Swiss AI InitiativeLanguage model

Apertus 8B

Apertus 8B has no overall index value yet because there are too few independent benchmark results.

PopularityRank 136 of 785

48out of 100+1 vs. last month

based on search, usage and attention

Summary

Apertus 8B is the smaller instruction checkpoint in Swiss AI’s fully open family. Like Apertus 70B, it is described as using 15 trillion pretraining tokens, openly published training resources, and 1,811 natively supported languages. The checkpoint has 8 billion parameters and a 65,536-token context.

Apertus 8B uses xIELU activations and was trained from scratch with the AdEMAMix optimizer. Post-training combines SFT with QRPO.

All benchmark results

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

Show all 58 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.

CritPt v2: Overall (Show measurement, test conditions, and source)

Source value: 0
Score: 0
Metric: pass@1 accuracy
Unit: %
Benchmark version: v2
Category: reasoning
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: disputed
Retrieved at: 2026-11-02
Methodology: Exact AA configuration: Apertus 8B Instruct (release apertus-8b-instruct, row apertus-8b-instruct, UUID 891bcdf2-8dd2-4dc3-829b-d963fde25876, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. The full CritPt v2 run covers 70 questions with five repeats. The official grading server reports pass@1.. Source model ID: artificial-analysis/891bcdf2-8dd2-4dc3-829b-d963fde25876/apertus-8b-instruct/apertus-8b-instruct. Source model name: Apertus 8B. Provider reported by source: Swiss AI Initiative. Model snapshot: Apertus 8B Instruct. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Python functions, symbolic expressions, and numerical answers; official grading server. Attempts: 70 questions, five repeats per question, pass@1. Reasoning: non-reasoning. Reasoning effort: none. Score display: 0.0%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-02-67d46be20a5b. Source payload hash: 67d46be20a5b412db6cfb60ad190441fcaa5513500b8fd47d033168d0e97b6b2. Parser version: artificial-analysis-rsc-models-v3. Imported at: 2026-11-02. Source cohort: artificial-analysis-critpt-v2:v2:standalone:non-reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Apertus 8B Instruct (release apertus-8b-instruct, row apertus-8b-instruct, UUID 891bcdf2-8dd2-4dc3-829b-d963fde25876, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. The full CritPt v2 run covers 70 questions with five repeats. The official grading server reports pass@1.. Tool policy: Python functions, symbolic expressions, and numerical answers; official grading server. Attempts: 70 questions, five repeats per question, pass@1. Reasoning: non-reasoning. Reasoning effort: none
Context: This AA source field contains the original CritPt score. The earlier CritPt v2 attribution was incorrect; the value and former label remain as disputed historical evidence.. The source dataset marks this result as disputed. It remains visible as documented evidence, but is not reliable comparative proof.

Source Artificial Analysis

IFBench: Overall (Show measurement, test conditions, and source)

Source value: 22.3809523809524
Score: 22.3809523809524
Metric: IFBench prompt-level loose accuracy (pass@1)
Unit: %
Benchmark version: AA test set: 294 questions
Category: general
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: superseded
Retrieved at: 2026-11-02
Methodology: Exact AA configuration: Apertus 8B Instruct (release apertus-8b-instruct, row apertus-8b-instruct, UUID 891bcdf2-8dd2-4dc3-829b-d963fde25876, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Source model ID: artificial-analysis/891bcdf2-8dd2-4dc3-829b-d963fde25876/apertus-8b-instruct/apertus-8b-instruct. Source model name: Apertus 8B. Provider reported by source: Swiss AI Initiative. Model snapshot: Apertus 8B Instruct. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Reasoning effort: none. Score display: 22.4%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-02-67d46be20a5b. Source payload hash: 67d46be20a5b412db6cfb60ad190441fcaa5513500b8fd47d033168d0e97b6b2. Parser version: artificial-analysis-rsc-models-v3. Imported at: 2026-11-02. Source cohort: artificial-analysis-ifbench:AA test set: 294 questions:standalone:non-reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Apertus 8B Instruct (release apertus-8b-instruct, row apertus-8b-instruct, UUID 891bcdf2-8dd2-4dc3-829b-d963fde25876, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Reasoning effort: none
Context: The often cited 58 refers to verifiable out-of-domain constraint types, not the number of questions in AA's test run.. This result is retained as historical evidence and is not interchangeable with current results.

