Model vs model
GPT-6 Luna vs Gemini 3.8 Flash
GPT-6 Luna and Gemini 3.8 Flash are both in llmwise. What each gets on every plan, what it reads, how it's served and what happens when its provider fails, from the catalog and the code that runs them.
Model prices and specs checked against OpenRouter's GPT-6 Luna page, OpenRouter's Gemini 3.8 Flash page. Updated .
Short answer
GPT-6 Luna is an everyday model (60 messages a day on Pro); Gemini 3.8 Flash draws on the monthly allowance (up to 250 messages a month on Pro). Beyond that, they read the same files and neither is easier to try. In our test runs, GPT-6 Luna passed 47 of the 50 prompts both answered and Gemini 3.8 Flash 46; 5 prompts split them, most on writing (5 to 3).
GPT-6 Luna vs Gemini 3.8 Flash, prompt by prompt
Every prompt GPT-6 Luna and Gemini 3.8 Flash both answered, compared directly, their biggest differences first. One run each, through OpenRouter: a wait depends on the provider and the load that day, so a lead under 10% counts as close.
Of the 50 prompts both answered, both passed 44, only GPT-6 Luna passed 3, only Gemini 3.8 Flash passed 2, and neither passed 1. GPT-6 Luna answered sooner on 43 of the 50 and Gemini 3.8 Flash on 6; the rest were within 10% of each other. The 50 replies cost $0.0059 on GPT-6 Luna and $0.0644 on Gemini 3.8 Flash: 10.9× less on GPT-6 Luna.
The 5 prompts only one of GPT-6 Luna and Gemini 3.8 Flash passed
Rewrite corporate jargon in plain words (writing): GPT-6 Luna passed and Gemini 3.8 Flash didn't. GPT-6 Luna: Graded 4.7 of 5 on average (lowest 4). Gemini 3.8 Flash: Graded 3.7 of 5 on average (lowest 3).
A product announcement with five rules (writing): GPT-6 Luna passed and Gemini 3.8 Flash didn't. GPT-6 Luna: Graded 4.0 of 5 on average (lowest 3). Gemini 3.8 Flash: Graded 3.7 of 5 on average (lowest 3).
Pens at 3 for $4 (math): Gemini 3.8 Flash passed and GPT-6 Luna didn't. GPT-6 Luna: Final answer 2 bundles and 4 singles for 12; expected 13.5. Gemini 3.8 Flash: Final answer 3 sets of 3 pens and 1 single pen for 13.50: right.
An article in three bullets (summarization): Gemini 3.8 Flash passed and GPT-6 Luna didn't. GPT-6 Luna: Graded 4.0 of 5 on average (lowest 3); but 61 words, over the 60 allowed. Gemini 3.8 Flash: Graded 4.3 of 5 on average (lowest 4).
Correlation between ad spend and sign-ups (data analysis): GPT-6 Luna passed and Gemini 3.8 Flash didn't. GPT-6 Luna: Final answer 0.97: right. Gemini 3.8 Flash: Final answer 0.91; expected 0.97.
Job by job, the widest gaps first
Writing: GPT-6 Luna passed 5 of 5 and Gemini 3.8 Flash 3 of 5. GPT-6 Luna answered 1.6× sooner at the median, 2.4 s against 3.9 s. GPT-6 Luna cost 13.3× less, $0.0005 against $0.0066 for the 5 replies. Gemini 3.8 Flash's replies ran 28% longer, in tokens of reply, thinking not counted.
Data analysis: GPT-6 Luna passed 5 of 5 and Gemini 3.8 Flash 4 of 5. GPT-6 Luna answered 2.2× sooner at the median, 2.5 s against 5.4 s. GPT-6 Luna cost 15.7× less, $0.0009 against $0.0138 for the 5 replies. Gemini 3.8 Flash's replies ran 482% longer, in tokens of reply, thinking not counted.
Math: GPT-6 Luna passed 4 of 5 and Gemini 3.8 Flash 5 of 5. GPT-6 Luna answered 2.1× sooner at the median, 2.2 s against 4.7 s. GPT-6 Luna cost 14.5× less, $0.0005 against $0.0071 for the 5 replies. Gemini 3.8 Flash's replies ran 134% longer, in tokens of reply, thinking not counted.
