Comparison
ChatGPT vs GLM
On llmwise Pro, GPT-6 Sol up to 125 messages a month and GLM 5.3 up to 250. ChatGPT is OpenAI's own app for its GPT models; llmwise has GPT models in its own chat, not ChatGPT itself. We ran the same 50 prompts across 10 jobs on every GPT and GLM model and published every reply: each family's pick against the other's job by job, the prompts where they split, then their plans and how the lineups differ.
Based on 250 of our test runs on , through OpenRouter with the app's own prompt and settings. Updated .
Short answer
In our test runs on September 27, 2026, the same 50 prompts across 10 jobs: GPT's 3 models passed 140 of 150 replies and GLM's 2 models passed 88 of 100 replies. Job by job, both families' best models shared the top result on 7 of the 10 jobs; GPT's alone had it on writing and summarization; GLM's alone had it on customer support.
GPT vs GLM, job by job
On each job, GPT's pick against GLM's: the model of each family that passed the most of the job's 5 prompts (then the most hard ones, then the cheaper). The jobs where they differ most come 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.
Summarization: GPT-6 Astra passed 5 of 5 and GLM 5.3 4 of 5. GLM 5.3 answered 2.6× sooner at the median, 2.9 s against 1.1 s. GLM 5.3 cost 20.0× less, $0.0546 against $0.0027 for the 5 replies. Their replies ran to about the same length.
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.
Coding: GPT-6 Luna passed 5 of 5 and GLM 5.3 Flash 5 of 5. GPT-6 Luna answered 1.8× sooner at the median, 4.6 s against 8.4 s. GLM 5.3 Flash cost 1.1× less, $0.0014 against $0.0013 for the 5 replies. GLM 5.3 Flash's replies ran 10% longer, in tokens of reply, thinking not counted.
Math: GPT-6 Sol passed 5 of 5 and GLM 5.3 Flash 5 of 5. GPT-6 Sol answered 1.2× sooner at the median, 2.2 s against 2.7 s. GLM 5.3 Flash cost 12.1× less, $0.0090 against $0.0008 for the 5 replies. GLM 5.3 Flash's replies ran 43% 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 Flash 5 of 5. GPT-6 Luna answered 2.6× sooner at the median, 1.6 s against 4.1 s. They cost about the same, $0.0005 against $0.0005 for the 5 replies. GLM 5.3 Flash's replies ran 13% longer, in tokens of reply, thinking not counted.
SQL: GPT-6 Luna passed 5 of 5 and GLM 5.3 Flash 5 of 5. GPT-6 Luna answered 1.2× sooner at the median, 1.2 s against 1.4 s. GPT-6 Luna cost 1.5× less, $0.0004 against $0.0006 for the 5 replies. GPT-6 Luna's replies ran 15% longer, in tokens of reply, thinking not counted.
RAG and answering from documents: GPT-6 Luna passed 5 of 5 and GLM 5.3 Flash 5 of 5. GPT-6 Luna answered 1.8× sooner at the median, 1.3 s against 2.2 s. GPT-6 Luna cost 2.0× less, $0.0004 against $0.0008 for the 5 replies. GLM 5.3 Flash's replies ran 89% longer, in tokens of reply, thinking not counted.
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.
The 3 prompts only one of GPT and GLM passed
Where one family's pick passed a prompt and the other's didn't, in each check's own words.
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.
An article in three bullets (summarization): GPT-6 Astra passed and GLM 5.3 didn't. GPT-6 Astra: Graded 4.7 of 5 on average (lowest 4). GLM 5.3: Graded 4.3 of 5 on average (lowest 3); but 68 words, over the 60 allowed.
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).
Each GPT model against each GLM model
Every GPT model against every GLM model on the same 50 prompts: each one's passes, how many prompts split them, and who was cheaper and quicker.
