Qwen3.8-Flash-Next leads overall
Ahead on 6 of 9 benchmarks, averaging 1.4 points higher
Summarised from the 9 benchmarks both models were scored on; details in the charts below.

DeepSeek-V4-Flash
DeepSeek-AI
Updates live with the mode filters below.
Best overall
Qwen3.8-Flash-Next · 63.07
Best single
Qwen3.8-Flash-Next · LiveCodeBench 91.90
Modality coverage
Qwen3.8-Flash-Next · 3 modalities
Head to head
9
Benchmarks
3
Wins
6
Losses
-1.42
Average diff
Benchmark-by-benchmark comparison. Changing the thinking mode or tool filters updates the chart and table below.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Each axis is the mean percentage score of one benchmark domain. It is an average, not a capability rating.
Relative edge: Agent能力评测 +0.9 / Relative gap: 科学与综合推理 -9.5
Relative edge: 科学与综合推理 +9.5 / Relative gap: Agent能力评测 -0.9
Method: for each model and benchmark, all scores in the current mode scope are averaged (not the best score), then those benchmark scores are averaged within each domain. Only benchmarks scored on a 0-100 scale by at least two of the selected models count — Elo and rating-scale benchmarks such as Codeforces or Arena are excluded, because averaging a 1500 rating with an 85% accuracy produces a meaningless number. Missing values are not counted as zero, and the averages are unweighted, so domains with harder benchmarks read lower.
Every model and runtime mode, benchmark by benchmark. Values are comparable along a row, not between different benchmarks.
9 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | DeepSeek-V4-Flash | Qwen3.8-Flash-Next |
|---|---|---|
HLE 综合评估 | 45.10Thinking Level · Extra High | Tools | 35.90Thinking Level · Extra High |
GPQA Diamond 科学与综合推理 | 88.10Thinking Level · High | 91.70Thinking Level · Extra High |
DeepSWE 编程与软件工程 | 54.40Thinking Level · High | Tools | 58.70Thinking Level · Extra High | Tools |
LiveCodeBench 编程与软件工程 | 91.60Thinking Level · High | 91.90Thinking Level · Extra High |
NL2Repo-Bench 编程与软件工程 | 54.20Thinking Level · High | Tools | 48.10Thinking Level · Extra High | Tools |
SWE-bench Multilingual 编程与软件工程 | 73.30Thinking Level · Extra High | Tools | 81.00Thinking Level · Extra High | Tools |
SWE-Bench Pro - Public 编程与软件工程 | 52.60Thinking Level · Extra High | Tools | 62.50Thinking Level · Extra High | Tools |
Toolathlon-Verified AI Agent - 工具使用 | 70.30Thinking Level · High | Tools | 73.50Thinking Level · Extra High | Tools |
Agents' Last Exam Agent能力评测 | 25.20Thinking Level · High | Tools | 24.30Thinking Level · Extra High | Tools |
Official list prices per model API, split by input and output. Unit: USD per 1M tokens.
Architecture, licensing and API modalities. "Not provided" means the field is missing from our database.
| Features & specs | DeepSeek-V4-FlashDeepSeek-AI | Qwen3.8-Flash-Next阿里巴巴 |
|---|---|---|
Core specsRelease | 2026-04-24 | 2026-08-26 |
Context length | 1M | 256K(原生,可扩展至 1M) |
Total parameters | 284B | 125B |
Active parameters | 13B | 6B |
Max output length | 384,000 tokens | 131,072 tokens |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 最高关闭高 | 极高关闭低中 |
LicenseCode Open Source | Open Source · MIT License | Open Source · Qwen Community License 1.0 |
Weights Open Source | Open Source · MIT License | Open Source · Qwen Community License 1.0 |
Licensing status | 免费商用授权 | 有条件免费商用授权 |
Local deploymentWeight size | Not provided | 约 360 GB(BF16,131 个 safetensors 分片) |
VRAM for weights | Not provided | ≈ 360 GB (weights only, excludes KV cache) |
Weights | Hugging Face | Hugging Face |
Source repo | Not provided | GitHub |
API modality supportText Input/Output | / | / |
Image Input/Output | Not provided | / |
Video Input/Output | Not provided | / |
ResourcesPaper / report | DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence | On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability |

Qwen3.8-Flash-Next
阿里巴巴