LLM Comparison

SWE-1.7 vs Qwen3.8 27B: benchmark scores, pricing & comparison.

Side-by-side SWE-1.7 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.

Open live leaderboardSWE-1.7 model pageQwen3.8 27B model page
Rank #30 vs #31AskClash overall scores 51.1 vs 49.3.
Pricing $0/$0 vs $0.45/$3.20Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightCognition vs Alibaba.

SWE-1.7 vs Qwen3.8 27B benchmark comparison

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

MetricSWE-1.7Qwen3.8 27B
Overall Score51.149.3
Leaderboard Rank#30#31
ACB—56.4
RWT8.5—
HLE—30.8
GPQA—89.2
IFEval—79.5
SWE-Pro—61.7
Terminal-Bench81.573.0
DeepSWE—42.2
OSWorld—84.3
Finance Agent—48.6
CharXiv—90.2
Input Price (per 1M tokens)$0$0.45
Output Price (per 1M tokens)$0$3.20
Context Window256K262K
Benchmarks Published110

SWE-1.7 vs Qwen3.8 27B head-to-head charts

SWE-1.7 leads 2 and Qwen3.8 27B leads 0 of 2 shared benchmarks. SWE-1.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

SWE-1.7Qwen3.8 27B
Overall
51.1SWE-1.7
49.3Qwen3.8 27B
Terminal-Bench
81.5SWE-1.7
73.0Qwen3.8 27B
SWE-1.7
Input$0
Output$0
Workload$0.00
Context256K
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 SWE-1.7 and Qwen3.8 27B comparisons

Explore how SWE-1.7 and Qwen3.8 27B stack up against other top-ranked LLMs.

Claude Fable 5.1 vs SWE-1.7Claude Fable 5.1 vs Qwen3.8 27BClaude Fable 5 vs SWE-1.7Claude Fable 5 vs Qwen3.8 27BGPT-5.6 Sol vs SWE-1.7GPT-5.6 Sol vs Qwen3.8 27BClaude Opus 5 vs SWE-1.7Claude Opus 5 vs Qwen3.8 27BGrok 4.6 vs SWE-1.7Grok 4.6 vs Qwen3.8 27B

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.