Model vs model
GPT-6 Luna vs GLM 5.3
GPT-6 Luna and GLM 5.3 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 GLM 5.3 page. Updated .
Short answer
GPT-6 Luna is an everyday model (60 messages a day on Pro); GLM 5.3 draws on the monthly allowance (up to 250 messages a month on Pro). Otherwise, only GPT-6 Luna reads images and only GPT-6 Luna reads a PDF as the whole file. In our test runs, GPT-6 Luna passed 47 of the 50 prompts both answered and GLM 5.3 48; 3 prompts split them, most on writing (5 to 4).
GPT-6 Luna vs GLM 5.3, prompt by prompt
Every prompt GPT-6 Luna and GLM 5.3 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 46, only GPT-6 Luna passed 1, only GLM 5.3 passed 2, and neither passed 1. GPT-6 Luna answered sooner on 13 of the 50 and GLM 5.3 on 29; the rest were within 10% of each other. The 50 replies cost $0.0059 on GPT-6 Luna and $0.0364 on GLM 5.3: 6.2× less on GPT-6 Luna.
The 3 prompts only one of GPT-6 Luna and GLM 5.3 passed
Argue both sides of free buses (writing): GPT-6 Luna passed and GLM 5.3 didn't. GPT-6 Luna: Graded 4.3 of 5 on average (lowest 4). GLM 5.3: Graded 3.7 of 5 on average (lowest 3); but a paragraph of 92 words, over the 90 allowed.
Pens at 3 for $4 (math): GLM 5.3 passed and GPT-6 Luna didn't. GPT-6 Luna: Final answer 2 bundles and 4 singles for 12; expected 13.5. GLM 5.3: Final answer 13.50 (three 3-for- 4 packs plus one single pen): right.
A frustrated customer (customer support): GLM 5.3 passed and GPT-6 Luna didn't. GPT-6 Luna: Graded 3.0 of 5 on average (lowest 2). GLM 5.3: Graded 4.7 of 5 on average (lowest 4).
Job by job, the widest gaps first
Writing: GPT-6 Luna passed 5 of 5 and GLM 5.3 4 of 5. GLM 5.3 answered 1.4× sooner at the median, 2.4 s against 1.8 s. GPT-6 Luna cost 5.8× less, $0.0005 against $0.0029 for the 5 replies. GLM 5.3's replies ran 26% longer, in tokens of reply, thinking not counted.
Customer support: GPT-6 Luna passed 4 of 5 and GLM 5.3 5 of 5. GPT-6 Luna answered 1.6× sooner at the median, 1.4 s against 2.3 s. GPT-6 Luna cost 5.4× less, $0.0004 against $0.0023 for the 5 replies. GLM 5.3's replies ran 97% longer, in tokens of reply, thinking not counted.
Math: GPT-6 Luna passed 4 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 2.3× sooner at the median, 2.2 s against 0.9 s. GPT-6 Luna cost 3.6× less, $0.0005 against $0.0018 for the 5 replies. GLM 5.3's replies ran 19% longer, in tokens of reply, thinking not counted.
Data analysis: GPT-6 Luna passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 1.3× sooner at the median, 2.5 s against 1.9 s. GPT-6 Luna cost 10.9× less, $0.0009 against $0.0096 for the 5 replies. GLM 5.3's replies ran 98% longer, in tokens of reply, thinking not counted.
Translation: GPT-6 Luna passed 5 of 5 and GLM 5.3 5 of 5. Their median waits were close, 1.6 s against 1.6 s. GPT-6 Luna cost 7.6× less, $0.0005 against $0.0035 for the 5 replies. GLM 5.3's replies ran 60% longer, in tokens of reply, thinking not counted.
RAG and answering from documents: GPT-6 Luna passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 2.0× sooner at the median, 1.3 s against 0.6 s. GPT-6 Luna cost 6.5× less, $0.0004 against $0.0025 for the 5 replies. Their replies ran to about the same length.
