Llama 3.2 1B Instruct
Meta · released Sep 18, 2024 · meta-llama/Llama-3.2-1B-Instruct
- Type
- Open weightsLlama 3.2 Community License
- Params
- 1.2B
- Context
- 60K
about 45K words of context · download allowed, licence restricts use
Our take
Written Sep 2, 2026Llama 3.2 is Meta's smallest instruction-tuned model, built for edge devices and cost-sensitive tasks where a tiny footprint matters more than top-tier capability. It handles up to 60,000 tokens in a single request and is available at extremely low hosted rates.
Pick this for ultra-low-cost inference where every token counts, or for edge deployment on hardware that cannot fit larger models. Use it for basic instruction-following tasks where measured accuracy in the mid-fifties is sufficient. Skip it if you need strong reasoning, graduate-level knowledge, or high instruction-following precision.
The case for it
- Extremely low hosting cost: the cheapest tracked offer in its set, with output rates well under most peers.
- Fast edge inference at 113 tokens per second through one tracked host.
- Broad arena coverage for its size, with measured scores across coding, maths, hard prompts, instruction following and creative writing.
The case against it
- Near floor-level performance on graduate-level science and knowledge benchmarks.
- Instruction-following accuracy of 54.8% leaves substantial room for improvement versus larger models.
How good is it?
A small open text model for basic chat, though it trails most others at everyday questions, writing and code.
- getting answers to everyday questionsArena Text (overall) · 168th of 168
- drafts, rewrites and editingArena Creative Writing · 168th of 168
- writing and completing codeArena Coding · 168th of 168
EverydayGeneral questions and everyday reasoning
Arena Text (overall)168th of 168 · 1111
CodingWriting and fixing code on its own
Arena Coding168th of 168 · 1149
Arena Coding is the only board that has scored it for this.
AgenticPlanning, calling tools, staying on task
Not yet scored on Arena Agent.
WritingDrafting and rewriting prose
Arena Creative Writing168th of 168 · 1083
Arena Creative Writing is the only board that has scored it for this.
These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done.
Every published score for this model9 scoresEvery figure we hold, from 9 boards, with who ran it and a link to the source — including the boards no rating above is built on.
Can you run it yourself?
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 20.8 GB spare means a 10% error in the size would not change the answer.
Apple M1 Pro (16-core GPU) · 32 GB
Room to spare. 22 GB spare means a 10% error in the size would not change the answer.
Apple M2 (8-core GPU, 8GB unified) · 8 GB
Room to spare. 4 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first.
Check against your own machine → · Where to rent it hosted →
Or rent it from someone else
Prices checked 2 hours ago — each listing carries its own date.
- per 1M tokens
- $0.020 in / $0.020 out
- Context served
- 131K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| Novita AIDirect | $0.020 / $0.020checked 2 hours ago | 131K | not measured | Unknown | Unknown | Unknown |
| OpenRouterOpenRouter's own listing | $0.027 / $0.20checked 2 hours ago | 60K | not measured | Unknown | Unknown | Unknown |
| Cloudflare Workers AIThrough OpenRouter | $0.027 / $0.20checked 2 hours ago | 60K54K max reply | 83 tok/s | No | Yesunknown period | Unknown |
OpenRouter
Cloudflare Workers AIAcross the 3 listings we hold: 1 says it does not train on prompts, 0 say they do and 2 do not say. 0 appear in the zero-retention registry we check; the rest are unknown to us.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| Novita AIDirect | |||
| OpenRouterOpenRouter's own listing | ✗ | ✗ | ✗ |
| Cloudflare Workers AIThrough OpenRouter | ✗ | ✗ | ✗ |
Tool calling: 0 of 3 listings say yes, 2 say no, 1 publishes no parameter list. JSON output: 0 of 3 listings say yes, 2 say no, 1 publishes no parameter list. Strict schema: 0 of 3 listings say yes, 2 say no, 1 publishes no parameter list.
Models people weigh against Llama 3.2 1B Instruct
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- 1 of 3 listings publishes no parameter list, so what its API accepts is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 2 of 3 listings do not say whether they train on prompts.
- We hold no cached-input rate for any of its listings.
- We hold no batch or off-peak rate for any of its listings.
- We hold a decode speed for it, but no prompt-processing (prefill) figure, so how long the input side of a job takes is unknown to us.
Licence and identifiers
What the licence allowsLlama 3.2 Community License, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Llama 3.2 Community License
Same terms as Llama 3.1 (700M MAU cap, naming rules). The multimodal 3.2 models add a clause restricting use by entities domiciled in the EU.
Identifiers
- Hugging Face
- meta-llama/Llama-3.2-1B-Instruct
- Takes in, gives back
- Text in, text out
- Catalogue slug
- meta-llama-llama-3-2-1b-instruct