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.
MMLU-Pro
85.3 / 100
Knowledge and reasoning across many subjects. Published accuracy; different evaluation settings can change the result.
Benchmark efficiency: 3.828 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: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: MMLU-Pro.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Hard science questions. This is a science reasoning test, not an overall intelligence score.
Benchmark efficiency: 3.778 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: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: GPQA Diamond.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Solving coding problems. Version 6 only; published pass rates can vary with generation settings.
Benchmark efficiency: 3.348 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: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: LiveCodeBench v6.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Following precise instructions. These cards report IFEval without consistently specifying prompt-level versus instruction-level scoring. Treat this view as approximate.
Benchmark efficiency: 4.124 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: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: IFEval.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Choosing and calling functions. Published BFCL v4 scores; tool environments and settings differ.
Benchmark efficiency: 3.02 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: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: BFCL-V4.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Fixing real software issues with tools. Agent setup and external tools affect these scores. File size covers model weights only.
Benchmark efficiency: 3.105 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: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: SWE-bench Verified.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Revised MMLU knowledge test. Kept separate from original MMLU and MMLU-Pro.
Benchmark efficiency: 4.187 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: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: MMLU-Redux.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
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 22.285 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 22.29 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: 2026-10-02. Apple US launch price for the base M5 Max Mac Studio, published 22 September 2026: USD 2,499. 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 22.285 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.