Summarization: GPT-6 Luna passed 4 of 5 and GLM 5.3 4 of 5. GLM 5.3 answered 1.3× sooner at the median, 1.5 s against 1.1 s. GPT-6 Luna cost 5.8× less, $0.0005 against $0.0027 for the 5 replies. Their replies ran to about the same length.
Agents and tool use: GPT-6 Luna passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 2.1× sooner at the median, 1.9 s against 0.9 s. GPT-6 Luna cost 5.5× less, $0.0004 against $0.0022 for the 5 replies. Their replies ran to about the same length.
Coding: GPT-6 Luna passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 4.3× sooner at the median, 4.6 s against 1.1 s. GPT-6 Luna cost 5.1× less, $0.0014 against $0.0073 for the 5 replies. Their replies ran to about the same length.
SQL: GPT-6 Luna passed 5 of 5 and GLM 5.3 5 of 5. Their median waits were close, 1.2 s against 1.2 s. GPT-6 Luna cost 3.5× less, $0.0004 against $0.0016 for the 5 replies. GPT-6 Luna's replies ran 14% 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 | GLM 5.3, 5.3×took 2.3 s and 0.4 s | GPT-6 Luna, 12.0×cost $0.0001 and $0.0010 |
| Parse a duration like “1h 30m” | Both passed | GLM 5.3, 4.3×took 4.6 s and 1.1 s | GPT-6 Luna, 5.6×cost $0.0003 and $0.0014 |
| Merge overlapping intervals | Both passed | GLM 5.3, 1.8×took 1.5 s and 0.8 s | GPT-6 Luna, 7.4×cost $0.0001 and $0.0008 |
| Evaluate an arithmetic expression, no eval | Both passed | GPT-6 Luna, 1.4×took 8.8 s and 12.5 s | GPT-6 Luna, 3.0×cost $0.0006 and $0.0017 |
| Parse CSV with quoted fields | Both passed | GLM 5.3, 2.7×took 7.2 s and 2.6 s | GPT-6 Luna, 5.6×cost $0.0004 and $0.0024 |
| Announce a second bakery shop on LinkedIn | Both passed | Closetook 2.9 s and 2.9 s | GPT-6 Luna, 3.3×cost $0.0001 and $0.0004 |
| Rewrite corporate jargon in plain words | Both passed | GLM 5.3, 1.4×took 2.4 s and 1.8 s | GPT-6 Luna, 1.9×cost $0.0001 and $0.0002 |
| Decline a meeting and offer two times | Both passed | GPT-6 Luna, 1.1×took 1.3 s and 1.4 s | GPT-6 Luna, 3.7×cost $0.0001 and $0.0003 |
| A product announcement with five rules | Both passed | GLM 5.3, 1.7×took 1.5 s and 0.8 s | GPT-6 Luna, 10.0×cost $0.0001 and $0.0008 |
| Argue both sides of free buses | Only GPT-6 Luna | GLM 5.3, 1.4×took 2.5 s and 1.8 s | GPT-6 Luna, 9.6×cost $0.0001 and $0.0013 |
| A discount, then sales tax | Both passed | GLM 5.3, 3.3×took 3.1 s and 0.9 s | GPT-6 Luna, 2.7×cost $0.0001 and $0.0002 |
| Pens at 3 for $4 | Only GLM 5.3 | GLM 5.3, 1.9×took 2.9 s and 1.5 s | GPT-6 Luna, 1.3×cost $0.0001 and $0.0002 |
| Compound interest over three years | Both passed | GLM 5.3, 3.5×took 2.0 s and 0.6 s | GPT-6 Luna, 6.9×cost $0.0001 and $0.0005 |
| Four-digit numbers whose digits sum to 9 | Both passed | Closetook 2.0 s and 1.9 s | GPT-6 Luna, 2.5×cost $0.0001 and $0.0003 |
| The highest of three dice is a 5 | Both passed | GLM 5.3, 2.6×took 2.2 s and 0.9 s | GPT-6 Luna, 6.0×cost $0.0001 and $0.0006 |
| An article in three bullets | Neither passed | GPT-6 Luna, 1.9×took 1.5 s and 2.8 s | GPT-6 Luna, 4.5×cost $0.0001 and $0.0004 |
