Qwen 3.8 vs GPT-5.6 Luna: 7x Price Gap, Same Context
Qwen 3.8 costs $2/$6 per million tokens, GPT-5.6 Luna $0.20/$1.20 - with identical context windows. What a real month costs on each.
👁 120 reads
Qwen 3.8 versus GPT-5.6 Luna is not a complete model specification: the Qwen variant and the API provider must also be named. GPT-5.6 Luna’s official standard rates remain $0.20 per million input tokens and $1.20 per million output tokens. Its long-input surcharge changes the comparison, so the historical monthly example below applies only to requests within the standard price tier.
For the newer Qwen release, I keep the availability and documentation in a separate guide.
The context window is not a flat-price allowance
OpenAI’s model page, checked on 9 November 2026, applies a higher rate to a request with more than 272,000 input tokens: twice the input rate and 1.5 times the output rate for the whole request. That makes the corresponding rates $0.40 and $1.80 per million tokens. Cache writes, cached reads and tools can add further distinctions.
I would record the exact endpoint, provider and input length before estimating a month of work. Open weights are a separate deployment choice; renting an API does not automatically buy portability for the application or make a very large model practical on a consumer GPU. The rates dated September below remain a historical comparison, not a newly verified Qwen quote.
Qwen 3.8 or GPT Luna?
First choose the exact Qwen model and the exact Luna generation. Then run a representative set of tasks with the same acceptance rules. Count retries and rejected answers in the bill. A lower token rate is useful only when the result meets the requirements.
Related guides: Model benchmark guide · Claude as another option · Compare coding workflows
Sources checked on 9 November 2026
Earlier comparison and examples
Two models keep coming up in the same shortlist: Qwen 3.8 and GPT-5.6 Luna. They sit in different places on the price list, and the gap is wider than most comparison tables admit. Here are the numbers, the cost of a realistic month of work, and the one question that decides it.
The numbers
Rates per million tokens, from our price snapshot of 9 September 2026. Vendors change these without notice, so treat the date as part of the figure.
| Model | Input | Output | Context |
|---|---|---|---|
| GPT-5.6 Luna | $0.20 | $1.20 | 1 050 000 |
| Qwen 3.8 (2.4T) | $2.00 | $6.00 | 1 048 576 |
Luna is ten times cheaper on input and five times cheaper on output. The context windows are effectively identical - the 1 424-token difference is noise, not a feature.
What a month actually costs
Take a workload that is easy to picture: a support assistant that reads 10 million tokens of tickets and documentation a month and writes 2 million tokens of replies.
| Model | Input (10M) | Output (2M) | Month |
|---|---|---|---|
| GPT-5.6 Luna | $2.00 | $2.40 | $4.40 |
| Qwen 3.8 (2.4T) | $20.00 | $12.00 | $32.00 |
Same job, seven times the bill. At small volumes nobody notices. At a hundred times that volume it is the difference between $440 and $3 200 a month, which is a hire.
About that “27B”
Most people typing qwen 3.8 27b are not shopping for an API at all - they are looking for a set of open weights small enough to run on their own hardware. That is a different question, and it is worth separating cleanly:
- Hosted Qwen 3.8 is the 2.4T mixture-of-experts model priced above. You rent it by the token, like any other API.
- Open-weight Qwen releases in the 20-30B range are downloaded and run locally. The token price is zero; you pay in GPU memory, electricity and your own time.
Our price list tracks hosted endpoints, so it has no row for a 27B local build - and we would rather say that than print a number we cannot source. If you are sizing local hardware, the figure that matters is VRAM, not dollars per million tokens: a 27B model in 4-bit quantisation needs roughly 16-20 GB to be comfortable, which is one consumer card, while the 2.4T hosted model is not something you run at home under any quantisation.
Which one to pick
Pick GPT-5.6 Luna if the work is high-volume and unremarkable: classification, extraction, summaries, first-line replies. This is where the price difference compounds, and where a cheaper model that is merely good enough wins on arithmetic alone.
Pick Qwen 3.8 if you need open weights, or if you need the option of moving the workload onto your own machines later. You are not paying seven times more for seven times the quality - you are paying for portability and for not being locked to one vendor’s roadmap. That can be worth every cent, but be honest with yourself about whether you will actually exercise it.
Do not pick either for work where output quality is the whole product - long-form reasoning, code that ships without review, legal or medical drafting. The models occupy different positions in their vendors’ line-ups. Evaluate the exact versions against your acceptance criteria before you commit.
Google · Your sourcesQuantslant higher in your Google - one clickAdd to preferred sources →The test that settles it in an afternoon
Take fifty real prompts from your own workload - not benchmark questions, your prompts. Run both models. Count how many outputs you would ship without editing. If the gap is under ten percentage points, the cheaper model wins, because the seven-fold price difference buys a lot of human review time.
That is the entire method, and it beats any leaderboard, because the only benchmark that predicts your bill is your own traffic.
If you use more than one model, my service Jantaris puts GPT-6 Sol, Claude Opus 5.5, Gemini 3.6 Flash, DeepSeek V4, Veo 3.1 and Nano Banana Pro on one prepaid USD balance with one API key - no subscription, the balance never expires and failed generations cost $0. Everything in one place: the Jantaris guide.
More on this from QUANTSLANT
ai tools·AI Image Generator Free: What You Get and Where Plans BeginFree tiers let you test AI image tools, but monthly limits arrive quickly and paid plans unlock serious creative work.