Particle-1 is a token-efficient model from Particle AI, post-trained on DeepSeek V4.1 Flash using reinforcement learning to reduce token usage while maintaining accuracy. It excels in software engineering tasks such as terminal workflows, React app building, data engineering, and codebase modifications. The model features a 1M token context window, supports text and image inputs, tool calling, and produces shorter reasoning chains that lead to faster responses and lower costs.
According to the official announcement, Particle-1 uses around 45% fewer tokens on average across tasks compared to the base model, with benchmark results showing 28.4%–52.2% fewer output tokens and 34.4%–51.6% lower cost per task on specialized benchmarks like Terminal-Bench 2.1, ReactBench, ADE Bench, and DeepSWE 1.1. General benchmarks remain close to the base model (e.g., GPQA Diamond 90.40% vs base 90.91%).
See the model page on qubax.ai
Price Comparison
Prices as of Oct 31, 2026, source: qubax.ai/price-index
| Provider | Input ($/M) | Output ($/M) |
|---|---|---|
| Qubax | 0.15 | 0.60 |
| OpenRouter | 0.05 | 0.50 |
Qubax offers competitive access with reliable uptime and simple billing. For the latest prices see the full price index.
How to Use Particle-1 with OpenAI SDK
import openai
client = openai.OpenAI(
base_url="https://api.qubax.ai/v1",
api_key="your-qubax-api-key"
)
response = client.chat.completions.create(
model="particle-1",
messages=[{"role": "user", "content": "Write a Python function to fetch data from an API and parse JSON."}],
temperature=0.7,
max_tokens=2000
)
print(response.choices[0].message.content)This example uses the standard OpenAI SDK with Qubax's endpoint. See our /docs/quickstart for full setup including streaming and tool calling.
When to Use Particle-1 vs Alternatives
Particle-1 shines for agentic coding and efficiency-focused tasks where token count directly impacts speed and cost. Compare it to two other strong options we offer:
- DeepSeek V4.1 Flash — the base model, strong general performance but higher token usage on complex tasks. Use when raw capability is prioritized over efficiency.
- Claude 3.5 Sonnet (or latest equivalent) — excellent for nuanced reasoning and writing; choose this for creative or highly complex non-coding workflows where precision outweighs token savings.
Check current pricing on the /price-index to decide based on your workload. Many developers route between these models dynamically for optimal cost/performance (see our cost router tutorial).
FAQ
What is the price of Particle-1 on Qubax?
Input is $0.15 per million tokens and output is $0.60 per million tokens (as of Oct 31, 2026). This is competitive with OpenRouter pricing of $0.05/$0.50.
What is the context window of Particle-1?
Particle-1 supports a 1,048,576 token (1M) context window, making it suitable for large codebases or long documents.
How do I call Particle-1 using the OpenAI SDK?
Set base_url="https://api.qubax.ai/v1" and use model="particle-1" in your completion call. Full example above and in /docs/quickstart.
Is there a cheaper option than Particle-1 for coding tasks?
Depending on workload, DeepSeek variants or other open market options may be cheaper on input. Always compare live on the /price-index.
Does Particle-1 support tool calling and images?
Yes, it supports tool calling, image inputs, and streaming responses.
Ready to try Particle-1? Sign up free at qubax.ai/register and get immediate access to this and 370+ other models.
For more comparisons, see our recent post on LLM cost audits.
