particle-ai
Particle AI: Particle-1 (free)
particle-ai/particle-1:free
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter's catalog presents particle-ai/particle-1:free as a Particle AI coding model aimed at agentic software work, with an emphasis on efficient token use. The listing reports post-training on DeepSeek V4.1 Flash, text and image inputs with text output, optional adjustable reasoning enabled by default, tool and structured-output support, and a 1,048,576-token context window. These are catalog claims rather than measured results; no independent model card or benchmark evidence was included, so workload validation remains necessary.[1]
Strengths
- Catalog-reported fit for agentic coding, including terminal workflows and codebase edits, with an emphasis on token efficiency.[1]
- Accepts text and image inputs, emits text, and is listed with a 1,048,576-token context window.[1]
- Reasoning is optional but enabled by default, with selectable effort; tools and structured outputs are supported.[1]
Limitations
Where to use it
- Evaluate for repository-scale code modification or terminal-oriented agent workflows where the advertised long context may help.[1]
- Test on multimodal programming assistance, such as interpreting screenshots or diagrams and returning textual guidance.[1]
- Prototype tool-using assistants that need structured text responses and configurable reasoning effort.[1]
Sources
- [1] OpenRouter catalog · Particle AI: Particle-1 (free)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Particle-1 is a token-efficient agentic coding model from Particle AI, post-trained on DeepSeek V4.1 Flash. It is suited for software engineering work such as terminal workflows, codebase changes, and data...
Specifications
- Model ID
- particle-ai/particle-1:free
- Canonical ID
- particle-ai/particle-1-20261027
- Creator
- particle-ai
- Added to OpenRouter
- 28.10.2026
- Context length
- 1,048,576
- Modality
- text+image->text
- Input modalities
- text, image
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0 / out $0
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | reasoning: optional · default high · efforts: max, xhigh, high, medium, low, minimal, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default high · efforts: max, xhigh, high, medium, low, minimal, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | — |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
No evaluations are supplied for this model.
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| Particle AI | 1,048,576 | $0 | $0 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.