How do Qwen3.8 Flash Next 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 Flash Next and GPT-5.6 Terra when publicly disclosed.

LLM Comparison

Qwen3.8 Flash Next vs GPT-5.6 Terra: benchmark scores, pricing & comparison.

Q: Which is better: Qwen3.8 Flash Next or GPT-5.6 Terra?

AskClash compares Qwen3.8 Flash Next 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 Flash Next 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 Flash Next model page GPT-5.6 Terra model page

Rank #14 vs #17AskClash overall scores 60.5 vs 60.1.

Pricing $0.16/$0.47 vs $2.50/$15.0Input and output token prices per 1M tokens when published.

Open Weight vs ProprietaryAlibaba vs OpenAI.

Qwen3.8 Flash Next 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 Flash Next	GPT-5.6 Terra
Overall Score	60.5	60.1
Leaderboard Rank	#14	#17
ACB	66.5	56.4
RWT	—	8.5
Coding Agent Index	—	77.0
HLE	35.9	—
GPQA	91.7	92.9
IFEval	81.3	—
SWE-Pro	62.5	63.4
SWE-Atlas	—	81.0
Terminal-Bench	86.1	87.4
DeepSWE	58.7	69.6
GDPval-AA	1743.0	1593.0
Finance Agent	—	54.4
CharXiv	90.6	—
MMMU-Pro	—	80.7
ARC-AGI 2	—	83.9
Tau2	—	86.3
MRCR	—	89.6
Input Price (per 1M tokens)	$0.16	$2.50
Output Price (per 1M tokens)	$0.47	$15.0
Context Window	1M	1M
Benchmarks Published	10	14

Qwen3.8 Flash Next vs GPT-5.6 Terra head-to-head charts

Qwen3.8 Flash Next leads 3 and GPT-5.6 Terra leads 4 of 7 shared benchmarks. Qwen3.8 Flash Next is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Qwen3.8 Flash NextGPT-5.6 Terra

Overall

60.5Qwen3.8 Flash Next

60.1GPT-5.6 Terra

ACB

66.5Qwen3.8 Flash Next

56.4GPT-5.6 Terra

GPQA

91.7Qwen3.8 Flash Next

92.9GPT-5.6 Terra

SWE-Pro

62.5Qwen3.8 Flash Next

63.4GPT-5.6 Terra

Terminal-Bench

86.1Qwen3.8 Flash Next

87.4GPT-5.6 Terra

DeepSWE

58.7Qwen3.8 Flash Next

69.6GPT-5.6 Terra

GDPval-AA

1743.0Qwen3.8 Flash Next

1593.0GPT-5.6 Terra

Qwen3.8 Flash Next

Input$0.16

Output$0.47

Workload$0.25

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.

More Qwen3.8 Flash Next and GPT-5.6 Terra comparisons

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

Claude Fable 5.1 vs Qwen3.8 Flash Next Claude Fable 5.1 vs GPT-5.6 Terra Claude Fable 5 vs Qwen3.8 Flash Next Claude Fable 5 vs GPT-5.6 Terra GPT-6 Astra vs Qwen3.8 Flash Next GPT-6 Astra vs GPT-5.6 Terra GPT-5.6 Sol vs Qwen3.8 Flash Next GPT-5.6 Sol vs GPT-5.6 Terra Claude Opus 5 vs Qwen3.8 Flash Next Claude Opus 5 vs GPT-5.6 Terra

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.