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.
GPQA Diamond
83.84 / 100
Hard science questions. This is a science reasoning test, not an overall intelligence score.
Benchmark efficiency: 4.332 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 formal evaluation of the exact Q4-LynnStyle artifact: 166 correct from 198 GPQA Diamond questions. The release says non-passing samples remain in the denominator.
Publisher-reported, not an independent test. The selected quantized artifact was evaluated, but prompt, decoding and runtime details can affect reproduction.
Publisher-reported accuracy over a 500-question MMLU evaluation for this release. Kept separate from full MMLU and other sampled protocols.
Benchmark efficiency: 4.578 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 formal evaluation of the exact Q4-LynnStyle artifact: 443 correct from a 500-question MMLU suite.
Publisher-reported, not an independent test. This sampled 500-question protocol is kept separate from full MMLU and other MMLU variants.
Publisher-reported pass rate over 100 LiveCodeBench tasks for this release. The benchmark version is not stated, so it is not comparable with LiveCodeBench v6.
Benchmark efficiency: 3.824 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 formal evaluation of the exact Q4-LynnStyle artifact: 74 passes from 100 unique LiveCodeBench tasks, with 23/23 easy, 30/31 medium and 21/46 hard; errors=0 and clean gate PASS.
Publisher-reported, not an independent test. The LiveCodeBench version is not stated, so this score is kept separate from the site's v6 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 19.352 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.
Need a computer for Qwen3.8-27B EfficientThink SFT-SimPO?
These complete configurations pass the initial weight-size check for the selected 19.35 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.
RAM and VRAM planning for Qwen3.8 27B EfficientThink
Confirm model architecture and quantization support in your runner. The selected weights use 19.352 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.