| An email thread in one sentence | Both passed | Closetook 1.1 s and 1.0 s | GPT-6 Luna, 3.5×cost $0.0001 and $0.0002 |
| Decisions and action items from a meeting | Both passed | Closetook 1.5 s and 1.5 s | GPT-6 Luna, 2.3×cost $0.0001 and $0.0002 |
| A quarterly memo for the CEO | Both passed | GLM 5.3, 1.4×took 1.5 s and 1.1 s | GPT-6 Luna, 8.2×cost $0.0001 and $0.0010 |
| A study with a negative result | Both passed | GLM 5.3, 1.6×took 1.3 s and 0.8 s | GPT-6 Luna, 10.4×cost $0.0001 and $0.0008 |
| The region with the most revenue | Both passed | GLM 5.3, 1.2×took 2.5 s and 2.1 s | GPT-6 Luna, 3.0×cost $0.0001 and $0.0004 |
| Average order value in August | Both passed | GLM 5.3, 1.4×took 2.6 s and 1.9 s | GPT-6 Luna, 2.8×cost $0.0001 and $0.0004 |
| Revenue change from July to August | Both passed | GLM 5.3, 1.8×took 2.5 s and 1.4 s | GPT-6 Luna, 11.8×cost $0.0001 and $0.0016 |
| A median, filtered two ways | Both passed | GLM 5.3, 2.6×took 2.5 s and 1.0 s | GPT-6 Luna, 9.2×cost $0.0001 and $0.0011 |
| Correlation between ad spend and sign-ups | Both passed | GLM 5.3, 1.2×took 6.7 s and 5.6 s | GPT-6 Luna, 16.7×cost $0.0004 and $0.0062 |
| A late order | Both passed | GPT-6 Luna, 1.7×took 1.6 s and 2.6 s | GPT-6 Luna, 4.3×cost $0.0001 and $0.0004 |
| A return inside the window | Both passed | GPT-6 Luna, 1.3×took 1.2 s and 1.6 s | GPT-6 Luna, 2.8×cost $0.0001 and $0.0002 |
| A frustrated customer | Only GLM 5.3 | GPT-6 Luna, 1.6×took 1.4 s and 2.3 s | GPT-6 Luna, 3.8×cost $0.0001 and $0.0003 |
| A refund request outside the window | Both passed | GLM 5.3, 1.1×took 1.3 s and 1.1 s | GPT-6 Luna, 11.0×cost $0.0001 and $0.0009 |
| A message with a planted instruction | Both passed | GPT-6 Luna, 3.5×took 1.5 s and 5.3 s | GPT-6 Luna, 5.2×cost $0.0001 and $0.0005 |
| A delivery message into Spanish | Both passed | GLM 5.3, 1.7×took 2.6 s and 1.5 s | GPT-6 Luna, 2.4×cost $0.0001 and $0.0002 |
| A product description into French | Both passed | Closetook 1.4 s and 1.5 s | GPT-6 Luna, 3.2×cost $0.0001 and $0.0003 |
| A meeting note into German | Both passed | Closetook 1.7 s and 1.6 s | GPT-6 Luna, 2.3×cost $0.0001 and $0.0002 |
| Idioms into natural Japanese | Both passed | GPT-6 Luna, 1.1×took 1.6 s and 1.7 s | GPT-6 Luna, 11.8×cost $0.0001 and $0.0012 |
| A lease clause into Brazilian Portuguese | Both passed | GPT-6 Luna, 1.1×took 1.5 s and 1.7 s | GPT-6 Luna, 16.7×cost $0.0001 and $0.0016 |
| Customers in one country | Both passed | GLM 5.3, 1.3×took 1.0 s and 0.8 s | GPT-6 Luna, 2.2×cost $0.0001 and $0.0001 |
| Count orders by status | Both passed | GPT-6 Luna, 1.3×took 0.9 s and 1.2 s | GPT-6 Luna, 2.2×cost $0.0001 and $0.0001 |
| Revenue by category | Both passed | Closetook 1.2 s and 1.2 s | GPT-6 Luna, 2.3×cost $0.0001 and $0.0002 |
| Every customer, even those without orders | Both passed | GLM 5.3, 2.5×took 2.4 s and 1.0 s | GPT-6 Luna, 7.3×cost $0.0001 and $0.0007 |
| Monthly revenue with a running total | Both passed | GPT-6 Luna, 1.8×took 1.8 s and 3.2 s | GPT-6 Luna, 2.8×cost $0.0001 and $0.0004 |
