Which is better: SWE-2 or Qwen3.8 27B?

Verdict: SWE-2 ranks higher (#22 vs #43) and wins 7 of 8 shared benchmarks; SWE-2 costs less ($0/$0 vs $0.45/$3.20 per 1M tokens). AskClash compares SWE-2 and Qwen3.8 27B side by side across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, AskClash ACB, and context window so you can see which model wins on each benchmark.

How do SWE-2 and Qwen3.8 27B compare on coding benchmarks?

The comparison table shows SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, DeepSWE, and coding agent index scores for both SWE-2 and Qwen3.8 27B when publicly disclosed.

LLM Comparison

SWE-2 vs Qwen3.8 27B: benchmark scores, pricing & comparison.

Side-by-side SWE-2 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.

Verdict: SWE-2 ranks higher (#22 vs #43) and wins 7 of 8 shared benchmarks; SWE-2 costs less ($0/$0 vs $0.45/$3.20 per 1M tokens).

Open live leaderboard SWE-2 model page Qwen3.8 27B model page

Rank #22 vs #43AskClash overall scores 55.3 vs 40.4.

Pricing $0/$0 vs $0.45/$3.20Input and output token prices per 1M tokens when published.

Proprietary vs Open WeightCognition vs Alibaba.

SWE-2 vs Qwen3.8 27B benchmark comparison

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

Metric	SWE-2	Qwen3.8 27B
Overall Score	55.3	40.4
Leaderboard Rank	#22	#43
ACB	60.0	56.4
RWT	8.5	—
Coding Agent Index	51.9	—
HLE	43.5	30.8
GPQA	93.5	89.2
IFEval	—	79.5
SWE-Pro	—	61.7
SWE-Atlas	66.1	—
Terminal-Bench	27.3	—
DeepSWE	73.0	42.2
GDPval-AA	1668.0	1427.3
OSWorld	—	84.3
MCP Atlas	84.2	—
Finance Agent	54.4	48.6
CharXiv	84.8	90.2
MMMU-Pro	81.6	76.3
Input Price (per 1M tokens)	$0	$0.45
Output Price (per 1M tokens)	$0	$3.20
Context Window	256K	262K
Benchmarks Published	13	12

SWE-2 vs Qwen3.8 27B head-to-head charts

SWE-2 leads 8 and Qwen3.8 27B leads 1 of 9 shared benchmarks. SWE-2 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

SWE-2Qwen3.8 27B

Overall

55.3SWE-2

40.4Qwen3.8 27B

ACB

60.0SWE-2

56.4Qwen3.8 27B

HLE

43.5SWE-2

30.8Qwen3.8 27B

GPQA

93.5SWE-2

89.2Qwen3.8 27B

DeepSWE

73.0SWE-2

42.2Qwen3.8 27B

GDPval-AA

1668.0SWE-2

1427.3Qwen3.8 27B

Finance Agent

54.4SWE-2

48.6Qwen3.8 27B

CharXiv

84.8SWE-2

90.2Qwen3.8 27B

MMMU-Pro

81.6SWE-2

76.3Qwen3.8 27B

SWE-2

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-2 and Qwen3.8 27B comparisons

Explore how SWE-2 and Qwen3.8 27B stack up against other top-ranked LLMs.

Claude Opus 5.5 vs SWE-2 Claude Opus 5.5 vs Qwen3.8 27B Claude Sonnet 5.5 vs SWE-2 Claude Sonnet 5.5 vs Qwen3.8 27B Claude Fable 5.1 vs SWE-2 Claude Fable 5.1 vs Qwen3.8 27B Claude Opus 5 vs SWE-2 Claude Opus 5 vs Qwen3.8 27B Claude Fable 5 vs SWE-2 Claude Fable 5 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.