MiniMax-M2.1
minimax/m2-1Por MiniMax · familia: minimax · lanzado 2025-12-23 · fecha de conocimiento: 2025-10
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding66
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)6/20
- Provider availability10/10
Agents75
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability10/10
JSON / structured output47
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume17/20
Cost efficiency58
- Headline price (log-scaled)58/95
- Has prompt-cache pricing0/5
Long context56
- Context window (100K → 2M)46/90
- Has published price for full window10/10
Production-readiness96
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers6/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $2.10 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $4.20 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $1.20 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $3.60 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $4.32 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalle de precios
Precio recomendado de minimax · MiniMax-M2.1
Proveedor más barato: minimax-coding-plan · Unknown entrada + Unknown salida
Disponible en 25 proveedores
| Proveedor | ID de modelo del proveedor | Entrada / 1M | Salida / 1M | Contexto | Lanzado |
|---|---|---|---|---|---|
| MiniMax (minimax.io) minimax | MiniMax-M2.1 | $0.300 | $1.20 | 205K | 2025-12-23 |
| MiniMax Coding Plan (minimax.io) minimax-coding-plan | MiniMax-M2.1 | Unknown | Unknown | 205K | 2025-12-23 |
| MiniMax Coding Plan (minimaxi.com) minimax-cn-coding-plan | MiniMax-M2.1 | Unknown | Unknown | 205K | 2025-12-23 |
| MiniMax (minimaxi.com) minimax-cn | MiniMax-M2.1 | $0.300 | $1.20 | 205K | 2025-12-23 |
| OpenRouter openrouter | minimax/minimax-m2.1 | $0.300 | $1.20 | 205K | 2025-12-23 |
| Vercel AI Gateway vercel | minimax/minimax-m2.1 | $0.300 | $1.20 | 205K | 2025-10-27 |
| Deep Infra deepinfra | MiniMaxAI/MiniMax-M2.1 | $0.280 | $1.20 | 197K | 2025-12-23 |
| Hugging Face huggingface | MiniMaxAI/MiniMax-M2.1 | $0.300 | $1.20 | 205K | 2025-12-23 |
| 302.AI 302ai | MiniMax-M2.1 | $0.300 | $1.20 | 1M | 2025-12-19 |
| NanoGPT nano-gpt | TEE/minimax-m2.1 | $0.300 | $1.20 | 200K | 2025-12-23 |
| NanoGPT nano-gpt | minimax/minimax-m2.1 | $0.330 | $1.32 | 200K | 2025-12-19 |
| SiliconFlow (China) siliconflow-cn | Pro/MiniMaxAI/MiniMax-M2.1 | $0.300 | $1.20 | 197K | 2025-12-23 |
| Moark moark | MiniMax-M2.1 | $2.10 | $8.40 | 205K | 2025-12-23 |
| Jiekou.AI jiekou | minimax/minimax-m2.1 | $0.300 | $1.20 | 205K | 2026-01 |
| ZenMux zenmux | minimax/minimax-m2.1 | $0.300 | $1.20 | 204K | 2025-12-22 |
| NovitaAI novita-ai | minimax/minimax-m2.1 | $0.300 | $1.20 | 205K | 2025-12-23 |
| Qiniu qiniu-ai | minimax/minimax-m2.1 | Unknown | Unknown | 205K | 2025-12-23 |
| Kilo Gateway kilo | minimax/minimax-m2.1 | $0.270 | $0.950 | 197K | 2025-12-23 |
| OpenCode Zen opencode | minimax-m2.1 | $0.300 | $1.20 | 205K | 2025-12-23 |
| Ollama Cloud ollama-cloud | minimax-m2.1 | Unknown | Unknown | 205K | 2025-12-23 |
| Meganova meganova | MiniMaxAI/MiniMax-M2.1 | $0.280 | $1.20 | 197K | 2025-12-23 |
| Synthetic synthetic | hf:MiniMaxAI/MiniMax-M2.1 | $0.550 | $2.19 | 205K | 2025-12-23 |
| Cortecs cortecs | minimax-m2.1 | $0.340 | $1.34 | 196K | 2025-12-23 |
| SiliconFlow siliconflow | MiniMaxAI/MiniMax-M2.1 | $0.300 | $1.20 | 197K | 2025-12-23 |
| LLM Gateway llmgateway | minimax-m2.1 | $0.300 | $1.20 | 205K | 2025-12-23 |
Inconsistencias de datos entre proveedores
- context_window varies: 1000000, 196000, 196608, 197000, 200000, 204000, 204800
- release_date varies (span 66d): 2025-10-27, 2025-12-19, 2025-12-22, 2025-12-23, 2026-01
Los proveedores reportan valores distintos para este modelo. Los datos clave de arriba usan un proveedor representativo; consulta la tabla para detalles por proveedor.
Frequently asked questions
How much does MiniMax-M2.1 cost?
MiniMax-M2.1 costs $0.300 per 1M input tokens and $1.20 per 1M output tokens, sourced from minimax. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of MiniMax-M2.1?
MiniMax-M2.1 has a context window of 205K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does MiniMax-M2.1 support tool calling?
Yes. MiniMax-M2.1 supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does MiniMax-M2.1 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for MiniMax-M2.1 in our data source. Verify with MiniMax's official documentation before relying on it in production.
Can MiniMax-M2.1 accept image input?
No. MiniMax-M2.1 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is MiniMax-M2.1 open-weight?
Yes. MiniMax-M2.1's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to MiniMax-M2.1?
If MiniMax-M2.1 doesn't fit, consider MiniMax-M2.5, MiniMax-M2.7, MiniMax-M2. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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Capability lists this model is in
Última actualización:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.