Gemini 3 Flash vs MiniMax-M2.5-highspeed
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 3 Flash by Google and MiniMax-M2.5-highspeed by MiniMax 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, MiniMax-M2.5-highspeed holds a notable pricing advantage for token ingestion. On output generation, MiniMax-M2.5-highspeed is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3 Flash provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 3 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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Gemini 3 Flash
高度な推論モデルGoogle が提供する Gemini 3 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 $0.5/100万トークン・出力 $3/100万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。
$0.50 / 1M
$3.00 / 1M
1.05M tokens
66.5%
MiniMax-M2.5-highspeed
高度な推論モデルMiniMax が提供する MiniMax-M2.5-highspeed は、最大 204K トークンのコンテキストウィンドウを備え、入力 $0.15/100万トークン・出力 $0.6/100万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。
$0.15 / 1M
$0.60 / 1M
204K tokens
N/A
ひと目でわかる主な違い
- MiniMax-M2.5-highspeed is 4.6x cheaper for API usage (input + output tokens combined).
- Gemini 3 Flash has a larger context window (1M vs 204K tokens).
- Gemini 3 Flash is faster at 180 tok/s vs 150 tok/s.
| Feature | Gemini 3 Flash | MiniMax-M2.5-highspeed |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 78 |
Provider | MiniMax | |
Input Price Cost per million input tokens | $0.50 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $3.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.49 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 204K tokens~306 A4 pages of text |
Max Output Tokens Per single request | 66K | 8K |
Throughput Speed Tokens generated per second | 180 tok/s | 150 tok/s |
SWE-bench Verified Real-world software engineering | 66.5% | N/A |
GPQA Diamond Graduate-level reasoning | 79% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.13 / 1M | $0.01 / 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.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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 3 Flash · Strengths & Key Advantages
- Google の高度な Next-gen sparse multimodal mixture-of-experts 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 $0.5/1M、出力 $3/1M）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
MiniMax-M2.5-highspeed · Strengths & Key Advantages
- MiniMax の高度な Latency-Optimized Hosted Cloud Transformer アーキテクチャと 204K 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 $0.15/1M、出力 $0.6/1M）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 | Gemini 3 Flash | MiniMax-M2.5-highspeed |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.95 | $0.04 |
Output Tokens / Task Tokens generated per task | 35K | 12K |
Reasoning Tokens / Task Thinking tokens burned per task | 16K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3 Flash | MiniMax-M2.5-highspeed |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-flash | minimax-m2-5-highspeed |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | MiniMax REST API (OpenAI-Compatible) |
| Release Date | 2025-12 | 2026-02 |
| Knowledge Cutoff | September 2025 | December 2025 |
| Architecture | Next-gen sparse multimodal mixture-of-experts | Latency-Optimized Hosted Cloud Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($0.15/1M in) across full 204K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.125/1M with configurable TTL | Cached input at $0.015/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) |
| 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 | MiniMax Production Safety & Alignment 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, audio, video | text |
| Output Modalities | text | text |
Gemini 3 Flash vs MiniMax-M2.5-highspeed: よくある質問 (FAQ)
Which is more intelligent, Gemini 3 Flash or MiniMax-M2.5-highspeed?
MiniMax-M2.5-highspeed is more intelligent. It scores 78 compared with 75 on the composite Intelligence Index.
Which model is faster?
Gemini 3 Flash is faster, generating 180 tokens per second compared with 150 tokens per second.
Which model is cheaper to run?
MiniMax-M2.5-highspeed is cheaper at $0.10 per 1M tokens blended, compared with $0.49 (7:2:1 cache:input:output ratio).
Which model has lower latency?
MiniMax-M2.5-highspeed has lower latency, with a time to first token of 0.27s compared with 0.40s.
Which model has a larger context window?
Gemini 3 Flash has a larger context window, supporting 1.05M tokens compared with 204K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Gemini 3 Flash if your priority is Production multimodal software agents and real-time interactive intelligence. Its performance in demanding tasks justifies its operational token cost. Choose MiniMax-M2.5-highspeed if your workflow prioritizes Customer support bots, real-time code completion, and interactive chat interfaces. 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 Gemini 3 Flash compare to MiniMax-M2.5-highspeed in API token pricing and cost per token?+
Gemini 3 Flash costs $0.50 per 1M input tokens and $3.00 per 1M output tokens, whereas MiniMax-M2.5-highspeed costs $0.15 input and $0.60 output. Overall, MiniMax-M2.5-highspeed 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 Flash or MiniMax-M2.5-highspeed?+
Gemini 3 Flash supports a context window of 1,049K tokens (~786k words), while MiniMax-M2.5-highspeed supports 204K tokens (~153k words). Therefore, Gemini 3 Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini 3 Flash or MiniMax-M2.5-highspeed?+
Gemini 3 Flash scores 66.5% on SWE-bench Verified, while MiniMax-M2.5-highspeed scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini 3 Flash provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemini 3 Flash and MiniMax-M2.5-highspeed?+
Gemini 3 Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, MiniMax-M2.5-highspeed provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini 3 Flash and MiniMax-M2.5-highspeed?+
Gemini 3 Flash generates approximately 180 tokens per second, while MiniMax-M2.5-highspeed delivers 150 tokens per second. In latency-sensitive production environments, Gemini 3 Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 3 Flash and MiniMax-M2.5-highspeed to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to MiniMax-M2.5-highspeed to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3 Flash. 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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs