MiniMax-M2.5 vs MiniMax-M2.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
MiniMax-M2.5 by MiniMax and MiniMax-M2.1 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, both models offer identical input token rates. On output generation, costs are matched. 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-M2.1 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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MiniMax-M2.5
Open-Weights-ModellMiniMax-M2.5 von MiniMax ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 204K Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
204K tokens
48%
MiniMax-M2.1
Open-Weights-ModellMiniMax-M2.1 von MiniMax ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 204K Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
204K tokens
N/A
Wichtigste Unterschiede auf einen Blick
- Both models cost the same under this workload — choose on capability, not price.
- MiniMax-M2.1 is faster at 88 tok/s vs 80 tok/s.
| Feature | MiniMax-M2.5 | MiniMax-M2.1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 82 | 80 |
Provider | MiniMax | MiniMax |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 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 | 16K | 8K |
Throughput Speed Tokens generated per second | 80 tok/s | 88 tok/s |
SWE-bench Verified Real-world software engineering | 48% | N/A |
GPQA Diamond Graduate-level reasoning | 64% | 61% |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
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.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
MiniMax-M2.5 · Strengths & Key Advantages
- Fortschrittliche Coding-Centric Sparse MoE Transformer-Architektur von MiniMax mit 204K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
MiniMax-M2.1 · Strengths & Key Advantages
- Fortschrittliche Refined Agentic Mixture-of-Experts Transformer-Architektur von MiniMax mit 204K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | MiniMax-M2.5 | MiniMax-M2.1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 18K | 16K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 4K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | MiniMax-M2.5 | MiniMax-M2.1 |
|---|---|---|
| Official API Model ID (SDK) | minimax-m2-5 | minimax-m2-1 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-02 | 2025-12 |
| Knowledge Cutoff | December 2025 | October 2025 |
| Architecture | Coding-Centric Sparse MoE Transformer | Refined Agentic Mixture-of-Experts Transformer |
| Parameters | Confidential / 160B MoE | Confidential / 130B MoE |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 99 GB VRAM (4-bit Quantized) · 344 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 81 GB VRAM (4-bit Quantized) · 280 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
MiniMax-M2.5 vs MiniMax-M2.1: Schnelle Antworten (FAQ)
Which is more intelligent, MiniMax-M2.5 or MiniMax-M2.1?
MiniMax-M2.5 is more intelligent. It scores 82 compared with 80 on the composite Intelligence Index.
Which model is faster?
MiniMax-M2.1 is faster, generating 88 tokens per second compared with 80 tokens per second.
Which model has lower latency?
MiniMax-M2.1 has lower latency, with a time to first token of 0.42s compared with 0.47s.
Fazit
Verdict & Model Selection Recommendation: Choose MiniMax-M2.5 if your priority is On-premise code assistants, internal corporate automation, and technical data transformation. Its performance in demanding tasks justifies its operational token cost. Choose MiniMax-M2.1 if your workflow prioritizes Agentic tool loops, enterprise office automation, and structured data extraction. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does MiniMax-M2.5 compare to MiniMax-M2.1 in API token pricing and cost per token?+
Both MiniMax-M2.5 and MiniMax-M2.1 are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: MiniMax-M2.5 or MiniMax-M2.1?+
Both MiniMax-M2.5 and MiniMax-M2.1 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-M2.5 or MiniMax-M2.1?+
MiniMax-M2.5 scores 48% on SWE-bench Verified, while MiniMax-M2.1 scores competitively for software generation. For complex autonomous debugging and agentic engineering, MiniMax-M2.5 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between MiniMax-M2.5 and MiniMax-M2.1?+
MiniMax-M2.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, MiniMax-M2.1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between MiniMax-M2.5 and MiniMax-M2.1?+
MiniMax-M2.5 generates approximately 80 tokens per second, while MiniMax-M2.1 delivers 88 tokens per second. In latency-sensitive production environments, MiniMax-M2.1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining MiniMax-M2.5 and MiniMax-M2.1 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to MiniMax-M2.1, and route deep architectural or complex reasoning queries to MiniMax-M2.5. This balanced routing strategy maximizes system throughput and answer quality.
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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs