MiniMax M2.1
by MiniMaxminimax/minimax-m2.1
LLMCompare cannot run MiniMax M2.1 yet.No route to this model is configured on this site yet. Its prices and parameters are listed below for reference.
See the models that can be run nowMiniMax M2.1 at a glance
- Context length
- 204,800 tokens
- Maximum output
- 131,072 tokens
- Accepts
- Text
- Returns
- Text
- Features
- JSON outputTool callingPrompt caching
- Reasoning effort levels
- Not supported
- Released
- 23 Dec 2025
- Knowledge cutoff
- Not published
Model facts synced from the OpenRouter model list on 21 Oct 2026.
MiniMax M2.1 pricing by route
A route is the service LLMCompare calls to reach the model. MiniMax M2.1 has 1 route. Prices are in US dollars.
| Route | On LLMCompare | Inputper 1M tokens | Outputper 1M tokens | Cache readper 1M tokens | Price verified on | Source |
|---|---|---|---|---|---|---|
| OpenRouterDefault | Not configured | $0.30 | $1.20 | $0.03 | openrouter.ai/minimax/minimax-m2.1 |
The cost shown in an LLMCompare report is calculated from these prices as they were at the time of the run.
Cost calculator
Estimate what a batch of calls to MiniMax M2.1 costs. The starting numbers are examples. Change them to match your task.
Part of the input tokens above, billed at the cache read price.
- Cost per call
- $0.0009
- 2 credits
- Cost of 100 calls
- $0.09
- 200 credits
Calculated from the OpenRouter prices verified on 21 Oct 2026. Credits are the cost plus a 20% service fee, at $0.001 per credit, rounded up for each call, with a minimum of 1 per call. A real run is charged for the usage the provider reports.
Parameters you can set
These are the parameters LLMCompare can set when it calls the model through OpenRouter.
| Parameter | Type | Range or options | Default |
|---|---|---|---|
temperature | Number | 0 to 2in steps of 0.1 | Provider default |
top_p | Number | 0 to 1in steps of 0.05 | Provider default |
max_tokens | Integer | 1 to 131,072 | Provider default |
response_format | One of | textjson_object | Provider default |
About MiniMax M2.1
MiniMax-M2.1 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world...
Description as published for this model. LLMCompare has not edited it.
Models to compare with MiniMax M2.1
Text models from the same provider and models at the nearest price. The order is not a ranking.
| Add to comparison | Model | Context | Max output | Input priceper 1M tokens | Output priceper 1M tokens | Features | Released | On LLMCompare |
|---|---|---|---|---|---|---|---|---|
MiniMax M3MiniMaxminimax/minimax-m3 | 1.05M | 512k | $0.30 | $1.20 | JSONToolsCacheVision | No route yet | ||
MiniMax M2.7MiniMaxminimax/minimax-m2.7 | 205k | 131k | $0.30 | $1.20 | JSONToolsCache | No route yet | ||
MiniMax M2-herMiniMaxminimax/minimax-m2-her | 65.5k | 2k | $0.30 | $1.20 | Cache | No route yet | ||
Muse Glimmer 30BMetameta/muse-glimmer-30b | 131k | 16.4k | $0.30 | $1.20 | ReasoningJSONToolsCacheVision | No route yet | ||
LongCat 2.0Meituanmeituan/longcat-2.0 | 1.05M | 262k | $0.30 | $1.20 | ToolsCache | No route yet | ||
GLM 5.3Z.aiz-ai/glm-5.3 | 1.05M | 944k | $0.079 | $1.40 | ReasoningJSONToolsCache | No route yet |
Questions about MiniMax M2.1
- How much does MiniMax M2.1 cost?
- On the OpenRouter route, MiniMax M2.1 costs $0.30 per 1 million input tokens and $1.20 per 1 million output tokens, in US dollars. Input tokens that MiniMax M2.1 reads from the prompt cache cost $0.03 per 1 million tokens. LLMCompare verified this price on 21 Oct 2026.
- What is the context length of MiniMax M2.1?
- MiniMax M2.1 has a context length of 204,800 tokens and returns up to 131,072 output tokens in one call.
- What inputs does MiniMax M2.1 accept?
- MiniMax M2.1 accepts these inputs: Text. MiniMax M2.1 returns: Text.
- Can I test MiniMax M2.1 on my own data?
- Not yet. LLMCompare lists the prices and parameters of MiniMax M2.1, but no route to MiniMax M2.1 is configured on LLMCompare, so it cannot be selected for an evaluation.