Speed
Output tokens / second
Solar Mini 4 overview
Solar Mini 4 specs
- Model ID
upstage/solar-mini4- Developer
- Upstage
- Context window
- 524,288 tokens
- Max output
- 131,072 tokens
- Input
- Text
- Output
- Text
- API
- OpenAI-compatible, base URL
https://api.aimlapi.com/v1
What Is Solar Mini 4?
Solar Mini 4 is Upstage's compact language model and the small sibling of Solar Pro 4. It is a mixture-of-experts model with 35 billion parameters in total but only 3 billion active on any given token, and that ratio is the whole design: a request is priced and served roughly like a 3B model while the model can still draw on the knowledge held across all 35B. Upstage pairs it with a 524,288-token context window, the same window the larger Solar models get, and aims it squarely at agentic use.
On AI/ML API it is served as upstage/solar-mini4 through the standard chat completions endpoint, so any OpenAI-compatible client works with only the base URL changed.
Technical Reference
- Release date: 22 September 2026
- Architecture: mixture-of-experts, 35B total parameters, 3B active per token
- Endpoint: POST /v1/chat/completions
- Context window: 524,288 tokens
- Capabilities: reasoning, parallel tool calls, streaming, structured output, tool calling
Why The Mixture-of-Experts Ratio Matters Here
A dense 35B model would have to move all 35 billion parameters for every token it generates. Solar Mini 4 routes each token to a small subset of experts, so only about 3 billion take part. The practical consequences are the ones that show up in a bill and in a latency graph: cost per token lands in small-model territory, and throughput stays high enough for loops that make many short calls rather than a few long ones.
What the ratio does not buy is frontier-level depth on hard single-shot reasoning. A 3B active-parameter forward pass is a 3B forward pass, whatever the total parameter count, which is why Upstage positions this model for volume and agentic work rather than as a replacement for its flagship tier.
The 524K Context Window
524,288 tokens is roughly 1,500 pages of plain prose, or a mid-sized codebase with its tests and documentation. Two things follow. First, a whole class of retrieval plumbing becomes optional: for corpora that fit, the model can be handed the documents directly instead of a chunking pipeline, an embedding store and a reranker. Second, an agent's transcript does not have to be summarised and truncated every few steps, which is where long agent runs usually start losing the thread.
The output ceiling is 131,072 tokens, so the model can also write at length, a full refactor across many files, or a long structured report, without splitting the job into several calls and stitching the results.
Where Solar Mini 4 Makes The Most Sense
High-volume agentic work. Pipelines that run dozens of steps per task, where the per-step price decides whether the workflow is viable at all. Parallel tool calls let one step issue several tool invocations at once rather than serialising them.
Document processing at scale. Summarising, extracting and classifying across long documents, where the context window removes the chunking layer and the price makes running over a whole archive realistic.
Routine engineering tasks. Code review comments, migration edits, test generation and other work that is well specified and repetitive, the kind of job usually handed to a frontier model out of habit rather than need.
Structured extraction. Turning free text into JSON against a schema, at a price where running it over millions of records is not a budget conversation.
When To Reach For Something Else
Solar Mini 4 is text-only: no image or audio input. If a request involves screenshots, diagrams or scanned pages, Solar Pro 4 handles vision and adds web search. For problems where a single hard answer matters more than the cost of getting it, novel algorithmic work, intricate multi-step proofs, deep legal or medical analysis, a frontier-tier model is the better trade even at ten times the price. And for very short, latency-critical classification, a model with a smaller context window may return faster simply because there is less machinery involved.
Use cases
Agentic pipelines. Multi-step loops that plan, call tools and check their own work. Parallel tool calls mean a single step can fire several tool invocations at once, and the cached-input rate keeps the repeated system prompt from dominating the bill across thirty or forty steps.
Long-document analysis. Contracts, filings, research papers and support archives go into a single request rather than through a chunk-embed-rerank pipeline, because 524,288 tokens covers roughly 1,500 pages.
Repository-scale code work. Code review, test generation and mechanical migrations across a mid-sized codebase, with room for a 131,072-token reply when the answer is a large diff rather than a comment.
Structured extraction at volume. Turning invoices, tickets or product feeds into schema-conformant JSON, at a price where running the job across millions of records is affordable.
Capabilities
- Function calling
- Reasoning
- Streaming
- Structured outputs
Solar Mini 4 benchmarks by 
Artificial Analysis
See how Solar Mini 4 compares in generation speed and latency. Data retrieved Nov 3, 2026.
Benchmark results
Compare 14 models available on AI/ML API across 4 independent benchmarks.
Humanity's Last Exam
Expert-written questions across academic subjects.
Terminal-Bench 4.0
Agentic tasks completed in a command-line environment.
AA-LCR
Reasoning across long documents of about 100k tokens.
SciCode
Python coding tasks drawn from scientific research.
Solar Mini 4 API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $0.1058 | 1M tokens |
| Output | $0.4232 | 1M tokens |
| Cached input | $0.0106 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
Solar Mini 4 vs other models: token prices
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
 Solar Mini 4 | $0.1058 | $0.4232 |
| Solar Pro 4 | $0.0952 | $0.3809 |
 DeepSeek 4 Flash | $0.2645 | $0.4221 |
 Seed-2.0-Mini | $0.1058 | $0.4232 |
| UnslopNemo 12B | $0.4232 | $0.4232 |
How to use the Solar Mini 4 API
View docs
Build with your agent
Give your agent the model details, code and documentation.
Paste it into your coding agent to build the integration.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "upstage/solar-mini4",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "upstage/solar-mini4",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "upstage/solar-mini4",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to Solar Mini 4
Solar Mini 4 FAQ
What is Solar Mini 4?
Solar Mini 4 is Upstage's compact language model: a 35-billion-parameter mixture-of-experts with 3 billion active parameters and a 524,288-token context window. It is built for agentic use cases where cost per step matters.
How much does the Solar Mini 4 API cost?
Solar Mini 4 costs $0.1058 input and $0.4232 output per 1M tokens on AI/ML API, billed as you go with no subscription.
What is the context window of Solar Mini 4?
Solar Mini 4 accepts up to 524,288 tokens in a single request and can return up to 131,072 tokens, so long documents and large codebases fit without chunking.
What can Solar Mini 4 do?
It is a text-in, text-out model with reasoning, streaming, structured outputs, tool calling and parallel tool calls, the pieces an agent loop needs to plan, call tools and verify its own work.
How do I call Solar Mini 4 through the API?
Send a POST request to https://api.aimlapi.com/v1/chat/completions with upstage/solar-mini4 as the model id and your AI/ML API key in the Authorization header. The endpoint is OpenAI-compatible, so existing chat completion clients work unchanged.
What is the Solar Mini 4 model ID?
Use upstage/solar-mini4 as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.