Summarization: GPT-6 Luna passed 4 of 5 and Gemini 3.8 Flash 5 of 5. GPT-6 Luna answered 2.5× sooner at the median, 1.5 s against 3.7 s. GPT-6 Luna cost 8.1× less, $0.0005 against $0.0038 for the 5 replies. Their replies ran to about the same length.
Customer support: GPT-6 Luna passed 4 of 5 and Gemini 3.8 Flash 4 of 5. GPT-6 Luna answered 2.6× sooner at the median, 1.4 s against 3.7 s. GPT-6 Luna cost 19.0× less, $0.0004 against $0.0082 for the 5 replies. Gemini 3.8 Flash's replies ran 101% longer, in tokens of reply, thinking not counted.
Agents and tool use: GPT-6 Luna passed 5 of 5 and Gemini 3.8 Flash 5 of 5. GPT-6 Luna answered 2.3× sooner at the median, 1.9 s against 4.4 s. GPT-6 Luna cost 10.7× less, $0.0004 against $0.0043 for the 5 replies. Gemini 3.8 Flash's replies ran 19% longer, in tokens of reply, thinking not counted.
RAG and answering from documents: GPT-6 Luna passed 5 of 5 and Gemini 3.8 Flash 5 of 5. GPT-6 Luna answered 2.5× sooner at the median, 1.3 s against 3.1 s. GPT-6 Luna cost 8.6× less, $0.0004 against $0.0033 for the 5 replies. Gemini 3.8 Flash's replies ran 40% longer, in tokens of reply, thinking not counted.
SQL: GPT-6 Luna passed 5 of 5 and Gemini 3.8 Flash 5 of 5. GPT-6 Luna answered 3.1× sooner at the median, 1.2 s against 3.7 s. GPT-6 Luna cost 8.4× less, $0.0004 against $0.0037 for the 5 replies. Gemini 3.8 Flash's replies ran 15% longer, in tokens of reply, thinking not counted.
Translation: GPT-6 Luna passed 5 of 5 and Gemini 3.8 Flash 5 of 5. GPT-6 Luna answered 1.9× sooner at the median, 1.6 s against 3.0 s. GPT-6 Luna cost 8.1× less, $0.0005 against $0.0037 for the 5 replies. Their replies ran to about the same length.
Coding: GPT-6 Luna passed 5 of 5 and Gemini 3.8 Flash 5 of 5. Their median waits were close, 4.6 s against 4.2 s. GPT-6 Luna cost 6.8× less, $0.0014 against $0.0098 for the 5 replies. Gemini 3.8 Flash's replies ran 72% longer, in tokens of reply, thinking not counted.
All 50 prompts: who passed, who answered sooner, who cost less
| Prompt | Result | Sooner | Cheaper |
|---|---|---|---|
| Turn a title into a URL slug | Both passed | GPT-6 Luna, 1.8×took 2.3 s and 4.2 s | GPT-6 Luna, 10.1×cost $0.0001 and $0.0009 |
| Parse a duration like “1h 30m” | Both passed | Gemini 3.8 Flash, 2.0×took 4.6 s and 2.3 s | GPT-6 Luna, 4.1×cost $0.0003 and $0.0010 |
| Merge overlapping intervals | Both passed | GPT-6 Luna, 2.6×took 1.5 s and 3.8 s | GPT-6 Luna, 11.1×cost $0.0001 and $0.0012 |
| Evaluate an arithmetic expression, no eval | Both passed | Gemini 3.8 Flash, 1.3×took 8.8 s and 6.7 s | GPT-6 Luna, 8.0×cost $0.0006 and $0.0045 |
| Parse CSV with quoted fields | Both passed | Gemini 3.8 Flash, 1.4×took 7.2 s and 5.0 s | GPT-6 Luna, 5.1×cost $0.0004 and $0.0022 |
| Announce a second bakery shop on LinkedIn | Both passed | GPT-6 Luna, 1.3×took 2.9 s and 3.9 s | GPT-6 Luna, 8.1×cost $0.0001 and $0.0009 |
