minimax-m3
MiniMax/🇸🇬 Singapore/chat50% off
MiniMax M3 is a frontier multimodal model with a 1M-token context window built on MiniMax Sparse Attention (MSA). It reaches frontier-level performance on coding and agentic tasks, surpassing GPT-5.5 and Gemini 3.1 Pro on SWE-Bench Pro and approaching Claude Opus 4.7. It natively supports image and video input and is the first open-weight model to combine frontier coding, ultra-long context, and native multimodality.MoreLess
Which id to call
minimaxi/minimax-m3This exact deployment on MiniMax, with no routing and no failover. Send it as the model field.
Input /1M
$0.30
$0.60 list
$0.06 cached
Output /1M
$1.20
$2.40 list
4.0x input
Context
1M
128K output
Paid /1M
$0.09
measured, cache included
Capabilities 5/8
Provider rates
What minimax-m3 costs
Provider prices per 1M tokens, updated September 6, 2026.moreless
These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.
This endpoint is discounted. List is $0.60 per 1M input and $2.40 per 1M output, and the rates below are what you pay. The discount applies to every request on this endpoint, with nothing to claim or enter.
Input /1M
$0.30
$0.60 list
Output /1M
$1.20
$2.40 list
Cache write /1M
-
Cache read /1M
$0.06
What a workload costs
- 100K input + 10K output
- $0.0420
- 1M input + 100K output
- $0.42
- 10M input + 1M output
- $4.20
At the rates above, before caching. A cache read costs $0.06 per 1M, so repeated context lands under these figures.
model=
Which id to call
One base url, https://router.requesty.ai/v1, and one key for every id here.
minimaxi/minimax-m3This exact deployment on MiniMax, with no routing and no failover. Send it as the model field.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to any id on the left. Existing OpenAI SDK code needs no other edit.
123456789101112131415from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1", ) response = client.chat.completions.create( model="minimaxi/minimax-m3", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Live from production
MiniMax on minimax-m3, measured
What this provider was measured doing on minimax-m3 across Requesty traffic.moreless
Whole-window figures, because that is the grain published per provider. They cover MiniMax serving this model in every region it serves it from, so a region-pinned deployment shares them with its siblings. A figure is absent where no qualifying sample exists.
First token
1.90s
median
p95 wait
5.37s
slowest 5%
Output speed
125/s
median tokens
Paid /1M
$0.09
blended, cache included
Cache hit
89.6%
of input tokens
Compare these against the other 2 providers serving minimax-m3.
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Released 2026-06-01
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
Artificial Analysis Coding Index: a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.
Graduate-level physics, chemistry & biology questions designed to resist Googling.
Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.
Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.
Same provider
More from MiniMax
Newest first, on the same provider and the same key.
Reference
minimax-m3 questions
How much does minimax-m3 cost?
minimax-m3 is priced at $0.30 per million input tokens and $1.20 per million output tokens when accessed via Requesty. Those figures include a 50% discount on this endpoint, off a list rate of $0.60 per million input tokens and $2.40 per million output tokens. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.
What is the context window of minimax-m3?
minimax-m3 has a context window of 1M tokens, with a maximum output of 128K tokens per response. That's roughly 1,333 words of input you can fit in a single prompt.
How does minimax-m3 perform on benchmarks?
minimax-m3 scores 92.9% on GPQA Diamond, 88.9% on τ²-Bench, 58.6% on Coding Index. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can minimax-m3 do?
minimax-m3 supports vision input, tool calling, extended reasoning, prompt caching, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use minimax-m3 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 "minimaxi/minimax-m3". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run minimax-m3 through Requesty?
Yes. minimax-m3 runs through Requesty's OpenAI-compatible API, served from MiniMax. You do not host the model yourself: point base_url at Requesty, set the model to "minimaxi/minimax-m3", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call minimax-m3 through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.