MiniMax M2.5 — SOTA productivity LLM with strong coding + agentic capabilities, 200k context, ~60 tps output.
MiniMax M2.5 is a large language model developed by Minimax and made available through OrcaRouter's API. It is designed to process natural language inputs and generate text outputs. The model is…
MiniMax M2.5 excels at tasks that involve understanding and generating text, especially over long contexts. It can summarize lengthy documents, answer questions based on extensive background material, write coherent essays, and perform complex reasoning. The model handles instruction following and can be used for creative writing, code generation, and translation. However, its output is limited to 2048 tokens per request, so it is not suited for generating very long responses in one pass. For longer outputs, you may need to chain multiple calls or use streaming. The model's strength is in leveraging its large context to produce accurate and contextually aware outputs.
The best use cases for MiniMax M2.5 leverage its 204800-token context window. Examples include analyzing legal contracts of dozens of pages: the model can ingest the entire contract and answer detailed questions about clauses. Another use case is building a chat assistant that remembers an entire conversation history across many sessions. Educational applications can provide explanations based on entire textbook chapters. Code analysis tools can process whole repositories to suggest fixes or document functionality. For any task requiring synthesis of large volumes of text, MiniMax M2.5 is a strong candidate.
Despite its strengths, MiniMax M2.5 may not be the most cost-effective choice for all scenarios. If your task has a short context requirement (e.g., a few thousand tokens), a smaller model with lower per-token cost would suffice. Similarly, if you need faster inference or higher throughput, smaller models typically offer lower latency. For tasks that do not need the large context, you may be overpaying for unused capacity. OrcaRouter provides access to many models; evaluate your token usage and latency requirements before committing. Also consider that the maximum output is 2048 tokens, which may be insufficient for generating lengthy reports in a single call.
MiniMax M2.5 accepts only text as input. It does not support image, audio, video, or file uploads directly. If your application requires multimodal input (e.g., analyzing images or transcribing speech), you will need to preprocess that data into text or use a different model. The output is also text-only. The model can generate plain text or structured formats like JSON if prompted appropriately. Because of its text-only nature, it is best suited for classic natural language processing tasks. There is no built-in support for function calling or tool use, but these can be implemented manually via prompt engineering.
τ²-Bench is a benchmark designed to evaluate language models on task-oriented performance. It measures the ability of models to follow instructions and complete real-world tasks such as information extraction, summarization, and reasoning. MiniMax M2.5 achieved a score of 95.3 on this benchmark. This indicates that the model performs well on these task-oriented evaluations relative to other models tested on the same benchmark. However, τ²-Bench is just one metric; performance may vary on other benchmarks or in real-world applications. Users should consider their specific use case and test the model accordingly.
Based on its τ²-Bench score of 95.3, MiniMax M2.5 demonstrates strong capabilities in task-oriented scenarios. The large context window allows it to incorporate extensive background information, which likely contributes to its performance on tasks requiring deep context. The model is also competitively priced for its context size, making it an economical choice for long-context applications. It handles text-only inputs efficiently. Users have reported good results on long-document summarization and question answering. The model's architecture is designed to maintain coherence over thousands of tokens.
MiniMax M2.5 has several limitations. First, it is text-only and cannot process images or other modalities. Second, the maximum output is 2048 tokens, which restricts the length of individual responses. Third, while the τ²-Bench score is high, there are many other benchmarks (e.g., MMLU, HumanEval) on which we do not have public scores for this model. Performance in creative writing or code generation may differ. Fourth, latency and throughput data are not provided; real-world speed depends on provider infrastructure and load. Finally, the model may not be as widely tested as some alternatives, so edge-case behavior is less predictable.
No specific latency or throughput figures are publicly available for MiniMax M2.5. In general, models with very large context windows can be slower than smaller models due to the computational cost of processing many tokens. The actual response time will depend on the length of the input, the number of output tokens requested, and the current load on Minimax's servers as accessed via OrcaRouter. Users requiring low latency should test with their typical prompt sizes. Streaming responses can help reduce perceived latency. OrcaRouter's infrastructure may add a small overhead, but it is designed to be minimal.
MiniMax M2.5 is priced at $0.30 per 1 million input tokens and $1.20 per 1 million output tokens. This pricing is set by the provider Minimax and is passed through by OrcaRouter without any additional markup. Tokens are counted by the provider's tokenizer; input tokens include the prompt and any system message or context, while output tokens are the generated response. There are no additional fees for API calls beyond token consumption. This pricing makes MiniMax M2.5 cost-effective for long-context tasks, especially when compared to some other large-context models.
When evaluating cost, consider that the effective cost per task depends on the number of input and output tokens. For tasks with very long prompts (e.g., 200k tokens), the input cost can be significant: $0.30 per million tokens means 200k tokens cost $0.06 per call. Output costs are higher per token, so tasks generating long responses will incur more expense. If your prompts are short, a cheaper model with similar output quality may be more economical. Also, if you can cache or reuse portions of the context, you might reduce costs. There is no mention of discounts for high volume or batch processing; check OrcaRouter for potential volume pricing.
OrcaRouter bills MiniMax M2.5 at the provider rate with zero markup. The price you pay per token is exactly what Minimax charges. There are no hidden fees or platform surcharges. This transparent pricing applies to all models on OrcaRouter. Your usage is tracked and billed based on token counts reported by OrcaRouter. You can monitor costs in the OrcaRouter dashboard. Because there is no markup, the cost of using MiniMax M2.5 through OrcaRouter is identical to using it directly from Minimax, while gaining the benefits of a unified API and simplified integration.
