Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 50 | 87 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $1.10 / 1M | $0.75 / 1M |
Output Price Cost per million output tokens | $4.40 / 1M | $3.75 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.85 / 1M | $0.66 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 100K | 66K |
Throughput Speed Tokens generated per second | 105 tok/s | 175 tok/s |
SWE-bench Verified Real-world software engineering | 57.5% | 74.8% |
GPQA Diamond Graduate-level reasoning | 73% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.28 / 1M | $0.19 / 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.
o4-mini · Strengths & Key Advantages
- OpenAI の高度な Compact reasoning transformer アーキテクチャと 200K 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 $1.1/1M、出力 $4.4/1M）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 3.8 Flash · Strengths & Key Advantages
- Google の高度な Late-2026 optimized sparse multimodal transformer アーキテクチャと 1.0M 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 $0.75/1M、出力 $3.75/1M）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 | o4-mini | Gemini 3.8 Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.72 | $1.20 |
Output Tokens / Task Tokens generated per task | 45K | 44K |
Reasoning Tokens / Task Thinking tokens burned per task | 25K | 21K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | o4-mini | Gemini 3.8 Flash |
|---|---|---|
| Official API Model ID (SDK) | o4-mini | models/gemini-3-8-flash |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | June 2024 | June 2026 |
| Architecture | Compact reasoning transformer | Late-2026 optimized sparse multimodal 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.1/1M in) across full 200K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.275/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.1875/1M with configurable TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | 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) · Vision (~258–765 tokens per image tile) | 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 | text, vision | text, vision, audio, video |
| Output Modalities | text | text |
o4-mini vs Gemini 3.8 Flash: よくある質問 (FAQ)
Which is more intelligent, o4-mini or Gemini 3.8 Flash?
Gemini 3.8 Flash is more intelligent. It scores 87 compared with 50 on the composite Intelligence Index.
Which model is faster?
Gemini 3.8 Flash is faster, generating 175 tokens per second compared with 105 tokens per second.
Which model is cheaper to run?
Gemini 3.8 Flash is cheaper at $0.66 per 1M tokens blended, compared with $0.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.8 Flash has lower latency, with a time to first token of 0.39s compared with 1.80s.
Which model has a larger context window?
Gemini 3.8 Flash has a larger context window, supporting 1.05M tokens compared with 200K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose o4-mini if your priority is Budget reasoning inside agent pipelines and coding assistants. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.8 Flash if your workflow prioritizes Enterprise document synthesis, general coding, and high-quality voice/vision workflows. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does o4-mini compare to Gemini 3.8 Flash in API token pricing and cost per token?+
o4-mini costs $1.10 per 1M input tokens and $4.40 per 1M output tokens, whereas Gemini 3.8 Flash costs $0.75 input and $3.75 output. Overall, Gemini 3.8 Flash 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: o4-mini or Gemini 3.8 Flash?+
o4-mini supports a context window of 200K tokens (~150k words), while Gemini 3.8 Flash supports 1,049K tokens (~786k words). Therefore, Gemini 3.8 Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: o4-mini or Gemini 3.8 Flash?+
o4-mini scores 57.5% on SWE-bench Verified while Gemini 3.8 Flash scores 74.8%. For autonomous software engineering and multi-file debugging, Gemini 3.8 Flash holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o4-mini and Gemini 3.8 Flash?+
o4-mini features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 3.8 Flash provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o4-mini and Gemini 3.8 Flash?+
o4-mini generates approximately 105 tokens per second, while Gemini 3.8 Flash delivers 175 tokens per second. In latency-sensitive production environments, Gemini 3.8 Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o4-mini and Gemini 3.8 Flash to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.8 Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to o4-mini. 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.
Related reading
- Gemini 3.8 Flash vs Gemini 3.5 Flash: Benchmarks, Pricing & Speed Comparison比較
- Gemini 3.8 Flash vs GPT-4.1 mini: Benchmarks, Pricing & Speed Comparison比較
- Gemini 3.8 Flash vs Llama 5: Benchmarks, Pricing & Speed Comparison比較
- o4-mini vs Claude Haiku 5.5: Benchmarks, Pricing & Speed Comparison比較
- o4-mini vs DeepSeek V4.1-Flash: Benchmarks, Pricing & Speed Comparison比較
- o4-mini vs o1-mini: Benchmarks, Pricing & Speed Comparison比較
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: GetInfoUs Independent AI Benchmarks & Official API Specs