MetaLanguage model

Llama 3.1 70B Instruct

Llama 3.1 70B Instruct has no overall index value yet because there are too few independent benchmark results.

PopularityRank 224 of 785

43out of 100+8 vs. last month

based on search, usage and attention

Summary

Llama 3.1 70B Instruct tunes the 70-billion-parameter version of Llama 3.1 70B for multilingual dialogue. Meta combines supervised fine-tuning with reinforcement learning from human feedback.

Compared with Llama 3.1 8B Instruct, the parameter count grows while context remains at 128K tokens. Both versions support tool calls through chat templates and eight languages. The weights use the Llama 3.1 Community License, with a December 2023 knowledge cutoff.

See for yourself: one prompt, one attempt

We show the latest attempt without fixes; it does not count toward the overall index.

Pelican on a bicycle

Prompt

Generate an SVG of a pelican riding a bicycle

Oct 1, 2026 · 13 s · $0.0002

Test details

Run date: Oct 1, 2026
API provider: openrouter
Requested API model: meta-llama/llama-3.1-70b-instruct
Reasoning setting: not specified
Token limit including reasoning: 8,192 tokens
Output tokens: 603 tokens
Cost of the attempt: $0.0002
Response time: 13 s
API model reported in response: meta-llama/llama-3.1-70b-instruct
Pinned endpoint: DeepInfra | meta-llama/llama-3.1-70b-instruct
Temperature: Provider default
Test protocol: community-visual-tests-v1
Technical checks: Passed, not a quality rating

Visually reviewed. This individual test does not establish overall model quality.

Task origin: Simon Willison

SVG short test

One SVG task with the same fixed settings for every model, without hidden thinking.

Llama 3.1 70B Instruct

No independent test. This API route aliases llama-3.1-70b.

All benchmark results

Every published result with its source, test setup and comparison models.

Show all 74 published benchmark results

Measurements without matching peer values

These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.

MMLU instruct (Show measurement, test conditions, and source)

Source value: 83.6
Score: 83.6
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table; 5-shot

Source Meta Llama 3.1 model card

MMLU CoT (Show measurement, test conditions, and source)

Source value: 86
Score: 86
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table; CoT

Source Meta Llama 3.1 model card

MMLU Pro instruct (Show measurement, test conditions, and source)

Source value: 66.4
Score: 66.4
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table

Source Meta Llama 3.1 model card

IFEval (Show measurement, test conditions, and source)

Source value: 87.5
Score: 87.5
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table

Source Meta Llama 3.1 model card

ARC instruct (Show measurement, test conditions, and source)

Source value: 94.8
Score: 94.8
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table

Source Meta Llama 3.1 model card

GPQA (Show measurement, test conditions, and source)

Source value: 46.7
Score: 46.7
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table

Source Meta Llama 3.1 model card

HumanEval (Show measurement, test conditions, and source)

Source value: 80.5
Score: 80.5
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table

Source Meta Llama 3.1 model card

MBPP (Show measurement, test conditions, and source)

Source value: 86
Score: 86
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table

Source Meta Llama 3.1 model card

GSM8K (Show measurement, test conditions, and source)

Source value: 95.1
Score: 95.1
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table; CoT

Source Meta Llama 3.1 model card

MATH (Show measurement, test conditions, and source)

Source value: 68
Score: 68
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct table

Source Meta Llama 3.1 model card

API-Bank (Show measurement, test conditions, and source)

Source value: 90
Score: 90
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct tool-use table

Source Meta Llama 3.1 model card

BFCL (Show measurement, test conditions, and source)

Source value: 84.8
Score: 84.8
Metric: percent
Unit: %
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.1 model card
Status: active
Retrieved at: 2026-09-08
Methodology: Vendor-reported result. Instruct tool-use table

Source Meta Llama 3.1 model card

Page 1 of 7

Popularity compared

How often the model is searched for, used and mentioned, compared with every model of its category.

Popularity index

The 100 most popular models of this category, 0 to 100, higher is more popular

20 of 101 models

GPT-6 Astra83

Claude Haiku 5.577

GPT-6 Sol73

GPT-573

DeepSeek V4.1 Flash 101769

Kimi K367

MiniCPM-V 4.7 35B-A3B63

GPT-6.1 Sol63

Claude Opus 563

Qwen 3.8 Flash62

Llama 3.1 70B Instruct43

DeepSeek V4.1 Flash 1017

MiniCPM-V 4.7 35B-A3B

GPT-6.1 Sol

Claude Opus 5

Qwen 3.8 Flash

Llama 3.1 70B Instruct

As of November 2026 · Source: gradually.ai popularity index

Over time

Weekly values from October 18, 2026 to November 7, 2026

Llama 3.1 70B InstructDeepSeek V4 Flash Vision Exp 43Laguna XS 2.1 43Qwen 3.5 35B A3B 43Agnes 3.0 Flash 43

Method changes in the timeline, a jump at these points is no market event:

1November 1, 2026: Value only with search or usage data, 30-day attention

What is gaining popularity right now, across models, vendors, harnesses and AI subscriptions, is collected in All AI trends.

