# Mellum2 12B A2.5B Thinking

URL: https://interfaze.ai/models/jetbrainsmellum2-12b-a25b-thinking

Mellum2 12B A2.5B Thinking by JetBrains, a text-generation model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | Mellum2 12B A2.5B Thinking | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 131.1K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Mellum2 12B A2.5B Thinking | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Thinking)

> \[!Note\] Use this model when you want explicit chain-of-thought before the final answer — complex debugging, multi-step planning, agentic workflows, and math- or reasoning-heavy tasks. For direct, low-latency answers without reasoning traces, use [Instruct](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Instruct) instead.

## Mellum2 Thinking Highlights

Mellum 2 Thinking is a post-trained reasoning-augmented assistant model trained by JetBrains.

The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens.

It is produced from [`Mellum2-12B-A2.5B-Base`](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Base) by supervised fine-tuning (loss computed only on the final assistant turn) followed by reinforcement learning with verifiable rewards (RLVR) on a harder data mix that includes a long-form math subset. The model emits its reasoning inside `<think>...</think>` blocks before the final answer.

## Mellum2 Model Family

This repository contains one checkpoint from the Mellum 2 family.

| Checkpoint | Description |
| --- | --- |
| Base Pretrain | Base checkpoint before long-context extension |
| Base | Final base model |
| Instruct SFT | Supervised instruction-tuned checkpoint |
| Thinking SFT | Supervised thinking checkpoint |
| Instruct | RL-tuned instruction model |
| Thinking | RL-tuned thinking model |

## Model Overview

**Mellum2 Thinking** has the following features:

-   Number of Layers: 28
-   Hidden Size: 2304
-   Intermediate Size: 7168
-   MoE Intermediate Size: 896
-   Number of Experts: 64
-   Number of Activated Experts: 8
-   Number of Attention Heads (GQA): 32 for Q and 4 for KV
-   Context Length: 131,072
-   Sliding Window: 1,024
-   Vocabulary Size: 98,304
-   Precision: bfloat16
-   License: Apache 2.0

## Serving with vLLM

```
vllm serve JetBrains/Mellum2-12B-A2.5B-Thinking \
  --max-model-len 131072 \
  --reasoning-parser qwen3


vllm serve JetBrains/Mellum2-12B-A2.5B-Thinking \
  --max-model-len 131072 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes
```

## Quickstart

Text-Only Input

```
from openai import OpenAI

client = OpenAI()

messages = [
    {"role": "user", "content": "Is 1024 a power of 2? Explain your reasoning."},
]

chat_response = client.chat.completions.create(
    model="JetBrains/Mellum2-12B-A2.5B-Thinking",
    messages=messages,
    max_tokens=81920,
    temperature=0.6,
    top_p=0.95,
    extra_body={
        "top_k": 20,
    },
)
print("Chat response:", chat_response)
```

## Evaluation

Post-training evaluation for the thinking/reasoning variants. All values are percentages; higher is better except HarmBench, where lower is better. All values self-reported by JetBrains.

| Benchmark | Mellum2 Thinking SFT | Mellum2 Thinking | Qwen3.5 (4B) | Qwen3.5 (9B) | OLMo-3 (7B) | Ministral 3 (14B) |
| --- | --- | --- | --- | --- | --- | --- |
| Coding |  |  |  |  |  |  |
| LiveCodeBench v6 | 75.1 | 69.9 | 59.4 | 68.3 | 59.8 | 42.7 |
| Tool Use |  |  |  |  |  |  |
| BFCL v4 | 38.8 | 45.6 | 42.9 | 42.7 | — | 35.9 |
| BFCL v3 | 60.5 | 69.4 | 73.9 | 68.5 | — | 52.2 |
| Math |  |  |  |  |  |  |
| AIME | 20.0 | 58.4 | 68.3 | 73.4 | 61.7 | 38.3 |
| GSM-Plus | 62.6 | 87.0 | 89.3 | 90.7 | 88.1 | 86.5 |
| Knowledge |  |  |  |  |  |  |
| MMLU-Redux | 84.8 | 86.2 | 88.3 | 91.7 | 71.3 | 84.4 |
| GPQA Diamond | 39.9 | 57.6 | 76.8 | 81.3 | 29.3 | 46.0 |
| Conversational |  |  |  |  |  |  |
| IFEval | 69.1 | 76.5 | 87.1 | 89.8 | 84.7 | 59.7 |
| JetBrains pairwise | 64.4 | 69.5 | 40.5 | 56.7 | 32.2 | 63.8 |
| MixEval | 63.4 | 66.9 | 71.9 | 76.0 | 67.0 | 70.8 |
| BS-Bench | 14.0 | 15.0 | 63.0 | 70.0 | 23.0 | 9.0 |
| Safety |  |  |  |  |  |  |
| HarmBench (↓) | 12.2 | 20.6 | 15.9 | 6.6 | 48.7 | 70.0 |
| XSTest | 90.8 | 89.6 | 96.8 | 97.6 | 93.2 | 96.8 |

Notes:

-   **AIME** is the mean of AIME 2025 and AIME 2026 (30 questions each).
-   **BFCL v4** is the macro-average of five subtasks: v1, v2, v3, web search, memory.
-   **JetBrains pairwise** is win rate against `Qwen2.5-7B-Instruct` on an internal benchmark.
-   `—` indicates the model lacks native tool calling (OLMo-3-7B-Thinking).

For more details, see the [Mellum2 Technical Report](https://arxiv.org/abs/2605.31268).

## License

Released under the Apache 2.0 license.

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)