| Rewrite corporate jargon in plain words | Only GPT-6 Luna | GPT-6 Luna, 1.3×took 2.4 s and 3.1 s | GPT-6 Luna, 6.2×cost $0.0001 and $0.0006 |
| Decline a meeting and offer two times | Both passed | GPT-6 Luna, 2.8×took 1.3 s and 3.5 s | GPT-6 Luna, 10.8×cost $0.0001 and $0.0008 |
| A product announcement with five rules | Only GPT-6 Luna | GPT-6 Luna, 5.2×took 1.5 s and 7.6 s | GPT-6 Luna, 40.8×cost $0.0001 and $0.0031 |
| Argue both sides of free buses | Both passed | GPT-6 Luna, 2.1×took 2.5 s and 5.2 s | GPT-6 Luna, 8.6×cost $0.0001 and $0.0011 |
| A discount, then sales tax | Both passed | Gemini 3.8 Flash, 1.2×took 3.1 s and 2.5 s | GPT-6 Luna, 8.9×cost $0.0001 and $0.0007 |
| Pens at 3 for $4 | Only Gemini 3.8 Flash | GPT-6 Luna, 1.6×took 2.9 s and 4.7 s | GPT-6 Luna, 11.9×cost $0.0001 and $0.0017 |
| Compound interest over three years | Both passed | Closetook 2.0 s and 2.1 s | GPT-6 Luna, 11.0×cost $0.0001 and $0.0009 |
| Four-digit numbers whose digits sum to 9 | Both passed | GPT-6 Luna, 2.4×took 2.0 s and 4.7 s | GPT-6 Luna, 21.3×cost $0.0001 and $0.0022 |
| The highest of three dice is a 5 | Both passed | GPT-6 Luna, 2.2×took 2.2 s and 4.9 s | GPT-6 Luna, 18.3×cost $0.0001 and $0.0018 |
| An article in three bullets | Only Gemini 3.8 Flash | GPT-6 Luna, 2.5×took 1.5 s and 3.7 s | GPT-6 Luna, 7.4×cost $0.0001 and $0.0007 |
| An email thread in one sentence | Both passed | GPT-6 Luna, 3.6×took 1.1 s and 3.8 s | GPT-6 Luna, 7.9×cost $0.0001 and $0.0005 |
| Decisions and action items from a meeting | Both passed | GPT-6 Luna, 2.2×took 1.5 s and 3.2 s | GPT-6 Luna, 9.2×cost $0.0001 and $0.0009 |
| A quarterly memo for the CEO | Both passed | GPT-6 Luna, 2.6×took 1.5 s and 4.0 s | GPT-6 Luna, 7.5×cost $0.0001 and $0.0009 |
| A study with a negative result | Both passed | GPT-6 Luna, 2.7×took 1.3 s and 3.6 s | GPT-6 Luna, 8.4×cost $0.0001 and $0.0007 |
| The region with the most revenue | Both passed | GPT-6 Luna, 2.9×took 2.5 s and 7.4 s | GPT-6 Luna, 28.8×cost $0.0001 and $0.0038 |
| Average order value in August | Both passed | GPT-6 Luna, 1.7×took 2.6 s and 4.4 s | GPT-6 Luna, 12.3×cost $0.0001 and $0.0016 |
| Revenue change from July to August | Both passed | GPT-6 Luna, 2.7×took 2.5 s and 6.6 s | GPT-6 Luna, 27.6×cost $0.0001 and $0.0037 |
| A median, filtered two ways | Both passed | GPT-6 Luna, 1.4×took 2.5 s and 3.5 s | GPT-6 Luna, 9.1×cost $0.0001 and $0.0010 |
| Correlation between ad spend and sign-ups | Only GPT-6 Luna | Gemini 3.8 Flash, 1.2×took 6.7 s and 5.4 s | GPT-6 Luna, 10.0×cost $0.0004 and $0.0037 |
| A late order | Both passed | GPT-6 Luna, 2.0×took 1.6 s and 3.1 s | GPT-6 Luna, 10.1×cost $0.0001 and $0.0009 |
| A return inside the window | Both passed | GPT-6 Luna, 2.3×took 1.2 s and 2.9 s | GPT-6 Luna, 10.1×cost $0.0001 and $0.0009 |
| A frustrated customer | Neither passed | GPT-6 Luna, 2.6×took 1.4 s and 3.7 s | GPT-6 Luna, 13.2×cost $0.0001 and $0.0011 |
| A refund request outside the window | Both passed | GPT-6 Luna, 4.5×took 1.3 s and 5.9 s | GPT-6 Luna, 34.4×cost $0.0001 and $0.0029 |