Similar models

More models of the same kind as Llama 3.1 70B Instruct, chosen by model line, developer, size, price and release date.

All specifications

Architecture

Parameters: 70 billion; Source: Parameters
Knowledge cutoff: December 2023; Source: Knowledge cutoff

Input and output

Context window: 131,072 tokens; Source: Context window
Inputs: Text; Source: Inputs
Outputs: Text; Source: Outputs
Tool calling: Yes; Source: Tool calling

Watermarking and provenance

AI watermarking refers to invisible signals embedded in the output. Visible marks and provenance metadata are listed separately. Unknown means there is insufficient evidence for that particular field.

AI watermarking: Unknown; no source
Watermarking technique: Not known; no source

Licence and access

Weights: Open weights; Source: Weights
Access: API; Source: Access
License: Llama 3.1 Community; Source: License

Prices

Not included in any subscription in Germany.

All plans in the AI subscription comparison

Frequently asked questions about Llama 3.1 70B Instruct

What is the knowledge cutoff of Llama 3.1 70B Instruct?

December 2023, according to Meta. That is 7 months before release.

Stated by Meta

DecJanFebMarAprMayJunJulKnowledge cutoffDecember 2023ReleasedJuly 2024

How large is the context window of Llama 3.1 70B Instruct?

131,072 tokens. That is how much text Llama 3.1 70B Instruct reads at once, about 200 A4 pages.

Page count as a rule of thumb from Artificial Analysis

Context windowTokens the model reads at once · higher is better

GLM-5.1200K

DeepSeek-V3 0324164K

Llama 3.1 70B InstructRank 94 of 103131K

GLM-4.5131K

GPT OSS 120B131K

DeepSeek-V3.2128K

DeepSeek-R1128K

Can I run Llama 3.1 70B Instruct locally?

Yes, the weights are open.

Stated by Meta

Open weights: yes
License: Llama 3.1 Community
Parameters: 70B
active per token: n/a

Sources and data date

Show all 22 sources

Benchmark sources at a glance

Which independent evaluators tested this model. This is coverage, not a quality ranking.

Evaluator	Test series	Composite indices	Recorded active measurements
LMArena	0	0	1

Without vendor claims and historical or disputed results; those stay labelled in the single values. 1 measurements are not yet assigned to a test series.

Type	Evidence and data date
Input	Documented modalities. Source: Hugging Face · Hugging Face (retrieved October 15, 2026; October 17, 2026) · Hugging Face, Llama 3.1 70B Instruct · Editorial description reviewed October 4, 2026
Output	Documented modalities. Source: Hugging Face · Hugging Face (retrieved October 15, 2026; October 17, 2026) · Hugging Face, Llama 3.1 70B Instruct · Editorial description reviewed October 4, 2026
Research status	Research date September 8, 2026. 16 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not published	evaluated N Original wording The leaderboard reports preference-vote totals but no fixed task-case N; Votes are not copied to sampleCount.
Unresolved	confidence interval Original wording The Score cell displays ±, but the official policy does not define that notation as a confidence interval; no CI field is populated.
Unresolved	score direction Original wording The captured source does not explicitly state higher-is-better or lower-is-better; higherIsBetter remains null.
Not published	per-model prompts and generation settings Original wording No exact prompt, decoding settings, or serving configuration is published in the captured row.
Additional source	Meta Llama 3.1 model card (retrieved September 8, 2026)
Additional source	SciCode: A Research Coding Benchmark Curated by Scientists (retrieved October 17, 2026)
Additional source	ConvCodeWorld: Benchmarking Conversational Code Generation in Reproducible Feedback Environments (retrieved October 17, 2026)
Additional source	ACTIONREASONINGBENCH: Reasoning about Actions with and without Ramification Constraints (retrieved October 17, 2026)
Additional source	Arena leaderboard (retrieved October 18, 2026)
Additional source	LongBioBench maintainers, results table (retrieved October 19, 2026)
Additional source	OpenCompass GPassK maintainers, Main Results table (retrieved October 19, 2026)
Additional source	BigCodeBench official leaderboard (retrieved October 28, 2026)1 2
Additional source	Model metadata (retrieved September 8, 2026)
Additional source	Model metadata (retrieved October 18, 2026)
Additional source	Model metadata (retrieved October 28, 2026)1 2 3 4
Additional source	OpenRouter: Popularity index (derived), CC BY 4.0
Additional source	Vercel AI Gateway: Popularity index (derived), CC BY 4.0
Additional source	Ollama: cumulative downloads for the popularity index
Additional source	Replicate: cumulative runs for the popularity index
Additional source	Google Trending Now: limited feed selection for Germany and the US
Additional source	Creative Commons Attribution 4.0