ACB
56.4 score
LLM Leaderboard · Open Weight
Qwen3.8 27B benchmarks — leaderboard rank, pricing, and comparisons.
Qwen3.8 27B ranks #39 on the AskClash LLM leaderboard with an overall score of 51.4. It scores 73.0 on Terminal-Bench. Compare Qwen3.8 27B vs GPT, Claude, Gemini, DeepSeek, open-weight, and frontier AI models using public benchmark scores, token pricing, context window, and access details.
Rank #39AskClash overall score: 51.4
$0.45 / $3.20Input and output token price, when published. Context: 262K.
Visit websiteVisit the model provider's website.
Qwen3.8 27B benchmark snapshot
AskClash combines public LLM benchmark cells into a weighted percentile score and penalizes missing coverage so narrow rows do not dominate better-measured models.
Overall51.4
Benchmark cells13
Context262K
CreatorAlibaba
Qwen3.8 27B public benchmark scores
Cached benchmark values can include HLE, GPQA, SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, MCP Atlas, MMMU-Pro, ARC-AGI-2, Tau2, and model-specific coding or agent scores.
HLE
30.8 score
GPQA
89.2 score
IFEval
79.5 score
SWE-Pro
61.7 score
Terminal-Bench
73.0 score
DeepSWE
42.2 score
GDPval-AA
1408.9 score
OSWorld
84.3 score
Finance Agent
48.6 score
CharXiv
90.2 score
MMMU-Pro
76.3 score
Qwen3.8 27B vs other AI models
Use these comparison links to evaluate Qwen3.8 27B against nearby LLMs by benchmark score, price, context window, and provider.
Related AI and tech coverage
Cached AskClash article matches that can provide release, provider, benchmark, pricing, or market context around this model.
llama.cpp b11177 Release Notes
- rms_norm_f32 gets a do_scale flag, the same pattern as do_multiply/do_add, so the fused path shares the kernel, the reduction and the launcher. It computes scale * (rsqrt(mean + eps) * x), which matches the unfused rms_norm + scale bit for bit, so #28068 num
Last cached leaderboard date: . This model page is generated from the AskClash LLM Leaderboard cache and linked from the live leaderboard.