MiniMax-M2.7-highspeed vs MiniMax-M2.5-highspeed
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
MiniMax-M2.7-highspeed by MiniMax 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, both models provide an identical memory horizon with 204K tokens. In latency-sensitive production environments, MiniMax-M2.5-highspeed 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.
任意のAIモデルを切り替えて比較
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MiniMax-M2.7-highspeed
高度な推論モデルMiniMax が提供する MiniMax-M2.7-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 $0.25/100万トークン・出力 $1/100万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。
$0.25 / 1M
$1.00 / 1M
204K tokens
N/A
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 1.7x cheaper for API usage (input + output tokens combined).
- MiniMax-M2.5-highspeed is faster at 150 tok/s vs 140 tok/s.
| Feature | MiniMax-M2.7-highspeed | MiniMax-M2.5-highspeed |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 82 | 78 |
Provider | MiniMax | MiniMax |
Input Price Cost per million input tokens | $0.25 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $1.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.17 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 204K tokens~306 A4 pages of text | 204K tokens~306 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 140 tok/s | 150 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 63% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 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.
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.
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.
MiniMax-M2.7-highspeed · Strengths & Key Advantages
- MiniMax の高度な High-Throughput Hosted Cloud Accelerator 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 $0.25/1M、出力 $1/1M）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 | MiniMax-M2.7-highspeed | MiniMax-M2.5-highspeed |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $0.04 |
Output Tokens / Task Tokens generated per task | 15K | 12K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | MiniMax-M2.7-highspeed | MiniMax-M2.5-highspeed |
|---|---|---|
| Official API Model ID (SDK) | minimax-m2-7-highspeed | minimax-m2-5-highspeed |
| API Protocol | MiniMax REST API (OpenAI-Compatible) | MiniMax REST API (OpenAI-Compatible) |
| Release Date | 2026-03 | 2026-02 |
| Knowledge Cutoff | January 2026 | December 2025 |
| Architecture | High-Throughput Hosted Cloud Accelerator | Latency-Optimized Hosted Cloud Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.25/1M in) across full 204K window | Flat rate ($0.15/1M in) across full 204K window |
| Prompt Cache Policy & TTL | Cached input at $0.025/1M tokens | Cached input at $0.015/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | 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 | MiniMax Production Safety & Alignment 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 | text |
| Output Modalities | text | text |
MiniMax-M2.7-highspeed vs MiniMax-M2.5-highspeed: よくある質問 (FAQ)
Which is more intelligent, MiniMax-M2.7-highspeed or MiniMax-M2.5-highspeed?
MiniMax-M2.7-highspeed is more intelligent. It scores 82 compared with 78 on the composite Intelligence Index.
Which model is faster?
MiniMax-M2.5-highspeed is faster, generating 150 tokens per second compared with 140 tokens per second.
Which model is cheaper to run?
MiniMax-M2.5-highspeed is cheaper at $0.10 per 1M tokens blended, compared with $0.17 (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 0.29s.
総合評価
Verdict & Model Selection Recommendation: Choose MiniMax-M2.7-highspeed if your priority is High-frequency developer copilots, automated ticket triage, and responsive agents. 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 MiniMax-M2.7-highspeed compare to MiniMax-M2.5-highspeed in API token pricing and cost per token?+
MiniMax-M2.7-highspeed costs $0.25 per 1M input tokens and $1.00 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: MiniMax-M2.7-highspeed or MiniMax-M2.5-highspeed?+
Both MiniMax-M2.7-highspeed and MiniMax-M2.5-highspeed offer an identical context window of 204K tokens (~153k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: MiniMax-M2.7-highspeed or MiniMax-M2.5-highspeed?+
MiniMax-M2.7-highspeed scores at the frontier of coding benchmarks, while MiniMax-M2.5-highspeed scores competitively for software generation. For complex autonomous debugging and agentic engineering, MiniMax-M2.7-highspeed provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between MiniMax-M2.7-highspeed and MiniMax-M2.5-highspeed?+
MiniMax-M2.7-highspeed 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 MiniMax-M2.7-highspeed and MiniMax-M2.5-highspeed?+
MiniMax-M2.7-highspeed generates approximately 140 tokens per second, while MiniMax-M2.5-highspeed delivers 150 tokens per second. In latency-sensitive production environments, MiniMax-M2.5-highspeed provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining MiniMax-M2.7-highspeed 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 MiniMax-M2.7-highspeed. 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