How does Qwen3.8-Flash-Next compare with other LLMs?

AskClash compares Qwen3.8-Flash-Next against nearby AI models using public benchmark scores, pricing, context window, and access details.

What benchmarks are tracked for Qwen3.8-Flash-Next?

The page shows cached published benchmark signals such as HLE, GPQA, SWE-bench, SWE-Pro, Terminal-Bench, MCP Atlas, MMMU-Pro, ARC-AGI-2, Tau2, and related model scores when available.

LLM Leaderboard · Open Weight

Qwen3.8 Flash Next leaderboard — benchmarks, pricing, and comparisons.

Compare Qwen3.8 Flash Next vs GPT, Claude, Gemini, DeepSeek, open-weight, and frontier AI models using public benchmark scores, token pricing, context window, and access details.

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Rank #19AskClash overall score: 57.6

$0.16 / $0.47Input and output token price, when published. Context: 1M.

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Qwen3.8 Flash Next benchmark snapshot

AskClash combines public LLM benchmark cells into a weighted percentile score and penalizes missing coverage so narrow rows do not dominate better-measured models.

Overall57.6

Benchmark cells9

Context1M

CreatorAlibaba

Qwen3.8 Flash Next public benchmark scores

Cached benchmark values can include HLE, GPQA, SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, MCP Atlas, MMMU-Pro, ARC-AGI-2, Tau2, and model-specific coding or agent scores.

ACB

66.5 score

HLE

35.9 score

GPQA

91.7 score

IFEval

81.3 score

SWE-Pro

62.5 score

DeepSWE

58.7 score

GDPval-AA

1743.0 score

CharXiv

90.6 score

Qwen3.8 Flash Next vs other AI models

Use these comparison links to evaluate Qwen3.8 Flash Next against nearby LLMs by benchmark score, price, context window, and provider.

Claude Fable 5.1 vs Qwen3.8 Flash Next Claude Opus 5 vs Qwen3.8 Flash Next Claude Fable 5 vs Qwen3.8 Flash Next GLM-5.3 vs Qwen3.8 Flash Next Grok 4.6 vs Qwen3.8 Flash Next GPT-5.6 Sol vs Qwen3.8 Flash Next Kimi K3 vs Qwen3.8 Flash Next Qwen3.8 Max vs Qwen3.8 Flash Next

Related AI and tech coverage

Cached AskClash article matches that can provide release, provider, benchmark, pricing, or market context around this model.

Alibaba’s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture

We look at Qwen3.8-Flash-Next, Alibaba's open-weight multimodal Mixture-of-Experts model and an early preview of the Qwen4 architecture. We break down where the 180B parameters actually sit: a 125B backbone, a 51B N-gram embedding table, and a 4B multi-token p

Alibaba releases Qwen3.8-Flash-Next, targeting "ultimate cost efficiency"

Alibaba's Qwen team is previewing the Qwen4 architecture with Qwen3.8-Flash-Next, a mixture-of-experts model that activates just 6 out of 125 billion parameters per token. At one-ninth the training cost, it beats much larger competitors like DeepSeek-V4-Flash

GLM-5.3-Flash vs Qwen3.8-Flash-Next: Two Chinese AI Labs Independently Converge on the Same Model Architecture

Z.ai and Qwen independently shipped near-identical architectures: 3:1 linear hybrids, compressed indexers, gated residuals, and Muon training. The post GLM-5.3-Flash vs Qwen3.8-Flash-Next: Two Chinese AI Labs Independently Converge on the Same Model Architectu

Qwen3.8-Flash-Next

Qwen3.8-Flash-Next Simon Willison’s Weblog Subscribe Sponsored by: Teleport — AI agents don’t sleep and will try anything to achieve their goal. Teleport explains how to deploy AI safely, starting with an isolated ephemeral trusted runtime. 26th August 2026 -

Last cached leaderboard date: August 26, 2026. This model page is generated from the AskClash LLM Leaderboard cache and linked from the live leaderboard.