GPT-5.6 Luna vs Qwen3.8 Flash
Qwen3.8 Flash leads the LLM Stats Score 48.7 to 44.9. Qwen3.8 Flash is 2.0x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Flash leads the overall LLM Stats Score 48.7 to 44.9, ranking #24 overall.
In the 6 individual benchmarks reported for both models, GPT-5.6 Luna wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8 Flash is roughly 2.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5.6 Luna also accepts a larger context window (1,050,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-5.6 Luna
- you value its reported benchmark strengths — it wins 4 of 6 exact shared results
- you process long inputs — it offers a 1,050,000 token context window
Choose Qwen3.8 Flash
- overall performance matters — it scores 48.7 and ranks #24 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 2.0x cheaper per token
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
45 reported for GPT-5.6 Luna · 22 for Qwen3.8 Flash
GPT-5.6 Luna outperforms in 4 benchmarks (DeepSWE 1.1, GPQA, OSWorld 2.0, SWE-Bench Pro), while Qwen3.8 Flash is better at 2 benchmarks (Agents' Last Exam, Toolathlon).
GPT-5.6 Luna shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-5.6 Luna ($0.20/1M tokens) is 1.3x more expensive than Qwen3.8 Flash ($0.15/1M tokens).
For output processing, GPT-5.6 Luna ($1.20/1M tokens) is 2.6x more expensive than Qwen3.8 Flash ($0.47/1M tokens).
In conclusion, GPT-5.6 Luna is more expensive than Qwen3.8 Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5.6 Luna accepts 1,050,000 input tokens compared to Qwen3.8 Flash's 1,000,000 tokens. Qwen3.8 Flash can generate longer responses up to 131,072 tokens, while GPT-5.6 Luna is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both GPT-5.6 Luna and Qwen3.8 Flash support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-5.6 Luna
Qwen3.8 Flash
License
Usage and distribution terms
Both models are licensed under proprietary licenses.
Both models have usage restrictions defined by their respective organizations.
Proprietary
Closed source
Proprietary
Closed source
Release Timeline
When each model was launched
GPT-5.6 Luna was released on 2026-07-09, while Qwen3.8 Flash was released on 2026-08-26.
Qwen3.8 Flash is 2 months newer than GPT-5.6 Luna.
Jul 9, 2026
2 months ago
Aug 26, 2026
3 weeks ago
1mo newerKnowledge Cutoff
When training data ends
GPT-5.6 Luna has a documented knowledge cutoff of 2026-02-16, while Qwen3.8 Flash's cutoff date is not specified.
We can confirm GPT-5.6 Luna's training data extends to 2026-02-16, but cannot make a direct comparison without Qwen3.8 Flash's cutoff date.
Feb 2026
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Provider Availability
GPT-5.6 Luna is available from OpenAI. Qwen3.8 Flash is available from Novita.
GPT-5.6 Luna
Qwen3.8 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5.6 Luna and Qwen3.8 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5.6 Luna vs Qwen3.8 Flash.