| A fact from one section | Both passed | GLM 5.3, 1.4×took 1.3 s and 0.9 s | GPT-6 Luna, 3.6×cost $0.0001 and $0.0003 |
| Core hours and start times | Both passed | GLM 5.3, 2.0×took 1.0 s and 0.5 s | GPT-6 Luna, 9.1×cost $0.0001 and $0.0007 |
| Two sections in one answer | Both passed | GLM 5.3, 2.8×took 1.4 s and 0.5 s | GPT-6 Luna, 5.8×cost $0.0001 and $0.0005 |
| A later amendment changes the answer | Both passed | GPT-6 Luna, 1.3×took 1.0 s and 1.3 s | GPT-6 Luna, 7.2×cost $0.0001 and $0.0006 |
| A question the handbook doesn't answer | Both passed | GLM 5.3, 3.0×took 1.9 s and 0.6 s | GPT-6 Luna, 6.5×cost $0.0001 and $0.0005 |
| Pick the tool and work out the date | Both passed | GLM 5.3, 2.4×took 2.1 s and 0.9 s | GPT-6 Luna, 2.9×cost $0.0001 and $0.0002 |
| Convert a currency | Both passed | Closetook 1.1 s and 1.1 s | GPT-6 Luna, 2.1×cost $0.0001 and $0.0001 |
| Book a meeting from a sentence | Both passed | GPT-6 Luna, 1.4×took 1.2 s and 1.7 s | GPT-6 Luna, 6.3×cost $0.0001 and $0.0006 |
| Search, but don't book | Both passed | GLM 5.3, 4.0×took 2.2 s and 0.5 s | GPT-6 Luna, 6.4×cost $0.0001 and $0.0005 |
| Two calls with a unit conversion | Both passed | GLM 5.3, 2.7×took 1.9 s and 0.7 s | GPT-6 Luna, 8.2×cost $0.0001 and $0.0008 |
GPT-6 Luna vs GLM 5.3 in our test runs
GPT-6 Luna and GLM 5.3 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 | GLM 5.3 |
|---|---|---|
| Coding | 5 of 5 | 5 of 5 |
| Writing | 5 of 5 | 4 of 5 |
| Math | 4 of 5 | 5 of 5 |
| Summarization | 4 of 5 | 4 of 5 |
| Data analysis | 5 of 5 | 5 of 5 |
| Customer support | 4 of 5 | 5 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 | 48 of 50 · 1 message each · $0.0007 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.
A hard prompt: “Argue both sides of free buses”, in full.
GPT-6 Luna
Passed: Graded 4.3 of 5 on average (lowest 4).
Making buses free would give every resident reliable access to jobs, school, health care, and daily necessities, regardless of income. It would also speed boarding, reduce fare-enforcement costs, and encourage people to leave cars at home—easing congestion, pollution, and parking pressure. A city that treats public transit as essential infrastructure, rather than a service people must qualify to afford, can build a more connected and equitable community. But “free” buses still have to be paid for, and the bill could divert money from more urgent improvements: frequent service, safer stops, cleaner vehicles, and routes to underserved neighborhoods. If buses remain slow, crowded, or unreliable, removing fares may do little to persuade drivers to switch, while reducing funds that could make transit genuinely useful. Targeted fare assistance can protect riders who need it most without subsidizing every trip, including those by people who can readily afford to pay.
384 tokens in, 186 out · 2.5 s · $0.0001 · 1 message on Pro · answered by openai/gpt-6-luna via OpenAI ·
GLM 5.3
Failed: Graded 3.7 of 5 on average (lowest 3); but a paragraph of 92 words, over the 90 allowed.
