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JetBrains’ Mellum2.1 is an open-weight, 12-billion-parameter mixture-of-experts model designed for coding agents and fast sub-agents. It keeps 2.5 billion parameters active at a time, uses an Apache 2.0 license, and differs from Mellum2 chiefly through reinforcement-learning post-training that JetBrains says included work in real software repositories.
What is Mellum2.1?
Mellum2.1 is JetBrains’ update to its Mellum2 model. It is presented as a “thinking” model for complex agentic tasks—such as exploring a repository, running commands, editing files, and using tools—as well as demanding coding, math, and reasoning problems. JetBrains describes it as suitable for coding agents and fast sub-agents running on a user’s own hardware.
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The model is open-weight and listed under the Apache 2.0 license. That allows broad use under the license’s terms, but it does not mean the model is a hosted service or that running it locally requires no suitable compute.
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The architecture remains at 12 billion total parameters, with 2.5 billion active parameters. JetBrains says almost all version-specific work went into post-training, with reinforcement learning as the primary stage.
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According to JetBrains’ Mellum2.1 model card, training covered math, competitive programming, science, tool use, and software engineering. For the software-engineering work, the model operated inside real repositories using shell and file-editing tools, and received reward when tests passed. JetBrains’ release announcement describes millions of sandboxed runs across thousands of environments. These are JetBrains’ descriptions of its training process, not independently verified measurements.
Published model specifications
| Specification | Mellum2.1 Thinking |
|---|---|
| Total parameters | 12B |
| Active parameters | 2.5B |
| Layers | 28 |
| Experts | 64 total; 8 activated |
| Context length | 131,072 tokens |
| Precision | bfloat16 |
| License | Apache 2.0 |
These specifications are from the official model card. A long context window describes what the model can process under the stated specification; it does not establish how much memory a particular deployment needs.
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How Mellum2.1 performs on published benchmarks
JetBrains’ model card reports the following selected results. They are percentages, with higher scores better except for HarmBench; the four benchmarks below use the higher-is-better convention. All figures are JetBrains-reported, not independent reproductions.
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| Benchmark | Mellum2.1 Thinking | Mellum2 Thinking | Gemma 4 E4B | Qwen3.5 (9B) |
|---|---|---|---|---|
| LiveCodeBench v6 | 82.0% | 69.4% | 69.4% | 75.4% |
| SWE-bench Verified | 47.0% | 2.0% | 23.0% | 50.0% |
| Terminal-Bench 2.1 | 17.4% | 0.6% | 3.4% | 21.7% |
| BFCL v4 | 62.3% | 49.6% | 52.5% | 58.5% |
The table does not show Mellum2.1 leading on every task. It has the highest listed result on LiveCodeBench v6 and BFCL v4, while Qwen3.5 (9B) scores higher on SWE-bench Verified and Terminal-Bench 2.1. Those differences make task fit more useful than a single overall ranking.
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How JetBrains ran the comparisons
JetBrains says all models were evaluated with the same pipeline in thinking mode. Non-agentic tests used greedy decoding. Agentic tests used Pi v0.73.1 with shell and file tools, a 114,000-token context, up to 16,000 tokens per turn, and each model’s default sampling; Mellum2.1 used a temperature of 1.0. JetBrains also says Mellum2 Thinking was re-evaluated with this pipeline, so its scores differ slightly from those in its technical report. These setup details matter when comparing the numbers with results from other evaluations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you run Mellum2.1 locally?
JetBrains presents local, private, and self-hosted deployment as use cases, and the model card includes examples for serving with vLLM and SGLang. However, the official sources cited here do not specify a minimum GPU, system-memory capacity, or other minimum hardware configuration. The 2.5B active-parameter figure alone is not enough to establish a practical hardware requirement.
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At the time of the release announcement, JetBrains said GGUF builds for llama.cpp, Ollama, and LM Studio, plus an MTP head for speculative decoding in vLLM, were forthcoming. That release-time statement is not confirmation that those formats or features are available now; check the current model card and files before choosing a deployment path.
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