JetBrains shipped Mellum2.1 on October 8. The interesting part: almost nothing about the model itself changed. Architecture, parameter count, and license all carry over from Mellum2, open-sourced in June — still a 12B sparse mixture-of-experts with 2.5B active parameters per token under Apache 2.0. What got replaced is everything after pre-training: one summer, all in on reinforcement learning.

RL went from final stage to the main course

The official blog breaks the update into three moves. First, RL's role changed: in Mellum2 it was a short final stage after pre-training; here it became the main part of training. Second, more data, filtered harder: new RL tasks span math, competitive programming, science, tool use, and software engineering, mixing open RL datasets with tasks JetBrains built themselves — the team notes open data often ships with broken tests, unverifiable answers, or tasks too easy or impossible for the model, so every source was filtered before training. Third, for agentic skills they built infrastructure to run thousands of RL environments in-house, launching millions of sandboxes over the course of training.

The result, per the official post: Mellum2 was fast but couldn't work inside a repository at the level the team wanted; Mellum2.1 explores a codebase, edits files, and checks its own changes — from identifying the root cause of a failing test to drafting and checking a fix.

Benchmarks and speed: two cards for a small model

JetBrains ran Mellum2.1 against Mellum2, Qwen3.5-9B, and Gemma 4 E4B under the same evaluation setup. The official claim: the biggest gain lands in agentic coding, with improvements across coding, competitive programming, math, tool calling, and general knowledge, holding up on both hard and everyday problems.

Speed is the model's traditional strength. With the architecture untouched, Mellum2.1 is as fast as its predecessor, and multi-token prediction (MTP) pushes further: under heavy load it is the fastest in the tested group, serving almost twice as many tokens as Qwen3.5-9B on a single H200; for a single request, MTP gives roughly a 1.6x speedup.

A tool vendor's way of building models

What's worth chewing on is JetBrains' position. It's not a model company; it's an IDE company that watches real developers work in real repositories every day. Mellum2.1's target scenarios are spelled out: a capable worker inside agentic systems, a fast sub-agent, running on your own hardware so code never leaves the network. Weights are on Hugging Face; GGUF builds for llama.cpp, Ollama, and LM Studio plus an MTP head for speculative decoding in vLLM are "coming soon," per the official post.

Look back at the June Mellum 2 release and the narrative was "a fast model plus focal-model rerouting of production pipelines"; four months later, 2.1 shifts the center of gravity to agent skills. That tracks with the industry: pure-completion small models are a harder story to tell, and "can act inside an environment" is the new bar — even at 12B.

For small teams the implication is direct: an Apache-2.0, single-GPU agent worker with roughly double the throughput of same-class models pushes private deployment costs to a new low. Frontier models compete on ceiling; small models compete on unit cost — Mellum2.1 picked the second lane, and this time it learned to use tools too.

Reference: JetBrains blog, https://blog.jetbrains.com/ai/2026/10/mellum2-1-gets-to-work-a-fast-open-model-for-coding-agents/