A city should make its buses free because the benefits ripple far beyond riders. Fare-free transit boosts ridership immediately, cutting car trips, congestion, and emissions while improving air quality. It saves low-income residents money, connects workers to jobs, students to schools, and seniors to care, and it eliminates fare enforcement disputes that disproportionately harm marginalized riders. Fares often recover only a modest share of operating costs anyway, so removing them trades a small revenue stream for a faster, fairer, more accessible city — a genuine public good like roads and libraries. Yet free buses carry real costs that advocates understate. Fare revenue funds service; eliminate it and the burden shifts to taxpayers, many of whom never ride, straining budgets already stretched thin. Free buses can overload the system — especially with vulnerable riders seeking shelter — degrading the experience for commuters who depend on reliability. Fare-free transit can also reduce pressure to improve service quality, since riders are no longer paying customers with leverage. And studies suggest it mainly attracts walkers and cyclists, not drivers, doing little for climate. There's no free lunch: someone pays.
422 tokens in, 234 out (11 of them reasoning) · 1.8 s · $0.0013 · 1 message on Pro · answered by z-ai/glm-5.3 via Wafer ·
GPT-6 Luna and GLM 5.3 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 | GLM 5.3 |
|---|---|---|---|
| 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); GLM 5.3 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
GLM 5.3 doesn't read images. GLM 5.3 gets a PDF's text rather than the file itself.
On Free
Both are in the free trial.
Where messages go
GPT-6 Luna: Sent to OpenAI directly. GLM 5.3: Served through OpenRouter, only by hosts that don't store or train on prompts. The maker's own endpoint is never asked.
Fact by fact
| Fact | GPT-6 Luna | GLM 5.3 |
|---|---|---|
| Context window | 1.05M tokens | 1.05M tokens |
| Reads images | Yes | No |
| PDFs | Whole file | Text only |
| Reasoning | Yes | Yes |
| API price (September 2026) | $0.10 in / $0.50 out per million tokens | $1.40 in / $4.40 out per million tokens |
| A typical message at API prices (4,000 tokens in, 700 out) | $0.0008 | $0.0087 |
| A $10 top-up adds | Nothing: an everyday model's count is daily | 200 messages |
| Where a message goes | Sent to OpenAI directly. | Served through OpenRouter, only by hosts that don't store or train on prompts. The maker's own endpoint is never asked. |
| 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. | When one host is down, OpenRouter moves the request to another host that meets the same rules. |
| Anthropic's safety fallback | Doesn't apply | Doesn't apply |
GPT-6 Luna or GLM 5.3?
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; it reads images; it reads a PDF as the whole file, charts and scans included.
Each model's page, the families, and other pairs
GPT-6 Luna vs GLM 5.3 is one pair of models. The page below covers the whole families.
- GPT-6 Luna: price, limits and messages on every plan
- GLM 5.3: price, limits and messages on every plan
- ChatGPT vs GLM
- GPT-6 Luna vs DeepSeek V4.1 Flash
- GPT-6 Sol vs GPT-6 Luna
- Claude Haiku 4.5 vs GPT-6 Luna
- GLM 5.3 vs GLM 5.3 Flash
- Kimi K3 vs GLM 5.3
- DeepSeek V4 Pro vs GLM 5.3
- Every model-vs-model page
GPT-6 Luna is a GPT model; GLM 5.3 is a GLM model.
Questions
Is GPT-6 Luna or GLM 5.3 cheaper in llmwise?
GPT-6 Luna is an everyday model (60 messages a day on Pro); GLM 5.3 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 GLM 5.3 for free?
Yes: both are in the free trial of 5 messages.
Which has the bigger context window, GPT-6 Luna or GLM 5.3?
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 GLM 5.3 in the same chat?
Yes. Pick GPT-6 Luna for one message and GLM 5.3 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 GLM 5.3?
On the same 50 prompts, run on September 27, 2026, GPT-6 Luna passed 47 and GLM 5.3 passed 48. 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.