No specific caching mechanisms for MiniMax M2.5 are mentioned in the provided facts. Some providers offer prompt caching where repeated input prefixes are not recharged; it is unknown if Minimax supports this. To optimize costs, you can minimize the input length by trimming unnecessary context, or use shorter system prompts. For applications with many similar calls, consider batching multiple questions into a single prompt to share input costs. OrcaRouter does not charge extra for caching, but you would need to implement application-level caching of responses if desired.
To call MiniMax M2.5, send a POST request to OrcaRouter's OpenAI-compatible endpoint. Set the base URL to https://api.orcarouter.ai/v1 and include your API key in the Authorization header (Bearer token). In the request body, specify the model as "minimax/minimax-m2.5". You can pass standard parameters: messages (array of role/content objects), temperature, max_tokens (up to 2048), top_p, frequency_penalty, presence_penalty, and stop sequences. The response will be a JSON object with the generated text. OrcaRouter supports streaming by setting stream=true, which returns tokens as they are generated.
MiniMax M2.5 supports the typical parameters of OpenAI-compatible chat completions. The messages parameter accepts system, user, and assistant roles. The max_tokens parameter is capped at 2048, matching the model's maximum output length. The temperature parameter controls randomness (0.0 to 2.0, default typically 0.7). top_p uses nucleus sampling. frequency_penalty and presence_penalty can adjust repetitiveness. OrcaRouter also supports the n parameter for multiple completions, but note that this multiplies cost. You can use stop sequences to halt generation. No function calling or tool use is documented specifically for this model.
If you are currently using an OpenAI model or another provider's API, migrating to MiniMax M2.5 via OrcaRouter is straightforward. Change your base URL to https://api.orcarouter.ai/v1 and update the model name to "minimax/minimax-m2.5". Your existing code for chat completions will work with minor adjustments. Ensure your API key is from OrcaRouter rather than OpenAI. You may need to adjust parameters: for example, max_tokens cannot exceed 2048. Also note that system prompt behavior may differ slightly between models; test thoroughly. OrcaRouter provides a consistent interface, reducing migration friction.
Authentication is done via an API key passed in the Authorization header. You can obtain an API key from your OrcaRouter account dashboard. If you receive a 401 error, check that your key is correct and active. Rate limits and usage quotas are managed by OrcaRouter; check your plan for details. For errors like 400 (bad request), verify that your request body conforms to the expected format. OrcaRouter logs relevant error messages. Network timeouts can occur; implement retry logic with exponential backoff. There is no cost for failed requests beyond the token usage that was processed, but incomplete responses may still incur input token charges.
MiniMax M2.5 competes with other models offering large context windows, such as Google's Gemini and Anthropic's Claude, which also support over 100k tokens. Its pricing of $0.30/$1.20 per million tokens is competitive, often lower than some alternatives. The τ²-Bench score of 95.3 is a strong indicator of task-oriented performance. However, without direct comparisons on other benchmarks, it's difficult to assess relative quality. MiniMax M2.5 is text-only; models like Gemini also support images. Your choice should depend on multimodal needs, specific benchmark performance, and cost. OrcaRouter allows you to test multiple models easily.
Smaller models (e.g., GPT-4o-mini, Llama 3.1 8B) have much smaller context windows (typically 8k-128k) and lower per-token costs. For tasks that fit within a smaller context, these models are more economical and often faster. MiniMax M2.5's advantage is its 204800-token context, cost-effective at scale. If your prompts rarely exceed 50k tokens, a cheaper model may be better. Also, smaller models may have lower latency. Use OrcaRouter to benchmark on your specific data to decide. The τ²-Bench score is specific to M2.5; smaller models' scores on that benchmark may be lower.
Without direct benchmark comparisons, we can compare on specs. GPT-4 and Claude have proven track records on many benchmarks, including coding and reasoning. MiniMax M2.5 offers a larger context (204800 vs 128k for GPT-4 Turbo) at lower per-token prices. However, GPT-4 and Claude have larger output limits (4k-8k tokens) and support multimodal inputs. MiniMax M2.5 is text-only and limits output to 2048 tokens. For text-only long-context tasks, MiniMax M2.5 may be more cost-effective. For tasks requiring vision or longer generations, alternatives are better. OrcaRouter gives you access to all, enabling side-by-side testing.
Using MiniMax M2.5 alongside other models can optimize cost and performance. For example, use a small, fast model for simple queries and MiniMax M2.5 only when large context is needed. Or use it as a long-term memory buffer in a multi-turn conversation. OrcaRouter's unified API simplifies switching between models without code changes. You can also chain models: use a lightweight model to summarize context, then feed the summary to MiniMax. Because the pricing is transparent, you can budget accordingly. MiniMax M2.5 is a solid addition to any toolkit where deep context comprehension is required.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="minimax/minimax-m2.5",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)max_completion_tokensreasoningreasoning_splitstreamtemperaturetop_p| Input / 1M tokens | $0.300 |
| Output / 1M tokens | $1.20 |
| Cache read / 1M | $0.030 |
| Cache write / 1M | $0.375 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/minimax/minimax-m2.5Open @misc{orcarouter_minimax_m2_5,
title = {MiniMax M2.5 API},
author = {MiniMax},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/minimax/minimax-m2.5}
}MiniMax. (2026). MiniMax M2.5 API. OrcaRouter. https://www.orcarouter.ai/models/minimax/minimax-m2.5