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Fireworks AI

minimax-m2.7

Mixture of Experts language model. M2.7 is capable of building complex agent harnesses and completing highly elaborate productivity tasks, leveraging Agent Teams, complex Skills, and dynamic tool search.

🔧Tool calling⚡Caching
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Specifications

Context window197K tokens
Max output25K tokens
API typechat
AddedApr 29, 2026
Model IDfireworks/minimax-m2.7
Data retentionNo
Used for trainingNo
Provider location🇺🇸 US
Privacy policyFireworks Privacy Policy →

Benchmarks

Benchmarks haven't been published yet for this exact variant.

Some variants (region-specific deployments, highspeed tiers) share benchmarks with their base model — check the base model page or the Fireworks AI models overview.

Pricing

Input / 1M
$0.30
Output / 1M
$1.20
Cache write
—
Cache read / 1M
$0.06
Estimated cost
100K input + 10K output$0.0420
1M input + 100K output$0.42
10M input + 1M output$4.20

Requesty charges exactly what the upstream provider charges — no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to fireworks/minimax-m2.7.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="fireworks/minimax-m2.7",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

Other Fireworks AI models

qwen3.6-plus
131K ctx$0.50/1M
glm-5.1
202K ctx$1.40/1M
deepseek-v4-pro
1M ctx$1.74/1M
kimi-k2.6
262K ctx$0.95/1M
minimax-m2.5
197K ctx$0.30/1M
glm-5
203K ctx$1.00/1M

Frequently asked questions

How much does minimax-m2.7 cost?
minimax-m2.7 is priced at $0.30 per million input tokens and $1.20 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Requesty charges exactly what the upstream provider charges — we don't add markup.
What is the context window of minimax-m2.7?
minimax-m2.7 has a context window of 197K tokens, with a maximum output of 25K tokens per response. That's roughly 262 words of input you can fit in a single prompt.
What can minimax-m2.7 do?
minimax-m2.7 supports tool calling, prompt caching. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use minimax-m2.7 with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "fireworks/minimax-m2.7". The Quickstart above shows Python, JavaScript and cURL snippets.

Access minimax-m2.7 through Requesty

One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.

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