How do Qwen3.8 Max 0902 and GPT-5.6 Terra compare on coding benchmarks?

The comparison table shows SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, DeepSWE, and coding agent index scores for both Qwen3.8 Max 0902 and GPT-5.6 Terra when publicly disclosed.

LLM Comparison

Qwen3.8 Max 0902 vs GPT-5.6 Terra: benchmark scores, pricing & comparison.

Q: Which is better: Qwen3.8 Max 0902 or GPT-5.6 Terra?

AskClash compares Qwen3.8 Max 0902 and GPT-5.6 Terra 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.

Side-by-side Qwen3.8 Max 0902 vs GPT-5.6 Terra 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 leaderboard Qwen3.8 Max 0902 model page GPT-5.6 Terra model page

Rank #9 vs #15AskClash overall scores 70.0 vs 64.8.

Pricing $2.00/$6.00 vs $2.50/$15.0Input and output token prices per 1M tokens when published.

Proprietary vs ProprietaryAlibaba vs OpenAI.

Qwen3.8 Max 0902 vs GPT-5.6 Terra benchmark comparison

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

Metric	Qwen3.8 Max 0902	GPT-5.6 Terra
Overall Score	70.0	64.8
Leaderboard Rank	#9	#15
ACB	59.0	56.4
RWT	—	8.5
Coding Agent Index	—	77.0
HLE	43.6	—
GPQA	92.6	92.9
IFEval	82.8	—
SWE-Pro	67.7	63.4
SWE-Atlas	66.3	81.0
Terminal-Bench	86.6	87.4
DeepSWE	69.3	69.6
GDPval-AA	—	1593.0
OSWorld	86.1	—
Finance Agent	—	54.4
CharXiv	88.4	—
MMMU-Pro	82.7	80.7
ARC-AGI 2	—	83.9
Tau2	—	86.3
MRCR	92.9	89.6
Input Price (per 1M tokens)	$2.00	$2.50
Output Price (per 1M tokens)	$6.00	$15.0
Context Window	1M	1M
Benchmarks Published	12	14

Qwen3.8 Max 0902 vs GPT-5.6 Terra head-to-head charts

Qwen3.8 Max 0902 leads 5 and GPT-5.6 Terra leads 4 of 9 shared benchmarks. Qwen3.8 Max 0902 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Qwen3.8 Max 0902GPT-5.6 Terra

Overall

70.0Qwen3.8 Max 0902

64.8GPT-5.6 Terra

ACB

59.0Qwen3.8 Max 0902

56.4GPT-5.6 Terra

GPQA

92.6Qwen3.8 Max 0902

92.9GPT-5.6 Terra

SWE-Pro

67.7Qwen3.8 Max 0902

63.4GPT-5.6 Terra

SWE-Atlas

66.3Qwen3.8 Max 0902

81.0GPT-5.6 Terra

Terminal-Bench

86.6Qwen3.8 Max 0902

87.4GPT-5.6 Terra

DeepSWE

69.3Qwen3.8 Max 0902

69.6GPT-5.6 Terra

MMMU-Pro

82.7Qwen3.8 Max 0902

80.7GPT-5.6 Terra

MRCR

92.9Qwen3.8 Max 0902

89.6GPT-5.6 Terra

Qwen3.8 Max 0902

Input$2.00

Output$6.00

Workload$3.20

Context1M

GPT-5.6 Terra

Input$2.50

Output$15.0

Workload$5.50

Context1M

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

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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.