Source Artificial Analysis

IFBench: Overall (Show measurement, test conditions, and source)

Source value: 22.3809523809524
Score: 22.3809523809524
Metric: IFBench prompt-level loose accuracy (pass@1)
Unit: %
Benchmark version: AA test set: 294 questions
Category: general
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: active
Retrieved at: 2026-11-07
Methodology: Exact AA configuration: Apertus 8B Instruct (release apertus-8b-instruct, row apertus-8b-instruct, UUID 891bcdf2-8dd2-4dc3-829b-d963fde25876, reasoning=false, effort=not specified, suffix=not specified). This source capture does not provide an average reasoning-token count. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Source model ID: artificial-analysis/891bcdf2-8dd2-4dc3-829b-d963fde25876/apertus-8b-instruct/apertus-8b-instruct. Source model name: Apertus 8B. Provider reported by source: Swiss AI Initiative. Model snapshot: Apertus 8B Instruct. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Reasoning effort: none. Score display: 22.4%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-07-b96e3ffc8c5b. Source payload hash: b96e3ffc8c5b052aa56f96d05e89d02b8cc234a75085820fca0ca2a39e498274. Parser version: artificial-analysis-rsc-models-v4. Imported at: 2026-11-07. Source cohort: artificial-analysis-ifbench:AA test set: 294 questions:standalone:non-reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Apertus 8B Instruct (release apertus-8b-instruct, row apertus-8b-instruct, UUID 891bcdf2-8dd2-4dc3-829b-d963fde25876, reasoning=false, effort=not specified, suffix=not specified). This source capture does not provide an average reasoning-token count. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Reasoning effort: none
Context: The often cited 58 refers to verifiable out-of-domain constraint types, not the number of questions in AA's test run.

Source Artificial Analysis

MMLU (Show measurement, test conditions, and source)

Source value: 60.9
Score: 60.9
Metric: Exact Match
Unit: %
Category: knowledge
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 17
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: mmlu_flan_cot_zeroshot, MCQ generation, CoT, 0-shot, chat template, no multi-turn, one language.

Source Apertus v1 Technical Report v2, Table 17

Global-MMLU (Show measurement, test conditions, and source)

Source value: 55.7
Score: 55.7
Metric: Exact Match
Unit: %
Category: multilingual
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 17
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: global_mmlu_gen_0shot, MCQ generation, no CoT, 0-shot, chat template, no multi-turn, 15 languages.

Source Apertus v1 Technical Report v2, Table 17

TruthfulQA (Show measurement, test conditions, and source)

Source value: 56.7
Score: 56.7
Metric: Accuracy
Unit: %
Category: knowledge
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 17
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: truthfulqa_mc2, MCQ likelihood, no CoT, 6-shot, chat template, no multi-turn, one language.

Source Apertus v1 Technical Report v2, Table 17

TruthfulQA Multilingual (Show measurement, test conditions, and source)

Source value: 52.4
Score: 52.4
Metric: Accuracy
Unit: %
Category: multilingual
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 17
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: truthfulqa_multilingual_mc2, MCQ likelihood, no CoT, 5-shot, chat template, no multi-turn, 31 languages.

Source Apertus v1 Technical Report v2, Table 17

HellaSwag (Show measurement, test conditions, and source)

Source value: 74.6
Score: 74.6
Metric: Normalized Accuracy
Unit: %
Category: knowledge
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 17
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: hellaswag, MCQ likelihood, no CoT, 0-shot, chat template, no multi-turn, one language.

Source Apertus v1 Technical Report v2, Table 17

HellaSwag Multilingual (Show measurement, test conditions, and source)

Source value: 52.7
Score: 52.7
Metric: Normalized Accuracy
Unit: %
Category: multilingual
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 17
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: hellaswag_multilingual, MCQ likelihood, no CoT, 0-shot, chat template, no multi-turn, 31 languages.

Source Apertus v1 Technical Report v2, Table 17

BBH (Show measurement, test conditions, and source)

Source value: 55.9
Score: 55.9
Metric: Exact Match
Unit: %
Category: reasoning
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 19
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: bbh, generation, CoT, 3-shot, chat template and multi-turn demonstrations, one language.

Source Apertus v1 Technical Report v2, Table 19

DROP (Show measurement, test conditions, and source)

Source value: 49.7
Score: 49.7
Metric: F1
Unit: %
Category: reasoning
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 19
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: drop, generation, no CoT, 3-shot, chat template and multi-turn demonstrations, one language.