| A message with a planted instruction | Both passed | GPT-6 Luna, 4.8×took 1.5 s and 7.3 s | GPT-6 Luna, 27.3×cost $0.0001 and $0.0025 |
| A delivery message into Spanish | Both passed | GPT-6 Luna, 1.5×took 2.6 s and 3.7 s | GPT-6 Luna, 8.2×cost $0.0001 and $0.0008 |
| A product description into French | Both passed | GPT-6 Luna, 1.6×took 1.4 s and 2.2 s | GPT-6 Luna, 8.2×cost $0.0001 and $0.0008 |
| A meeting note into German | Both passed | GPT-6 Luna, 1.7×took 1.7 s and 3.0 s | GPT-6 Luna, 7.8×cost $0.0001 and $0.0006 |
| Idioms into natural Japanese | Both passed | GPT-6 Luna, 3.0×took 1.6 s and 4.7 s | GPT-6 Luna, 7.9×cost $0.0001 and $0.0008 |
| A lease clause into Brazilian Portuguese | Both passed | GPT-6 Luna, 1.4×took 1.5 s and 2.0 s | GPT-6 Luna, 8.2×cost $0.0001 and $0.0008 |
| Customers in one country | Both passed | GPT-6 Luna, 3.1×took 1.0 s and 3.1 s | GPT-6 Luna, 7.9×cost $0.0001 and $0.0005 |
| Count orders by status | Both passed | GPT-6 Luna, 4.2×took 0.9 s and 3.7 s | GPT-6 Luna, 8.1×cost $0.0001 and $0.0005 |
| Revenue by category | Both passed | GPT-6 Luna, 2.5×took 1.2 s and 3.0 s | GPT-6 Luna, 8.4×cost $0.0001 and $0.0008 |
| Every customer, even those without orders | Both passed | GPT-6 Luna, 1.5×took 2.4 s and 3.7 s | GPT-6 Luna, 8.0×cost $0.0001 and $0.0008 |
| Monthly revenue with a running total | Both passed | GPT-6 Luna, 2.6×took 1.8 s and 4.7 s | GPT-6 Luna, 8.9×cost $0.0001 and $0.0012 |
| A fact from one section | Both passed | GPT-6 Luna, 2.5×took 1.3 s and 3.1 s | GPT-6 Luna, 7.8×cost $0.0001 and $0.0006 |
| Core hours and start times | Both passed | GPT-6 Luna, 3.0×took 1.0 s and 2.9 s | GPT-6 Luna, 8.2×cost $0.0001 and $0.0006 |
| Two sections in one answer | Both passed | GPT-6 Luna, 2.3×took 1.4 s and 3.1 s | GPT-6 Luna, 8.2×cost $0.0001 and $0.0006 |
| A later amendment changes the answer | Both passed | GPT-6 Luna, 4.1×took 1.0 s and 4.1 s | GPT-6 Luna, 10.6×cost $0.0001 and $0.0009 |
| A question the handbook doesn't answer | Both passed | GPT-6 Luna, 1.5×took 1.9 s and 2.9 s | GPT-6 Luna, 7.8×cost $0.0001 and $0.0006 |
| Pick the tool and work out the date | Both passed | Gemini 3.8 Flash, 1.1×took 2.1 s and 1.9 s | GPT-6 Luna, 6.8×cost $0.0001 and $0.0005 |
| Convert a currency | Both passed | GPT-6 Luna, 3.9×took 1.1 s and 4.4 s | GPT-6 Luna, 11.5×cost $0.0001 and $0.0008 |
| Book a meeting from a sentence | Both passed | GPT-6 Luna, 5.1×took 1.2 s and 6.3 s | GPT-6 Luna, 13.5×cost $0.0001 and $0.0012 |
| Search, but don't book | Both passed | GPT-6 Luna, 2.9×took 2.2 s and 6.3 s | GPT-6 Luna, 10.6×cost $0.0001 and $0.0009 |
| Two calls with a unit conversion | Both passed | GPT-6 Luna, 2.0×took 1.9 s and 3.9 s | GPT-6 Luna, 10.6×cost $0.0001 and $0.0010 |
GPT-6 Luna vs Gemini 3.8 Flash in our test runs
GPT-6 Luna and Gemini 3.8 Flash on the same prompts, job by job: how many replies passed their check.
