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Kimi K2.7 Code vs Qwen3.8-Flash-Next

Qwen3.8-Flash-Next leads the LLM Stats Score 50.5 to 39.6.

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

Which is better?

Qwen3.8-Flash-Next leads the overall LLM Stats Score 50.5 to 39.6, ranking #14 overall.

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 Kimi K2.7 Code

  • you want predictable pricing at $0.74/M input and $3.50/M output

Choose Qwen3.8-Flash-Next

  • overall performance matters — it scores 50.5 and ranks #14 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • 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

At a glance

The differences that matter most.

Kimi K2.7 Code
Moonshot AI
Qwen3.8-Flash-Next
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
39.6
#49
50.5
#14
Reasoning
35.2
#74
50.6
#12
Coding
32.2
#42
38.4
#19
Agents
28.0
#37
37.2
#12
Cost, coverage & limits
Benchmark wins
0 of 1
1 of 1
Input price
$0.74 / M
— / M
Output price
$3.50 / M
— / M
Context window
262,144
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Kimi K2.7 Code
Qwen3.8-Flash-Next
Tool use
25.6#26
32.3#8
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

9 reported for Kimi K2.7 Code · 22 for Qwen3.8-Flash-Next

1 shared

Kimi K2.7 Code outperforms in 0 benchmarks, while Qwen3.8-Flash-Next is better at 1 benchmark (DeepSWE 1.1).

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

Fri Aug 28 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

875.0B diff

Kimi K2.7 Code has 875.0B more parameters than Qwen3.8-Flash-Next, making it 700.0% larger.

Moonshot AI
Kimi K2.7 Code
1.0Tparameters
Alibaba Cloud / Qwen Team
Qwen3.8-Flash-Next
125.0Bparameters
1000.0B
Kimi K2.7 Code
125.0B
Qwen3.8-Flash-Next

Context Window

Maximum input and output token capacity

Only Kimi K2.7 Code specifies input context (262,144 tokens). Only Kimi K2.7 Code specifies output context (131,072 tokens).

Moonshot AI
Kimi K2.7 Code
Input262,144 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen3.8-Flash-Next
Input- tokens
Output- tokens
Fri Aug 28 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Kimi K2.7 Code and Qwen3.8-Flash-Next support multimodal inputs.

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

Kimi K2.7 Code

Text
Images
Audio
Video

Qwen3.8-Flash-Next

Text
Images
Audio
Video

License

Usage and distribution terms

Kimi K2.7 Code is licensed under Modified MIT License, while Qwen3.8-Flash-Next uses Qwen Community License 1.0.

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

Kimi K2.7 Code

Modified MIT License

Open weights

Qwen3.8-Flash-Next

Qwen Community License 1.0

Open weights

Release Timeline

When each model was launched

Kimi K2.7 Code was released on 2026-06-12, while Qwen3.8-Flash-Next was released on 2026-08-26.

Qwen3.8-Flash-Next is 3 months newer than Kimi K2.7 Code.

Kimi K2.7 Code

Jun 12, 2026

2 months ago

Qwen3.8-Flash-Next

Aug 26, 2026

2 days ago

2mo newer

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 Kimi K2.7 Code and Qwen3.8-Flash-Next side-by-side, then vote on the output you prefer.

Kimi K2.7 Code
✓ Preferred
Qwen3.8-Flash-Next
Open in Playground

FAQ

Common questions about Kimi K2.7 Code vs Qwen3.8-Flash-Next.

Which is better, Kimi K2.7 Code or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next leads the LLM Stats Score 50.5 to 39.6. Kimi K2.7 Code is made by Moonshot AI and Qwen3.8-Flash-Next is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Kimi K2.7 Code compare to Qwen3.8-Flash-Next in benchmarks?

Kimi K2.7 Code scores MCP-Mark: 81.1%, MCP Atlas: 76.0%, LiveBench: 71.9%, Kimi Code Bench v2: 62.0%, Program Bench: 53.6%. Qwen3.8-Flash-Next scores MathVision: 95.7%, LiveCodeBench v6: 91.9%, GPQA: 91.7%, CharXiv-R: 90.6%, RealWorldQA: 88.5%.

What are the context window sizes for Kimi K2.7 Code and Qwen3.8-Flash-Next?

Kimi K2.7 Code supports 262K tokens and Qwen3.8-Flash-Next supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Kimi K2.7 Code and Qwen3.8-Flash-Next?

Key differences include LLM Stats Score (39.6 vs 50.5), licensing (Modified MIT License vs Qwen Community License 1.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Kimi K2.7 Code and Qwen3.8-Flash-Next?

Kimi K2.7 Code is developed by Moonshot AI and Qwen3.8-Flash-Next is developed by Alibaba Cloud / Qwen Team.

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