model, and we evaluate every checkpoint with thinking on and off, at a 32,768-token thinking budget. For small benchmarks with a high run-to-run variance, like AIME, we report mean@k metrics across multiple seeds (e.g., with k=8k=8 for AIME). Table 25 compares the Apertus models in both modes with thinking-native peers in their thinking mode. We compare to OLMo 3 7B Think (Team Olmo et al., 2025), OLMo 3.1 32B Think (Team Olmo et al., 2025), and K2 Think V2 (K2 Think Team et al., 2026). The improvement concentrates where reasoning is long: on the 8B, MATH-500 improves from 51.2 to 71.4, MATH from 41.2 to 57.3, and IFBench from 35.3 to 44.3, the strongest IFBench score among the compared 8B-class models; multimodal reasoning gains double digits, roughly 10 points on MMMU. Knowledge benchmarks move less, and the 70B even cedes GPQA under thinking.
For AIME 24/25, while still behind other peer models, Apertus has improved dramatically since version 1.0, when the models were unable to solve a substantial number of tasks. Thinking further pushes Apertus 1.5 8B from 7.5 to 22.9 on AIME 24, and from 8.3 to 23.8 on AIME 25.
Table 25: Thinking-mode comparison on reasoning-heavy benchmarks: the Apertus 1.5 models with thinking off and on, and thinking-native peers in their thinking mode. All values in percent, best per row in bold. We report mean@8 for AIME, and mean@4 for other math and coding benchmarks.
Apertus 1.5
8B
Apertus 1.5
70B
Thinking-native peers
Benchmark
Off
On
Off
On
OLMo 3 7B
OLMo 3.1 32B
K2 Think V2 70B
Knowledge and Reasoning
MMLU
70.5
74.5
76.5
77.1
74.0
82.7
88.0
GPQA
27.2
32.1
32.1
30.1
27.2
39.7
59.6
Instruction Following
IFBench
35.3
44.3
25.7
32.7
20.0
23.0
36.7
Mathematics
GSM8K
78.5
85.7
85.0
86.4
91.9
96.7
89.8
MATH
41.2
57.3
52.9
59.0
74.0
74.9
75.2
MATH-500
51.2
71.4
64.0
73.6
86.2
88.2
90.2
AIME 24
7.5
22.9
15.0
27.9
74.2
80.0
84.6
AIME 25
8.3
23.8
7.0
21.7
69.1
78.8
74.6
5.3.2 Post-Training Lineage
Our post-training pipeline runs in four stages: supervised finetuning, including its long-context stage (Section 4.2 and Section 4.3), reinforcement learning (Section 4.5), preference alignment with DPO (Section 4.6), and charter alignment with SDPO (Section 4.6.2). Table 26 follows both models through that sequence, each column adding one stage to the checkpoint of the column before it, so the difference between adjacent columns isolates what the intervening stage contributes. We split the suite in two: the development benchmarks, which guide our own iteration, and the held-out benchmarks of Table 27, which we never consult when selecting checkpoints and which therefore measure how far the gains generalize. Table 28 repeats the exercise with thinking enabled, and Section L.1 gives all 37 development benchmarks. In these tables, including Table L.3, GPT-4.1 (gpt-4.1-2025-04-14) judges AlpacaEval and Arena-Hard v0.1, and o3-mini (o3-mini-2025-01-31) judges Arena-Hard v2.
Every stage adds to the overall average, which rises from 47.3% to 55.0% for Apertus 1.5 8B and from 52.2% to 61.2% for Apertus 1.5 70B, and the held-out averages move with them, from 48.2% to 51.7% and from 53.2% to 56.4%. The stages divide the labor. Reinforcement learning does most of the work on reasoning and instruction following, lifting ACPBench by 5.1 and 11.4 points at the two scales and IFEval by 5.6 and 7.3. Preference alignment then takes the conversational and agentic axes: AlpacaEval gains 12.1 points at 8B and 33.5 at 70B, Arena-Hard v0.1 gains 13.9 and 38.8, and tool use gains 13.1 and 12.5. It also carries most of the safety movement, with BBQ rising 12.4 points at 70B and ToxiGen 8.6 at 8B. SDPO is deliberately narrow, moving charter alignment by 9.9 points and AlpacaEval by 10.0, and leaving the remaining benchmarks within a few points.
Two effects run against the trend. Reinforcement learning shortens responses at 70B scale, from 367 to 253 tokens on average over the three chat benchmarks, and offline DPO raises them to 495, t