LLM Comparison

Claude Fable 5.1 vs Qwen3.8 27B: benchmark scores, pricing & comparison.

Side-by-side Claude Fable 5.1 vs Qwen3.8 27B comparison across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, context window, and AskClash RWT. Green marks the winner on each benchmark.

Rank #1 vs #26AskClash overall scores 92.0 vs 47.5.
Pricing $10.0/$50.0 vs $0.45/$3.20Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightAnthropic vs Alibaba.

Claude Fable 5.1 vs Qwen3.8 27B benchmark comparison

Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.

MetricClaude Fable 5.1Qwen3.8 27B
Overall Score92.047.5
Leaderboard Rank#1#26
ACB79.256.4
RWT9.0
HLE65.030.8
GPQA93.789.2
IFEval79.5
SWE-Pro81.261.7
Terminal-Bench91.473.0
DeepSWE67.442.2
GDPval-AA1853.0
OSWorld84.3
Finance Agent58.948.6
CharXiv90.2
MMMU-Pro90.6
ARC-AGI 290.0
Input Price (per 1M tokens)$10.0$0.45
Output Price (per 1M tokens)$50.0$3.20
Context Window1M262K
Benchmarks Published1110

Claude Fable 5.1 vs Qwen3.8 27B head-to-head charts

Claude Fable 5.1 leads 8 and Qwen3.8 27B leads 0 of 8 shared benchmarks. Qwen3.8 27B is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Claude Fable 5.1Qwen3.8 27B
Overall
92.0Claude Fable 5.1
47.5Qwen3.8 27B
ACB
79.2Claude Fable 5.1
56.4Qwen3.8 27B
HLE
65.0Claude Fable 5.1
30.8Qwen3.8 27B
GPQA
93.7Claude Fable 5.1
89.2Qwen3.8 27B
SWE-Pro
81.2Claude Fable 5.1
61.7Qwen3.8 27B
Terminal-Bench
91.4Claude Fable 5.1
73.0Qwen3.8 27B
DeepSWE
67.4Claude Fable 5.1
42.2Qwen3.8 27B
Finance Agent
58.9Claude Fable 5.1
48.6Qwen3.8 27B
Claude Fable 5.1
Input$10.0
Output$50.0
Workload$20
Context1M
Qwen3.8 27B
Input$0.45
Output$3.20
Workload$1.09
Context262K

Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.

More Claude Fable 5.1 and Qwen3.8 27B comparisons

Explore how Claude Fable 5.1 and Qwen3.8 27B stack up against other top-ranked LLMs.

How to read this comparison

Benchmark scores

Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.

Token pricing

Lower is better for input and output prices. Green marks the cheaper model per 1M tokens.

Coverage matters

Models with fewer disclosed benchmark cells may have inflated percentile scores. Check the benchmark cell count for context.

This comparison page is generated from the AskClash LLM leaderboard cache. Open the live leaderboard for real-time scores and interactive filtering.