Mellum2.1 12B A2.5B Thinking has 12.15 billion total parameters. The selected Q4_K_M GGUF file is 8.071 GB.
Data snapshot: . 4 published tests in this catalog.
Model file and details
Provider
JetBrains
Total parameters
12,149,923,072
Exact file size
8,071,295,264 bytes
Quantization / precision
Q4_K_M
Weight repository
JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF
Original model repository
JetBrains/Mellum2.1-12B-A2.5B-Thinking
Weight revision
20b439426f1e997ba575eaccaf4856ac0f76f5d5
Model revision
92ddae9fc7665e9f801d141d2e5a6b2caf2460c4
Runtime notes
Confirm model architecture and quantization support in your runner.
Recorded release date
2026-10-08
Date evidence
JetBrains announced Mellum2.1 on 8 October 2026 after updating the official source checkpoint on 7 October; the official Apache-2.0 GGUF repository and complete Q4_K_M artifact were also created on 7 October 2026.
These scores were compiled from the linked sources. These published scores were not independently tested here. Different prompts, precision, thinking budgets and tools can change results.
LiveCodeBench v6
82 / 100
Solving coding problems. Version 6 only; published pass rates can vary with generation settings.
Benchmark efficiency: 10.159 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: JetBrains publisher evaluation of the official BF16 source checkpoint on LiveCodeBench v6 in thinking mode with greedy decoding.
Publisher-run source-checkpoint pass@1, not an independent result and not a benchmark of the selected Q4_K_M file. JetBrains states that all comparison models used the same internal evaluation pipeline, but the card does not pin that pipeline's code revision. The 8.071 GB file excludes runtime, KV cache and context memory and does not establish device speed or compatibility.
Choosing and calling functions. Published BFCL v4 scores; tool environments and settings differ.
Benchmark efficiency: 7.719 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: JetBrains publisher evaluation of the official BF16 source checkpoint in thinking mode. The reported BFCL v4 value is the macro-average of the v1, v2, v3, web-search and memory subtasks.
Publisher-run source-checkpoint result, not an independent evaluation and not a benchmark of the selected Q4_K_M file. Tool schemas, parser behavior and runtime configuration can change function-calling results.
Fixing real software issues with tools. Agent setup and external tools affect these scores. File size covers model weights only.
Benchmark efficiency: 5.823 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: JetBrains publisher evaluation of the official BF16 source checkpoint on SWE-bench Verified with Pi v0.73.1, shell and file tools, Mellum2.1's default temperature 1.0, a 114K-token context and up to 16K generated tokens per turn.
Publisher-run source-checkpoint resolved rate, not an independent result and not a benchmark of the selected Q4_K_M file. Agent scaffold, tools, timeouts, sampling and context budget materially affect this score. The Q4 file size alone does not establish compatible hardware or local-agent speed.
Agentic terminal-use task success. Publisher-reported percentages are retained, but agent scaffold, tools, timeouts and reasoning budget can materially affect results.
Benchmark efficiency: 2.156 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: JetBrains publisher evaluation of the official BF16 source checkpoint on Terminal-Bench 2.1 with Pi v0.73.1, shell and file tools, Mellum2.1's default temperature 1.0, a 114K-token context and up to 16K generated tokens per turn.
Publisher-run source-checkpoint resolved rate, not an independent result and not a benchmark of the selected Q4_K_M file. The published percentage is specific to the stated agent harness and token budget; no local Q4 speed result is claimed.
Context and version evidence: The repository and file name identify the selected model. Revision hashes are shown above when recorded. Tested context length, full hardware configuration and run date are not consistently recorded in this snapshot. Only settings explicitly stated in each benchmark record are known.
The 8.071 GB figure covers model weights in this file. It excludes the context cache, runtime, operating system and any separate multimodal files. It is not a measured RAM requirement or a token-generation speed.
These complete configurations pass the initial weight-size check for the selected 8.07 GB file. Allow extra memory for context and software. Check the model’s runtime notes and your inference software before buying.
Listing evidence: 2026-09-25. Amazon.com base price visible in an owner-supplied screenshot reviewed 25 Sep 2026; capture time not supplied. Dated reference, not a live quote. Excludes card signup offers, coupons, tax and shipping.
Listing evidence: 2026-10-03. Apple US launch price announced 22 September 2026: USD 1,699. Dated manufacturer reference, not an Amazon offer; tax and shipping may differ.
Listing evidence: 2026-09-24. GMKtec, EVO-X2 / 64GB / 1TB, recorded 22 Sep 2026; exact ASIN capacity identified 24 Sep. This is a dated reference, not a current Amazon price; tax, shipping and bundle contents may differ.
Memory matches are estimates, not tested compatibility or speed. As an Amazon Associate I earn from qualifying purchases.
Confirm model architecture and quantization support in your runner. The selected weights use 8.071 GB; this excludes context cache, runtime buffers and other programs.
These are the first listed tiers that pass our reserve policy, not measured minimum requirements. System RAM and VRAM remain separate. Use the memory tool to change the reserve for your context and runner.