Which is better: GPT-5.6 Terra or Qwen3.8 27B?

Verdict: GPT-5.6 Terra ranks higher (#12 vs #39) and wins 9 of 10 shared benchmarks; Qwen3.8 27B costs less ($0.45/$3.20 vs $2.50/$15.0 per 1M tokens). AskClash compares GPT-5.6 Terra 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 GPT-5.6 Terra 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 GPT-5.6 Terra and Qwen3.8 27B when publicly disclosed.

LLM Comparison

GPT-5.6 Terra vs Qwen3.8 27B: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Terra 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: GPT-5.6 Terra ranks higher (#12 vs #39) and wins 9 of 10 shared benchmarks; Qwen3.8 27B costs less ($0.45/$3.20 vs $2.50/$15.0 per 1M tokens).

Open live leaderboard GPT-5.6 Terra model page Qwen3.8 27B model page

Rank #12 vs #39AskClash overall scores 66.5 vs 51.4.

Pricing $2.50/$15.0 vs $0.45/$3.20Input and output token prices per 1M tokens when published.

Proprietary vs Open WeightOpenAI vs Alibaba.

GPT-5.6 Terra 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	GPT-5.6 Terra	Qwen3.8 27B
Overall Score	66.5	51.4
Leaderboard Rank	#12	#39
ACB	56.4	56.4
RWT	8.5	—
Coding Agent Index	77.0	—
HLE	42.9	30.8
GPQA	92.9	89.2
IFEval	71.2	79.5
SWE-Pro	63.4	61.7
SWE-Atlas	81.0	—
Terminal-Bench	87.4	73.0
DeepSWE	69.6	42.2
GDPval-AA	1593.0	1408.9
OSWorld	—	84.3
Finance Agent	54.4	48.6
CharXiv	—	90.2
MMMU-Pro	80.7	76.3
ARC-AGI 2	83.9	—
Tau2	86.3	—
MRCR	89.6	—
Input Price (per 1M tokens)	$2.50	$0.45
Output Price (per 1M tokens)	$15.0	$3.20
Context Window	1M	262K
Benchmarks Published	15	13

GPT-5.6 Terra vs Qwen3.8 27B head-to-head charts

GPT-5.6 Terra leads 10 and Qwen3.8 27B leads 1 of 11 shared benchmarks. Qwen3.8 27B is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 TerraQwen3.8 27B

Overall

66.5GPT-5.6 Terra

51.4Qwen3.8 27B

ACB

56.4GPT-5.6 Terra

56.4Qwen3.8 27B

HLE

42.9GPT-5.6 Terra

30.8Qwen3.8 27B

GPQA

92.9GPT-5.6 Terra

89.2Qwen3.8 27B

IFEval

71.2GPT-5.6 Terra

79.5Qwen3.8 27B

SWE-Pro

63.4GPT-5.6 Terra

61.7Qwen3.8 27B

Terminal-Bench

87.4GPT-5.6 Terra

73.0Qwen3.8 27B

DeepSWE

69.6GPT-5.6 Terra

42.2Qwen3.8 27B

GDPval-AA

1593.0GPT-5.6 Terra

1408.9Qwen3.8 27B

Finance Agent

54.4GPT-5.6 Terra

48.6Qwen3.8 27B

MMMU-Pro

80.7GPT-5.6 Terra

76.3Qwen3.8 27B

GPT-5.6 Terra

Input$2.50

Output$15.0

Workload$5.50

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 GPT-5.6 Terra and Qwen3.8 27B comparisons

Explore how GPT-5.6 Terra and Qwen3.8 27B stack up against other top-ranked LLMs.

Claude Fable 5.1 vs GPT-5.6 Terra Claude Fable 5.1 vs Qwen3.8 27B Claude Opus 5.5 vs GPT-5.6 Terra Claude Opus 5.5 vs Qwen3.8 27B Claude Fable 5 vs GPT-5.6 Terra Claude Fable 5 vs Qwen3.8 27B Claude Opus 5 vs GPT-5.6 Terra Claude Opus 5 vs Qwen3.8 27B GPT-5.6 Sol vs GPT-5.6 Terra GPT-5.6 Sol 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.