Based on 100 of our test runs on , through OpenRouter with the app's own prompt and settings. Updated .
| Job | GPT-6 Luna | Gemini 3.8 Flash |
|---|---|---|
| Coding | 5 of 5 | 5 of 5 |
| Writing | 5 of 5 | 3 of 5 |
| Math | 4 of 5 | 5 of 5 |
| Summarization | 4 of 5 | 5 of 5 |
| Data analysis | 5 of 5 | 4 of 5 |
| Customer support | 4 of 5 | 4 of 5 |
| Translation | 5 of 5 | 5 of 5 |
| SQL | 5 of 5 | 5 of 5 |
| RAG and answering from documents | 5 of 5 | 5 of 5 |
| Agents and tool use | 5 of 5 | 5 of 5 |
| All jobs | 47 of 50 · 1 message each · $0.0001 a reply | 46 of 50 · 1 message each · $0.0013 a reply |
The same prompts, sent to both through llmwise's own pipeline and checked the same way: five for each job. A bold count is the one that passed more. Messages: what each reply counted as on Pro. Cost: what OpenRouter charged us per reply, on average. Every prompt, and how it's scored.
Where they differed most, writing: both replies to a prompt only one of them passed.
An everyday prompt: “Rewrite corporate jargon in plain words”, in full.
GPT-6 Luna
Passed: Graded 4.7 of 5 on average (lowest 4).
In Q3, teams across the company worked together to focus more on customers. Retention measures improved by 12%, and the rate of customer loss fell by 0.3 percentage points in the enterprise segment. The company also adjusted its customer contact channels to make better use of available capacity.
417 tokens in, 124 out (59 of them reasoning) · 2.4 s · $0.0001 · 1 message on Pro · answered by openai/gpt-6-luna via OpenAI ·
Gemini 3.8 Flash
Failed: Graded 3.7 of 5 on average (lowest 3).
By having different teams work together in the third quarter, we refocused our business on customer needs. This effort paid off: client retention rates increased by 12%, and the rate at which enterprise clients canceled dropped by 0.30% (30 basis points). At the same time, we streamlined and balanced our communication channels to make better use of our team’s time and resources.
447 tokens in, 81 out · 3.1 s · $0.0006 · 1 message on Pro · answered by google/gemini-3.8-flash via Google ·
GPT-6 Luna and Gemini 3.8 Flash on every plan
Whether the one-time free trial reaches each model, then each paid plan's messages on it.
| Plan | Price | GPT-6 Luna | Gemini 3.8 Flash |
|---|---|---|---|
| Free | $0 | In the one-time trial of 5 messages | In the one-time trial of 5 messages |
| Pro | $20 a month | 60 a day | Up to 250 a month |
| Max | $50 a month | 120 a day | Up to 800 a month |
| Ultra | $100 a month | 200 a day | Up to 1,800 a month |
| Studio | $200 a month | 200 a day | Up to 4,000 a month |
Prices don't include tax, which is added where it applies and shown before you pay. A paid plan's month is one allowance shared by every model, so each monthly count is the most you get if all of it goes to that model. It renews each billing period; everyday models refill daily at 00:00 UTC. Long chats count more per reply. How pricing works.
Every limit is published. Paid plans also have a monthly fair-use limit on AI cost: Pro $7.50, Max $20, Ultra $42, Studio $85. Using every message on your plan at typical sizes stays under it; very large messages and heavy research use it faster. Every limit, explained.
