minimax-m3.1-flash
TensorX/🇪🇺 EU/chat
MiniMax M3.1 Flash is a fast multimodal model in the MiniMax M3 family, built for agentic cowork automation, coding and visual tasks. It scores 72.5% on AutomationBench AA (best among open models), 73.67% on DeepSWE, 91.0 on BabyVision with tools, 82.5 on CharXiv RQ, 56% on AA Omniscience and 48% on HLE. Supports image input, tool calling, structured JSON output, reasoning and prompt caching. Ideal for multi step workflows, coding agents, frontend and presentation generation, and professional analysis at low cost.MoreLess
Which id to call
tensorx/minimax-m3.1-flashThis exact deployment on TensorX, with no routing and no failover. Send it as the model field.
Input /1M
$0.30
$0.07 cached
Output /1M
$1.20
4.0x input
Context
1.0M
1.0M output
Paid /1M
$0.09
measured, cache included
Capabilities 5/8
Provider rates
What minimax-m3.1-flash costs
Provider prices per 1M tokens, updated November 2, 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.
Input /1M
$0.30
Output /1M
$1.20
Cache write /1M
-
Cache read /1M
$0.07
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 $0.07 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.
tensorx/minimax-m3.1-flashThis exact deployment on TensorX, 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="tensorx/minimax-m3.1-flash", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Live from production
TensorX on minimax-m3.1-flash, measured
What this provider was measured doing on minimax-m3.1-flash across Requesty traffic.moreless
Whole-window figures, because that is the grain published per provider. They cover TensorX 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
1.88s
median
p95 wait
6.49s
slowest 5%
Output speed
161/s
median tokens
Paid /1M
$0.09
blended, cache included
Cache hit
98.2%
of input tokens
Compare these against the other 1 provider serving minimax-m3.1-flash.
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Released 2026-10-19
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
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 TensorX
Newest first, on the same provider and the same key.
Reference
minimax-m3.1-flash questions
How much does minimax-m3.1-flash cost?
minimax-m3.1-flash 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. 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-m3.1-flash?
minimax-m3.1-flash has a context window of 1.0M tokens, with a maximum output of 1.0M tokens per response. That's roughly 1,398 words of input you can fit in a single prompt.
How does minimax-m3.1-flash perform on benchmarks?
minimax-m3.1-flash scores 53.4% on SciCode, 48.0% on Humanity's Last Exam, 28.9% on Intelligence 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-m3.1-flash do?
minimax-m3.1-flash 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-m3.1-flash 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 "tensorx/minimax-m3.1-flash". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run minimax-m3.1-flash through Requesty?
Yes. minimax-m3.1-flash runs through Requesty's OpenAI-compatible API, served from TensorX. You do not host the model yourself: point base_url at Requesty, set the model to "tensorx/minimax-m3.1-flash", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call minimax-m3.1-flash through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.