1
62.5
ARC-ml
38.4
37.5
25.5
26.4
37.9
33.1
HellaSwag-ml
55.0
53.6
37.4
42.1
51.9
49.1
LAMBADA-ml
52.2
48.9
40.2
44.7
50.3
49.6
TruthfulQA-ml
46.3
46.7
45.4
47.2
46.0
45.0
Avg
50.3
49.5
40.5
43.3
48.3
47.5
Table 20: Pretraining Evaluation (70B-class models). Performance (%) on multilingual tasks (“-ml” = multilingual variant), higher is better. Bold = best overall; italic = best among fully open models.
Fully open
Open-weight
Benchmark
Apertus
1.0
Apertus
1.5
OLMo 3
EuroLLM
Marin
K2-V2
Qwen3.5
Nemotron
3.5
Parameters
70B
70B
32B
22B
32B
70B
27B
30B-A3B
XNLI
45.9
46.3
42.4
44.1
42.3
44.1
45.3
43.4
XCOPA
69.9
68.0
59.5
61.3
58.6
63.8
67.3
62.1
ARC-ml
39.6
40.2
34.0
35.9
29.1
34.7
43.6
39.4
HellaSwag-ml
57.6
57.1
47.2
52.9
45.2
52.4
57.3
41.8
LAMBADA-ml
54.9
53.6
48.4
55.7
48.1
50.7
56.7
52.7
TruthfulQA-ml
48.4
48.8
46.7
46.1
47.2
48.3
51.1
48.8
Avg
52.7
52.3
46.4
49.3
45.1
49.0
53.6
48.0
Table 21: Pretraining Evaluation (8B-class models). Performance (%) on bias, safety, and cultural knowledge tasks, higher is better (higher ToxiGen/BBQ = less toxic/biased). Bold = best overall; italic = best among fully open models.
Fully open
Open-weight
Benchmark
Apertus 1.0
Apertus 1.5
OLMo 3
Marin
Qwen3.5
Llama 3.1
Parameters
8B
8B
7B
8B
9B
8B
BBQ
56.7
57.0
59.4
58.4
63.0
58.6
ToxiGen
52.2
42.7
58.8
48.3
59.8
43.0
WMDP
50.2
53.2
54.5
54.9
63.7
54.3
CulturalBench
69.7
69.9
70.6
72.8
78.1
67.2
Avg
57.2
55.7
60.8
58.6
66.2
55.8
Table 22: Pretraining Evaluation (70B-class models). Performance (%) on bias, safety, and cultural knowledge tasks, higher is better (higher ToxiGen/BBQ = less toxic/biased). Bold = best overall; italic = best among fully open models.
Fully open
Open-weight
Benchmark
Apertus
1.0
Apertus
1.5
OLMo 3
EuroLLM
Marin
K2-V2
Qwen3.5
Nemotron
3.5
Parameters
70B
70B
32B
22B
32B
70B
27B
30B-A3B
BBQ
55.1
58.6
61.8
57.4
61.2
62.7
63.0
59.5
ToxiGen
44.1
47.2
61.9
39.7
50.3
43.2
58.6
45.5
WMDP
53.3
55.9
60.5
52.5
59.5
59.2
70.0
63.6
CulturalBench
69.9
72.6
74.7
59.5
77.2
75.5
83.7
64.3
Avg
55.6
58.6
64.7
52.3
62.1
60.2
68.8
58.2
5.3 Post-Trained Models
Table 23and Table 24 report the release evaluation runs of the final checkpoints against Apertus 1.0 and contemporary peers, all with thinking disabled; the thinking-mode comparison follows in Table 25. Each table splits the peers into fully open models, which release weights, data, and training recipes, and open-weight models. We evaluate our 8B model against other fully open models: OLMo 3 7B Instruct (Team Olmo et al., 2025), Marin 8B Instruct (The Marin Project, 2025), and EuroLLM 9B Instruct-2512 (Ramos et al., 2026). We also report scores for the open-weight models Qwen3.5-9B (Qwen Team, 2026b) and Llama 3.1 8B Instruct (Grattafiori et al., 2024).
We report results for Apertus 1.5 70B along with other open models above 22B parameters in size. We compare to fully open models OLMo 3.1 32B Instruct (Team Olmo et al., 2025) by AI2, K2-V2 Instruct (K2 Team et al., 2025), and EuroLLM (Ramos et al., 2026). Additionally, we report results for the open-weight models Llama 3.3 70B Instruct (Grattafiori et al., 2024), Gemma 4 31B (Gemma Team et al., 2026), Qwen 3.5 27B (Qwen Team, 2026b), and Nemotron 3.5 Lightning 30B-A3B (NVIDIA et al., 2025).
The generational step is clear: Apertus 1.5 8B improves over Apertus 1.0 8B Instruct on every reported benchmark except TruthfulQA and GPQA (Table 23). Among the fully open models of its class, the 8B leads on knowledge, instruction following, and cultural benchmarks, while OLMo 3 leads on mathematics and code; across all compared models of the class, including open-weight ones, it is the strongest on instruction following (IFEval, Multi-IF, and IFBench) and AGIEval. We report separate scores for the subsequent steps of post-training in the following Section 5.3.2.
Apertus 1.5 70B improves over its predecessor on every benchmark and outperforms EuroLLM-22B on nearly all benchmarks, but trails the strongest open-weight contemporaries on most axes (Tab