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.
HumanEval
96.3 / 100
Writing short Python functions. Base HumanEval pass@1, separate from HumanEval+ and small subsets.
Benchmark efficiency: 9.841 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 EvalPlus 0.4.0.dev2 run on all 164 HumanEval tasks, one greedy completion per task at temperature 0, using this Q8_0 artifact; pass@1.
Publisher-run single-sample result on short Python functions. It does not establish repository-level, multi-file or agent performance.
Writing Python that passes extra edge-case tests. EvalPlus pass@1 on all 164 problems.
Benchmark efficiency: 9.289 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 EvalPlus 0.4.0.dev2 run on all 164 HumanEval tasks with augmented tests, one greedy completion per task at temperature 0, using this Q8_0 artifact; pass@1.
Publisher labels the run HumanEval+ Mini, but explicitly states all 164 HumanEval problems and both original and augmented tests. Single-sample short-function results do not measure repository-level coding.
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 9.786 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 9.79 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 9.786 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.