grok-3-mini:low
xAI Corp./🇺🇸 US/chat
A lightweight model that thinks before responding. Fast, smart, and great for logic-based tasks that do not require deep domain knowledge. The raw thinking traces are accessible.MoreLess
Which id to call
xai/grok-3-mini:lowThis exact deployment on xAI Corp., with no routing and no failover. Send it as the model field.
Input /1M
$0.30
xAI Corp.
Output /1M
$0.50
1.7x input
Context
131K
tokens
Added
Feb 2025
chat
Capabilities 4/8
Provider rates
What grok-3-mini:low costs
Provider prices per 1M tokens, updated September 12, 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
$0.50
Cache write /1M
-
Cache read /1M
-
What a workload costs
- 100K input + 10K output
- $0.0350
- 1M input + 100K output
- $0.35
- 10M input + 1M output
- $3.50
At the rates above. This endpoint does not offer prompt caching, so repeated context is billed as new input every time.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to xai/grok-3-mini:low. 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="xai/grok-3-mini:low", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Released 2025-02-19
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
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 xAI Corp.
Newest first, on the same provider and the same key.
Reference
grok-3-mini:low questions
How much does grok-3-mini:low cost?
grok-3-mini:low is priced at $0.30 per million input tokens and $0.50 per million output tokens when accessed via Requesty. 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 grok-3-mini:low?
grok-3-mini:low has a context window of 131K tokens. That's roughly 175 words of input you can fit in a single prompt.
How does grok-3-mini:low perform on benchmarks?
grok-3-mini:low scores 90.4% on τ²-Bench, 84.7% on Math Index, 84.7% on AIME 2025. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can grok-3-mini:low do?
grok-3-mini:low supports tool calling, extended reasoning, web search, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use grok-3-mini:low 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 "xai/grok-3-mini:low". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run grok-3-mini:low through Requesty?
Yes. grok-3-mini:low runs through Requesty's OpenAI-compatible API, served from xAI Corp.. You do not host the model yourself: point base_url at Requesty, set the model to "xai/grok-3-mini:low", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call grok-3-mini:low through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.