GPT-5.6 Luna vs Qwen3.8-27B
GPT-5.6 Luna and Qwen3.8-27B are closely matched at 45.3 and 45.2 on the LLM Stats Score. GPT-5.6 Luna is 2.3x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GPT-5.6 Luna and Qwen3.8-27B are closely matched on the overall LLM Stats Score at 45.3 and 45.2.
In the 5 individual benchmarks reported for both models, GPT-5.6 Luna wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-5.6 Luna is roughly 2.3x 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 5 of 5 exact shared results
- cost matters — it's about 2.3x cheaper per token
- you process long inputs — it offers a 1,050,000 token context window
Choose Qwen3.8-27B
- 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.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
45 reported for GPT-5.6 Luna · 26 for Qwen3.8-27B
GPT-5.6 Luna outperforms in 5 benchmarks (Agents' Last Exam, DeepSWE 1.1, GPQA, SWE-Bench Pro, Terminal-Bench 2.1), while Qwen3.8-27B is better at 0 benchmarks.
GPT-5.6 Luna significantly outperforms across most 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 2.0x cheaper than Qwen3.8-27B ($0.40/1M tokens).
For output processing, GPT-5.6 Luna ($1.20/1M tokens) is 2.5x cheaper than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Qwen3.8-27B is more expensive than GPT-5.6 Luna.*
* 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 Qwen3.8-27B's 262,144 tokens. Qwen3.8-27B can generate longer responses up to 262,144 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 Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-5.6 Luna
Qwen3.8-27B
License
Usage and distribution terms
GPT-5.6 Luna is licensed under a proprietary license, while Qwen3.8-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
GPT-5.6 Luna was released on 2026-07-09, while Qwen3.8-27B was released on 2026-08-14.
Qwen3.8-27B is 1 month newer than GPT-5.6 Luna.
Jul 9, 2026
2 months ago
Aug 14, 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 Qwen3.8-27B'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 Qwen3.8-27B's cutoff date.
Feb 2026
—
Provider Availability
GPT-5.6 Luna is available from OpenAI. Qwen3.8-27B is available from DeepInfra, FriendliAI.
GPT-5.6 Luna
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5.6 Luna and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5.6 Luna vs Qwen3.8-27B.