How do GPT-5.6 Sol and Qwen3.8 Max 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 Sol and Qwen3.8 Max when publicly disclosed.

LLM Comparison

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

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

AskClash compares GPT-5.6 Sol and Qwen3.8 Max 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 GPT-5.6 Sol vs Qwen3.8 Max 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 GPT-5.6 Sol model page Qwen3.8 Max model page

Rank #4 vs #5AskClash overall scores 71.2 vs 70.9.

Pricing $5.00/$30.0 vs $2.00/$6.00Input and output token prices per 1M tokens when published.

Proprietary vs Open WeightOpenAI vs Alibaba.

GPT-5.6 Sol vs Qwen3.8 Max benchmark comparison

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

Metric	GPT-5.6 Sol	Qwen3.8 Max
Overall Score	71.2	70.9
Leaderboard Rank	#4	#5
ACB	63.2	69.3
RWT	9.5	—
Coding Agent Index	80.0	43.3
HLE	47.2	43.6
GPQA	94.1	92.6
IFEval	72.7	82.8
SWE-Pro	64.6	67.7
SWE-Atlas	84.0	66.3
Terminal-Bench	88.8	86.6
DeepSWE	72.7	69.3
GDPval-AA	1748.0	1689.0
OSWorld	—	86.1
Finance Agent	53.8	50.6
CharXiv	—	88.4
MMMU-Pro	83.0	82.7
ARC-AGI 2	92.5	—
Tau2	85.1	—
MRCR	91.5	92.9
Input Price (per 1M tokens)	$5.00	$2.00
Output Price (per 1M tokens)	$30.0	$6.00
Context Window	1M	1M
Benchmarks Published	16	16

GPT-5.6 Sol vs Qwen3.8 Max head-to-head charts

GPT-5.6 Sol leads 10 and Qwen3.8 Max leads 4 of 14 shared benchmarks. Qwen3.8 Max is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 SolQwen3.8 Max

Overall

71.2GPT-5.6 Sol

70.9Qwen3.8 Max

ACB

63.2GPT-5.6 Sol

69.3Qwen3.8 Max

Coding Agent Index

80.0GPT-5.6 Sol

43.3Qwen3.8 Max

HLE

47.2GPT-5.6 Sol

43.6Qwen3.8 Max

GPQA

94.1GPT-5.6 Sol

92.6Qwen3.8 Max

IFEval

72.7GPT-5.6 Sol

82.8Qwen3.8 Max

SWE-Pro

64.6GPT-5.6 Sol

67.7Qwen3.8 Max

SWE-Atlas

84.0GPT-5.6 Sol

66.3Qwen3.8 Max

Terminal-Bench

88.8GPT-5.6 Sol

86.6Qwen3.8 Max

DeepSWE

72.7GPT-5.6 Sol

69.3Qwen3.8 Max

GDPval-AA

1748.0GPT-5.6 Sol

1689.0Qwen3.8 Max

Finance Agent

53.8GPT-5.6 Sol

50.6Qwen3.8 Max

GPT-5.6 Sol

Input$5.00

Output$30.0

Workload$11

Context1M

Qwen3.8 Max

Input$2.00

Output$6.00

Workload$3.20

Context1M

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

More GPT-5.6 Sol and Qwen3.8 Max comparisons

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Claude Fable 5.1 vs GPT-5.6 Sol Claude Fable 5.1 vs Qwen3.8 Max Claude Fable 5 vs GPT-5.6 Sol Claude Fable 5 vs Qwen3.8 Max Claude Opus 5 vs GPT-5.6 Sol Claude Opus 5 vs Qwen3.8 Max GPT-5.6 Sol vs GPT-6 Astra Qwen3.8 Max vs GPT-6 Astra GPT-5.6 Sol vs Claude Opus 4.8 Qwen3.8 Max vs Claude Opus 4.8

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.