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 Aug 3, 2026Llama 3.2 1B Instruct is Meta's smallest instruction-tuned model, a 1.2-billion-parameter text model released in 2024 for edge deployment and cost-sensitive inference. Its hosted cost is among the lowest we track, though its academic benchmark scores sit near the floor for measured models.
Pick this when you need downloadable weights for on-device or edge deployment where 1.2 billion parameters fit your hardware constraints, or for ultra-cheap hosted inference where cost matters more than accuracy. Skip it if you need strong instruction-following, creative writing, or verified academic performance — its scores there lag even its own modest overall results, and the licence requires a separate agreement for very large commercial use.
The case for it
- Extremely low hosted inference cost, with one tracked offer roughly one-tenth the output price of the next cheapest.
- Measured at 179 tokens per second on Cloudflare Workers AI, giving a concrete throughput figure for one edge platform.
- Six Arena leaderboards measured for its size class, including coding, hard prompts and maths.
The case against it
- Academic benchmark scores are very low: under 10% on MMLU-Pro and under 2% on GPQA Diamond, near the floor for measured models.
- Instruction-following and creative writing lag even its own overall Arena score, suggesting uneven capability across task types.
- The Llama 3.2 Community License carries restrictions — commercial use above 700 million users requires a separate agreement, unlike true Apache-style open weights.
How good is it?
IntelligencePuzzles, maths, exam questions
Arena Text (overall)143rd of 143 · 1110.7
CodingWriting and fixing code on its own
Arena Coding143rd of 143 · 1148.7
Arena Coding is the only board that has scored it for this.
AgenticPlanning, calling tools, staying on task
Nobody we watch has scored Llama 3.2 1B Instruct for this. We would take the rating from Arena Agent (IPS).
WritingWe do not rate this
Two boards come close and neither tests writing: Arena Creative Writing asks people which of two replies they prefer, and LiveBench Language tests whether a model understood a passage. So we show where Llama 3.2 1B Instruct placed and give it no mark out of five.
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, which is why they get no rating.
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?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
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.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
Or rent it from someone else
Cheapest of 3 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.
- 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 AI | $0.020 / $0.020 | 131K | not measured | Unknown | Unknown | Unknown |
| OpenRouter | $0.027 / $0.20 | 60K | not measured | Unknown | Unknown | Unknown |
| Cloudflare Workers AI | $0.027 / $0.20 | 60K | 153 tok/s | No | Yesunknown period | Unknown |
OpenRouter
Cloudflare Workers AIAcross the 3 listings we hold: 1 say they do 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 rather than confirmed either way.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
- ✓
- Supported
- ✗
- Not supported
- Not published
- host gave no parameter list
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| Novita AI | |||
| OpenRouter | ✗ | ✗ | ✗ |
| Cloudflare Workers AI | ✗ | ✗ | ✗ |
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
Dates behind this page
Prices last checked 3d ago
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 publish no parameter list, so what their 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.
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
- Modality record
- text->text
- Catalogue slug
- meta-llama-llama-3-2-1b-instruct