Llama 3.1 8B Instruct
Meta · released Jul 18, 2024 · meta-llama/Meta-Llama-3.1-8B-Instruct
- Type
- Open weightsLlama 3.1 Community License
- Params
- 8B
- Context
- 131K
about 98K words of context · download allowed, licence restricts use
Our take
Written Aug 3, 2026Llama 3.1 is a compact downloadable model from Meta with a 131,072-token request limit and a licence that restricts large-scale commercial use. It is one of the cheapest hosted options we track for basic text tasks, though its measured reasoning and knowledge scores lag well behind larger models.
Pick this for low-cost hosted inference on basic text tasks, or when you need the fastest measured throughput and can accept a higher per-token rate. It also suits local deployment where the licence terms are acceptable. Skip it if you need strong graduate-level reasoning, broad general knowledge, or if your company has over 700 million users and cannot secure a separate Meta agreement.
The case for it
- Extremely cheap hosted inference available, with the lowest tracked input rate in its class.
- Fastest measured throughput among listed endpoints at 110 tokens per second.
- 131,072-token request limit, unusually broad for an 8-billion-parameter model.
The case against it
- Very weak on graduate-level reasoning, with a GPQA Diamond score of 2.5%.
- Weak general knowledge versus larger models, at 25.1% on MMLU-Pro.
- Licence restricts large-scale commercial use and instruction-following quality is inconsistent, with its Arena Instruction Following score 68.6 points below its own coding score.
How good is it?
IntelligencePuzzles, maths, exam questions
Arena Text (overall)141st of 143 · 1211.3
CodingWriting and fixing code on its own
Arena Coding141st of 143 · 1259.6
Arena Coding is the only board that has scored it for this.
AgenticPlanning, calling tools, staying on task
Nobody we watch has scored Llama 3.1 8B 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 3.1 8B 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. 16.3 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 24.3 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU) · 16 GB
Room to spare. 5.5 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 7 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
- $0.050 in / $0.080 out
- Context served
- 131K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| DeepInfrafp8 | $0.020 / $0.040 | 131K | 16 tok/s | No | No | Confirmed |
| Novita AIfp8 | $0.020 / $0.050 | 16K | 54 tok/s | No | No | Confirmed |
| Novita AI | $0.020 / $0.050 | 16K | not measured | Unknown | Unknown | Unknown |
| OpenRouter | $0.050 / $0.080 | 131K | not measured | Unknown | Unknown | Unknown |
| Groq | $0.050 / $0.080 | 131K | 306 tok/s | No | No | Confirmed |
| CoreWeavebf16 | $0.22 / $0.22 | 128K | 119 tok/s | No | No | Confirmed |
| Cloudflare Workers AIfp8 | $0.15 / $0.29 | 32K | 19 tok/s | No | Yesunknown period | Unknown |
DeepInfra
Novita AI
Groq
Cloudflare Workers AIAcross the 7 listings we hold: 5 say they do not train on prompts, 0 say they do and 2 do not say. 4 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 |
|---|---|---|---|
| DeepInfrafp8 | ✗ | ✓ | ✗ |
| Novita AIfp8 | ✗ | ✓ | ✗ |
| Novita AI | |||
| OpenRouter | ✓ | ✓ | ✓ |
| Groq | ✓ | ✓ | ✗ |
| CoreWeavebf16 | ✓ | ✓ | ✓ |
| Cloudflare Workers AIfp8 | ✗ | ✓ | ✗ |
Tool calling: 3 of 7 listings say yes, 3 say no, 1 publishes no parameter list. JSON output: 6 of 7 listings say yes, 1 publishes no parameter list. Strict schema: 2 of 7 listings say yes, 4 say no, 1 publishes no parameter list.
Models people weigh against Llama 3.1 8B Instruct
When we formed this view
Dates behind this page
Prices last checked 5d 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 7 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 7 listings do not say whether they train on prompts.
Licence and identifiers
What the licence allowsLlama 3.1 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 3.1 Community License
Commercial use below 700M MAU. Notably allows using outputs to improve other models, which earlier Llama licenses banned. Derivative names must start with "Llama".
Identifiers
- Hugging Face
- meta-llama/Meta-Llama-3.1-8B-Instruct
- Modality record
- text->text
- Catalogue slug
- meta-llama-llama-3-1-8b-instruct