Mellum2.1-Thinking

[!Note] Mellum2.1 is a thinking model. Use it for complex agentic tasks, such as working in a repository, running commands, and calling tools, and for hard non-agentic problems in coding, math, and reasoning.

Mellum2.1 Highlights

Mellum2.1 is the next version of Mellum2 Thinking. The architecture is unchanged (a 12B mixture-of-experts model with 2.5B active parameters), and almost all of the work for this version went into post-training, primarily reinforcement learning (RL):

• Reinforcement learning at a new scale: RL went from a short final stage to the main part of training, after many experiments on both the methods and the data.
• More data, filtered harder: new RL tasks in math, competitive programming, science, tool use, and software engineering, combining open RL datasets with tasks we built ourselves and filtering every source before training.
• Real environments for agentic skills: for software engineering, the model trains inside real repositories with a shell and file-editing tools and is rewarded when the tests pass.

As a result, Mellum2.1 handles agentic tasks much better than Mellum2. After millions of sandboxed runs in real environments during training, it explores a codebase, edits files, and checks its own changes.

Model Overview

Mellum2.1 Thinking has the following features:

• Number of Parameters: 12B total, 2.5B active
• 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 (3 of every 4 layers)
• Vocabulary Size: 98,304
• Precision: bfloat16
• License: Apache 2.0

Serving with vLLM

# Without tool calling
vllm serve JetBrains/Mellum2.1-12B-A2.5B-Thinking \
  --max-model-len 131072 \
  --reasoning-parser qwen3

# With tool calling
vllm serve JetBrains/Mellum2.1-12B-A2.5B-Thinking \
  --max-model-len 131072 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

GGUF builds for llama.cpp, Ollama, and LM Studio, as well as the multi-token prediction (MTP) head for speculative decoding in vLLM, are coming soon.

Quickstart

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {"role": "user", "content": "Find the bug in this function and explain the fix: def mean(xs): return sum(xs) / len(xs) - 1"},
]

chat_response = client.chat.completions.create(
    model="JetBrains/Mellum2.1-12B-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

All values are percentages; higher is better except HarmBench, where lower is better. All models were evaluated by JetBrains with the same pipeline in thinking mode. All values are self-reported by JetBrains.

Notes:

• Non-agentic benchmarks use greedy decoding.
• Agentic benchmarks use the same open-source agent harness (Pi v0.73.1, shell and file tools) for every model, with each model's default sampling (temperature 1.0 for Mellum2.1), a 114K-token context, and up to 16K tokens per turn.
• 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.
• Mellum2 Thinking was re-evaluated with this pipeline, so its numbers differ slightly from the Mellum2 Technical Report.

License

Released under the Apache 2.0 license.