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GPT-5.3 Codex vs Qwen3.8-Flash-Next

Qwen3.8-Flash-Next leads the LLM Stats Score 49.7 to 36.8.

OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3.8-Flash-Next leads the overall LLM Stats Score 49.7 to 36.8, ranking #15 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 GPT-5.3 Codex

  • you want predictable pricing at $1.75/M input and $14.00/M output

Choose Qwen3.8-Flash-Next

  • overall performance matters — it scores 49.7 and ranks #15 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
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

GPT-5.3 Codex
OpenAI
Qwen3.8-Flash-Next
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
36.8
#66
49.7
#15
Reasoning
37.5
#61
49.3
#15
Coding
30.1
#45
36.6
#18
Agents
22.8
#48
35.5
#13
Cost, coverage & limits
Benchmark wins
0 of 1
1 of 1
Input price
$1.75 / M
— / M
Output price
$14.00 / M
— / M
Context window
400,000
—

Individual benchmarks

6 reported for GPT-5.3 Codex · 22 for Qwen3.8-Flash-Next

1 shared

GPT-5.3 Codex outperforms in 0 benchmarks, while Qwen3.8-Flash-Next is better at 1 benchmark (SWE-Bench Pro).

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

Tue Sep 01 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only GPT-5.3 Codex specifies input context (400,000 tokens). Only GPT-5.3 Codex specifies output context (128,000 tokens).

OpenAI
GPT-5.3 Codex
Input400,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3.8-Flash-Next
Input- tokens
Output- tokens
Tue Sep 01 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both GPT-5.3 Codex and Qwen3.8-Flash-Next support multimodal inputs.

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

GPT-5.3 Codex

Text
Images
Audio
Video

Qwen3.8-Flash-Next

Text
Images
Audio
Video

License

Usage and distribution terms

GPT-5.3 Codex is licensed under a proprietary 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.

GPT-5.3 Codex

Proprietary

Closed source

Qwen3.8-Flash-Next

Qwen Community License 1.0

Open weights

Release Timeline

When each model was launched

GPT-5.3 Codex was released on 2026-02-05, while Qwen3.8-Flash-Next was released on 2026-08-26.

Qwen3.8-Flash-Next is 7 months newer than GPT-5.3 Codex.

GPT-5.3 Codex

Feb 5, 2026

6 months ago

Qwen3.8-Flash-Next

Aug 26, 2026

6 days ago

6mo 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 GPT-5.3 Codex and Qwen3.8-Flash-Next side-by-side, then vote on the output you prefer.

GPT-5.3 Codex
✓ Preferred
Qwen3.8-Flash-Next
Open in Playground

FAQ

Common questions about GPT-5.3 Codex vs Qwen3.8-Flash-Next.

Which is better, GPT-5.3 Codex or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next leads the LLM Stats Score 49.7 to 36.8. GPT-5.3 Codex is made by OpenAI 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 GPT-5.3 Codex compare to Qwen3.8-Flash-Next in benchmarks?

GPT-5.3 Codex scores SWE-Lancer (IC-Diamond subset): 81.4%, Cybersecurity CTFs: 77.6%, Terminal-Bench 2.0: 77.3%, LiveBench: 72.8%, OSWorld-Verified: 64.7%. 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 GPT-5.3 Codex and Qwen3.8-Flash-Next?

GPT-5.3 Codex supports 400K 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 GPT-5.3 Codex and Qwen3.8-Flash-Next?

Key differences include LLM Stats Score (36.8 vs 49.7), licensing (Proprietary vs Qwen Community License 1.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GPT-5.3 Codex and Qwen3.8-Flash-Next?

GPT-5.3 Codex is developed by OpenAI and Qwen3.8-Flash-Next is developed by Alibaba Cloud / Qwen Team.

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