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Qwen3.8-Flash-Next vs Sakana Namazu

Qwen3.8-Flash-Next and Sakana Namazu are closely matched at 49.1 and 42.1 on the LLM Stats Score.

Alibaba Cloud / Qwen Team · Sakana AI · Updated for 2026

Which is better?

Qwen3.8-Flash-Next and Sakana Namazu are closely matched on the overall LLM Stats Score at 49.1 and 42.1.

In the 1 individual benchmarks reported for both models, Qwen3.8-Flash-Next wins 1; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Qwen3.8-Flash-Next

  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Aug 2026
  • you need open weights you can self-host or fine-tune

Choose Sakana Namazu

  • you want predictable pricing at $0.95/M input and $4.00/M output

At a glance

The differences that matter most.

Core performance indexes
49.1
#20
42.1
#49
48.9
#19
41.9
#47
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
— / M
$0.95 / M
Output price
— / M
$4.00 / M
Context window
256,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Qwen3.8-Flash-Next
Sakana Namazu
32.7#56
38.4#26
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

22 reported for Qwen3.8-Flash-Next · 3 for Sakana Namazu

1 shared

Qwen3.8-Flash-Next outperforms in 1 benchmarks (LiveCodeBench v6), while Sakana Namazu is better at 0 benchmarks.

Qwen3.8-Flash-Next significantly outperforms across most benchmarks.

Thu Sep 17 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only Sakana Namazu specifies input context (256,000 tokens). Only Sakana Namazu specifies output context (256,000 tokens).

Alibaba Cloud / Qwen Team
Qwen3.8-Flash-Next
Input- tokens
Output- tokens
Sakana AI
Sakana Namazu
Input256,000 tokens
Output256,000 tokens
Thu Sep 17 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Qwen3.8-Flash-Next and Sakana Namazu support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Qwen3.8-Flash-Next

Text
Images
Audio
Video

Sakana Namazu

Text
Images
Audio
Video

License

Usage and distribution terms

Qwen3.8-Flash-Next is licensed under Qwen Community License 1.0, while Sakana Namazu uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

Qwen3.8-Flash-Next

Qwen Community License 1.0

Open weights

Sakana Namazu

Proprietary

Closed source

Release Timeline

When each model was launched

Qwen3.8-Flash-Next was released on 2026-08-26, while Sakana Namazu was released on 2026-08-03.

Qwen3.8-Flash-Next is 1 month newer than Sakana Namazu.

Qwen3.8-Flash-Next

Aug 26, 2026

3 weeks ago

3w newer
Sakana Namazu

Aug 3, 2026

1 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Qwen3.8-Flash-Next and Sakana Namazu side-by-side, then vote on the output you prefer.

Qwen3.8-Flash-Next
✓ Preferred
Sakana Namazu
Open in Playground

FAQ

Common questions about Qwen3.8-Flash-Next vs Sakana Namazu.

Which is better, Qwen3.8-Flash-Next or Sakana Namazu?

Qwen3.8-Flash-Next and Sakana Namazu are closely matched on the LLM Stats Score at 49.1 and 42.1. Qwen3.8-Flash-Next is made by Alibaba Cloud / Qwen Team and Sakana Namazu is made by Sakana AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Qwen3.8-Flash-Next compare to Sakana Namazu in benchmarks?

Qwen3.8-Flash-Next scores MathVision: 95.7%, LiveCodeBench v6: 91.9%, GPQA: 91.7%, CharXiv-R: 90.6%, RealWorldQA: 88.5%. Sakana Namazu scores AIME 2026: 96.7%, LiveCodeBench v6: 90.3%, MMLU-Pro: 90.3%.

What are the context window sizes for Qwen3.8-Flash-Next and Sakana Namazu?

Qwen3.8-Flash-Next supports an unknown number of tokens and Sakana Namazu supports 256K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Qwen3.8-Flash-Next and Sakana Namazu?

Key differences include LLM Stats Score (49.1 vs 42.1), licensing (Qwen Community License 1.0 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Qwen3.8-Flash-Next and Sakana Namazu?

Qwen3.8-Flash-Next is developed by Alibaba Cloud / Qwen Team and Sakana Namazu is developed by Sakana AI.