Source Apertus v1 Technical Report v2, Table 19

ACP-Bench Bool (Show measurement, test conditions, and source)

Source value: 58.4
Score: 58.4
Metric: Exact Match
Unit: %
Category: reasoning
Direction: Higher is better
Source type: vendor-reported
Evaluator: Apertus v1 Technical Report v2, Table 19
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Apertus-8B-Instruct row. Table 22: acp_bench_bool, MCQ generation, CoT, 2-shot, chat template and multi-turn demonstrations, one language.

Source Apertus v1 Technical Report v2, Table 19

Page 1 of 5

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 100 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

Apertus 8B48

DeepSeek V4.1 Flash 1017

MiniCPM-V 4.7 35B-A3B

GPT-6.1 Sol

Claude Opus 5

Qwen 3.8 Flash

Apertus 8B

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

Over time

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

Apertus 8BGLM-5.3-FlashX 48Kolibri 1 48Qwen 3 30B A3B Instruct 2507 48DeepSeek-V3.2 48

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 Apertus 8B, chosen by model line, developer, size, price and release date.

All specifications

Architecture

Parameters: 8 billion; Source: Parameters
Knowledge cutoff: September 2025; Source: Knowledge cutoff

Input and output

Context window: 65,536 tokens; Source: Context window
Maximum output: 8,192 tokens; Source: Maximum output
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
License: Apache 2.0; Source: License

Frequently asked questions about Apertus 8B

What is the knowledge cutoff of Apertus 8B?

September 2025, according to Swiss AI Initiative. That is the month of release.

Stated by Swiss AI Initiative

SepKnowledge cutoffSeptember 2025ReleasedSep 2, 2025

How large is the context window of Apertus 8B?

65,536 tokens. That is how much text Apertus 8B reads at once, about 100 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

GLM-4.5131K

GPT OSS 120B131K

DeepSeek-V3.2128K

DeepSeek-R1128K

Apertus 8BRank 103 of 10366K

Can I run Apertus 8B locally?

Yes, the weights are open.

Stated by Swiss AI Initiative

Open weights: yes
License: Apache 2.0
Parameters: 8B
active per token: n/a

Sources and data date

Show all 13 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
Artificial Analysis	1	0	1

Without vendor claims and historical or disputed results; those stay labelled in the single values.

Type	Evidence and data date
Input	Documented modalities. Retrieved October 27, 2026. Source: models.dev catalog · models.dev catalog (retrieved October 27, 2026)
Output	Documented modalities. Retrieved October 27, 2026. Source: models.dev catalog · models.dev catalog (retrieved October 27, 2026)
Research status	Research date October 15, 2026. 3 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not published	evaluation-configuration Note The card lists exact model rows and tasks but not score units, prompts, few-shot settings, training/evaluation mode, or run dates.
Not found in the inspected sources	excluded-checkpoint-observations Note The six rows match Table 14 of the Apertus v1 technical report's pretraining evaluation. They describe pretrained Apertus-8B/70B checkpoints, not the separately post-trained Instruct profiles.
Not published	sample-count-and-confidence-intervals Original wording Die Tabellen veröffentlichen für diese Scorezellen keine ausgewerteten Fallzahlen oder Konfidenzintervalle. Korpusgrößen, Shot-Angaben und Sprachzahlen werden nicht als Ergebnis-N umgedeutet.
Not published	exact-decoding-and-run-provenance Original wording Tabelle 22 nennt für die meisten Evaluationen Benchmark-Metrik, Tasktyp, CoT, Shots, Chat-Template, Mehrturn- und Sprachumfang, aber keine vollständigen Harness-/Prompt-/Decoding-Versionen oder konkreten Lauftermine. Tabellen 18, 23 und 26 nennen außerdem keine Score-Einheit.
Additional source	Hugging Face (retrieved October 15, 2026; October 17, 2026) · Apertus 8B model card · Editorial description reviewed October 4, 2026
Additional source	Artificial Analysis (retrieved November 2, 2026)
Additional source	Artificial Analysis (retrieved November 2, 2026; November 7, 2026)
Additional source	Apertus v1 Technical Report v2, Table 17 (retrieved October 17, 2026)
Additional source	Artificial Analysis LLM Performance Leaderboard (retrieved October 23, 2026)
Additional source	Apertus technical report · Editorial description reviewed October 4, 2026
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