What differs
Messages on Pro
GPT-6 Luna is an everyday model (60 messages a day on Pro); Gemini 3.8 Flash draws on the monthly allowance (up to 250 messages a month on Pro).
Context window
Both take up to 1.05M tokens of context. A chat in llmwise holds up to 200k tokens, which fits in either, so the difference shows only through each maker's own API.
Images and PDFs
Both read images. Both take a PDF as the whole file, pages and all.
On Free
Both are in the free trial.
Where messages go
GPT-6 Luna: Sent to OpenAI directly. Gemini 3.8 Flash: Sent to Google directly.
Fact by fact
| Fact | GPT-6 Luna | Gemini 3.8 Flash |
|---|---|---|
| Context window | 1.05M tokens | 1.05M tokens |
| Reads images | Yes | Yes |
| PDFs | Whole file | Whole file |
| Reasoning | Yes | Yes |
| API price (September 2026) | $0.10 in / $0.50 out per million tokens | $0.75 in / $3.75 out per million tokens |
| A typical message at API prices (4,000 tokens in, 700 out) | $0.0008 | $0.0056 |
| A $10 top-up adds | Nothing: an everyday model's count is daily | 200 messages |
| Where a message goes | Sent to OpenAI directly. | Sent to Google directly. |
| If the provider fails | If OpenAI fails before the reply starts (an overload, a server error, a dropped connection), llmwise sends the same request to GPT-6 Luna through OpenRouter instead. | If Google fails before the reply starts (an overload, a server error, a dropped connection), llmwise sends the same request to Gemini 3.8 Flash through OpenRouter instead. |
| Anthropic's safety fallback | Doesn't apply | Doesn't apply |
GPT-6 Luna or Gemini 3.8 Flash?
From the facts above and our test runs: the rest is how their answers suit your work, which one chat can show you.
Pick GPT-6 Luna: its messages come from the daily count (60 messages a day on Pro), so they leave the monthly allowance for bigger models.
Each model's page, the families, and other pairs
GPT-6 Luna vs Gemini 3.8 Flash is one pair of models. The page below covers the whole families.
- GPT-6 Luna: price, limits and messages on every plan
- Gemini 3.8 Flash: price, limits and messages on every plan
- ChatGPT vs Gemini
- GPT-6 Luna vs DeepSeek V4.1 Flash
- GPT-6 Sol vs GPT-6 Luna
- Claude Haiku 4.5 vs GPT-6 Luna
- Gemini 3.8 Flash vs DeepSeek V4.1 Flash
- Claude Sonnet 5 vs Gemini 3.8 Flash
- Claude Haiku 4.5 vs Gemini 3.8 Flash
- Every model-vs-model page
GPT-6 Luna is a GPT model; Gemini 3.8 Flash is a Gemini model.
Questions
Is GPT-6 Luna or Gemini 3.8 Flash cheaper in llmwise?
GPT-6 Luna is an everyday model (60 messages a day on Pro); Gemini 3.8 Flash draws on the monthly allowance (up to 250 messages a month on Pro). Every paid plan's monthly allowance is shared by all models, so each count is the most you get if it all goes to that model.
Can I try GPT-6 Luna and Gemini 3.8 Flash for free?
Yes: both are in the free trial of 5 messages.
Which has the bigger context window, GPT-6 Luna or Gemini 3.8 Flash?
Neither: both take 1.05M tokens. A chat in llmwise holds up to 200k tokens, which fits in either, so the difference shows only through each maker's own API.
Can I use GPT-6 Luna and Gemini 3.8 Flash in the same chat?
Yes. Pick GPT-6 Luna for one message and Gemini 3.8 Flash for the next; the second sees the whole chat, including the first one's answer.
Which did better in your test runs, GPT-6 Luna or Gemini 3.8 Flash?
On the same 50 prompts, run on September 27, 2026, GPT-6 Luna passed 47 and Gemini 3.8 Flash passed 46. The table on this page has each job, and every reply is published.
Claude, GPT, Gemini, DeepSeek, Grok, Kimi, and GLM, in one chat.
See what a message costs before you send it. Free is 5 messages to try; sign in with an email link, no password or card.