MiniMax-M2.7 vs DeepSeek-V4-Flash: which one for your task

Your agent ran for ten minutes, the bill arrived, and now you wonder whether the other cheap model would have done the same job for less. Here is the short version. If your answers are short and the prompt fits into 180K tokens, both models fit the job on paper, so the cost of your workload decides. If you need a single answer longer than 8,192 tokens, or more than 180K of context, take DeepSeek-V4-Flash. Both sit behind one key and one base_url, so you can run your own task on each and look at the numbers.

The specs, from the CheapTokens API facts (26 October 2026):

• MiniMax-M2.7 (open weights, 229B parameters per its Hugging Face page): id MiniMaxAI/MiniMax-M2.7, 180K context, up to 8,192 output tokens per answer.
• DeepSeek-V4-Flash, release 0731: id deepseek-ai/DeepSeek-V4-Flash-0731, 380K context, up to 32,768 output tokens per answer.

Both speak OpenAI Chat Completions and Anthropic Messages at https://api.cheaptokens.dev/v1 (the Anthropic SDK and Claude Code use the same address without /v1). Each model has one fixed price, there are no peak hours, and one balance covers every model. Current prices are on the pricing page; in this article they come from live price tags, so they are always today's.

Which one for which job#

The two numbers that split the choice are how much the model reads and how much it can write in one go.

Now the money, on three workloads.

Case 1: a support bot with 1,000 requests a day#

Say you run a support bot with 1,000 tickets a day. Each request carries the system prompt, a few knowledge-base snippets and the last messages, about 1,500 input tokens. The reply is around 250 tokens. This is what a day costs on each model, with OpenRouter next to it:

Read the totals, not the columns. A request is billed for input and output together, and in this shape the input is six times the output. In total, with input and output counted together, both models come out cheaper than OpenRouter. For reference, OpenRouter's prices per 1M tokens on 26 October 2026: MiniMax-M2.7 $0.30 in / $1.20 out, DeepSeek-V4-Flash $0.0475 in / $0.1568 out.

Case 2: an agent loop where the input piles up#

Agent tools such as OpenClaw, Cline or Aider resend the working context on every step: files they have read, tool results, the history so far. Assume an average of 40,000 input tokens and 600 output tokens per step (a tool call and a short comment), and 200 steps in a working session:

That's the catch with agent loops: you pay for what the model re-reads, not for what it writes. Input is more than sixty times the output here, so the input side of the table carries the bill.

The other thing that matters in a loop is where the context ends. A session on a big repository grows step by step; MiniMax-M2.7 holds up to 180K, DeepSeek-V4-Flash up to 380K. If your sessions regularly creep past 180K, the larger window saves you from trimming history by hand. For a day of agent work across more models, see the cheapest model for OpenClaw.

Case 3: one long file in a single answer#

Now the opposite shape. You ask for a complete test suite for a module in one answer, and it comes to about 20,000 tokens. One answer on MiniMax-M2.7 holds up to 8,192 tokens, so a request like this stops mid-file, and the API tells you so: finish_reason comes back as length instead of stop. DeepSeek-V4-Flash takes up to 32,768 tokens in one answer, so the whole file fits in one call. With a 6,000-token prompt that call costs $0.0021.

Answers that finish well under 8,192 tokens fit both models: a function, a diff, a JSON pulled out of a document, a ticket reply. Here the totals decide. Same 6,000-token prompt, same 2,000-token answer, so compare the rows:

If you want the long-answer side in detail, the DeepSeek-V4-Flash quickstart covers streaming and long outputs, and the MiniMax-M2.7 pricing article has the price side.

Try both on your own prompt#

The cleanest test is the same request sent to both models. Only the model field changes.

Export your key as CHEAPTOKENS_API_KEY (setup is in the docs) and run:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.cheaptokens.dev/v1",
    api_key=os.environ["CHEAPTOKENS_API_KEY"],
)

MODELS = [
    "MiniMaxAI/MiniMax-M2.7",
    "deepseek-ai/DeepSeek-V4-Flash-0731",
]
PROMPT = "Write a Python function that parses ISO 8601 dates and returns a datetime. Add three tests."

for model in MODELS:
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": PROMPT}],
        max_tokens=4000,
    )
    print(model)
    print("  finish_reason:", r.choices[0].finish_reason)
    print("  tokens in/out:", r.usage.prompt_tokens, r.usage.completion_tokens)

You get two short blocks: the model id, finish_reason and the two token counts from the usage field. Most likely finish_reason is stop, which means the model finished on its own; if it is length, the model hit max_tokens, so raise it. Put your own counts next to the live rates on the pricing page and you have the cost of your task on each model, not an average from an article. MiniMax's model card on Hugging Face (checked 26 October 2026) recommends temperature 1.0 and top_p 0.95 for M2.7, so add those two parameters to the M2.7 call if you want the maker's settings.

If it doesn't work#

• The model isn't found. Use the full id from the list above. Short names like minimax-m2.7 are labels for price tags; the API takes MiniMaxAI/MiniMax-M2.7 and deepseek-ai/DeepSeek-V4-Flash-0731.
• The connection fails. For the OpenAI SDK the address ends with /v1. For the Anthropic SDK and Claude Code it doesn't.
• The answer ends mid-sentence. Check finish_reason. If it is length, raise max_tokens up to the model's limit, or move the task to DeepSeek-V4-Flash when you need more than 8,192 tokens.

Both models are also on the DeepSeek and MiniMax pages if you want the product view.