GPT-6 Astra vs GLM 5.3: 48 and 48 of 50; 4 prompts split them; GLM 5.3's replies cost 16.1× less in all, and GLM 5.3 answered sooner on 47, GPT-6 Astra on 1.
GPT-6 Astra vs GLM 5.3 Flash: 48 and 40 of 50; 8 prompts split them; GLM 5.3 Flash's replies cost 60.7× less in all, and GPT-6 Astra answered sooner on 31, GLM 5.3 Flash on 12.
GPT-6 Sol vs GLM 5.3: 45 and 48 of 50; 5 prompts split them; GLM 5.3's replies cost 3.6× less in all, and GLM 5.3 answered sooner on 42, GPT-6 Sol on 3. GPT-6 Sol vs GLM 5.3.
GPT-6 Sol vs GLM 5.3 Flash: 45 and 40 of 50; 7 prompts split them; GLM 5.3 Flash's replies cost 13.5× less in all, and GPT-6 Sol answered sooner on 38, GLM 5.3 Flash on 9.
GPT-6 Luna vs GLM 5.3: 47 and 48 of 50; 3 prompts split them; GPT-6 Luna's replies cost 6.2× less in all, and GLM 5.3 answered sooner on 29, GPT-6 Luna on 13. GPT-6 Luna vs GLM 5.3.
GPT-6 Luna vs GLM 5.3 Flash: 47 and 40 of 50; 9 prompts split them; GPT-6 Luna's replies cost 1.6× less in all, and GPT-6 Luna answered sooner on 43, GLM 5.3 Flash on 6. GPT-6 Luna vs GLM 5.3 Flash.
One writing prompt, both replies
GPT-6 Luna and GLM 5.3 on a writing prompt, the job where they differed most: both replies as they came.
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 ·
Every model, every job
All 5 GPT and GLM models in llmwise across the 50 prompts, with what a reply cost to run and each model's count on Pro.
| Model | Passed | Hard ones | Cost per reply | On Pro |
|---|---|---|---|---|
| GPT-6 AstraOpenAI | 48 of 50 | 20 of 20 | $0.0117 | Up to 31 a month |
| GPT-6 SolOpenAI | 45 of 50 | 19 of 20 | $0.0026 | Up to 125 a month |
| GPT-6 LunaOpenAI | 47 of 50 | 20 of 20 | $0.00012 | Up to 60 a day |
| GLM 5.3Z.ai | 48 of 50 | 19 of 20 | $0.00073 | Up to 250 a month |
| GLM 5.3 FlashZ.ai | 40 of 50 | 16 of 20 | $0.00019 | Up to 60 a day |
Passed: replies that passed their check, of those scored. Cost: what OpenRouter charged us per reply, on average; in llmwise you pay per message, not per token. Every prompt and how it's scored.
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.
Its own subscription
The one company here with its own plan at about llmwise Pro's price ($20 a month), in its own words, dated. We drop a plan here when its facts are more than 45 days old.
ChatGPT Plus (OpenAI), $20 a month
Models it names: GPT-6 Astra, GPT-6 Sol, and GPT-6 Luna. On its limits: “To ensure a smooth experience for all users, Plus subscriptions may include usage limits such as message caps, especially during high demand. These limits may vary based on system conditions.” ChatGPT Plus vs llmwise.
Sources: OpenAI Help Center: What is ChatGPT Plus?, OpenAI Help Center: Managing usage with GPT-6 Astra in Work and Codex and ChatGPT pricing, checked .
llmwise Pro, $20 a month, has all 5 of these models in one chat, from one monthly allowance: GPT-6 Sol up to 125 messages a month and GLM 5.3 up to 250.
The lineups at a glance
What follows from each model's facts in our catalog.
The lineups
GPT: 3 models, GPT-6 Astra, GPT-6 Sol, and GPT-6 Luna. GLM: 2 models, GLM 5.3 and GLM 5.3 Flash.
Price per message
The least expensive GPT model is GPT-6 Luna (60 messages a day on Pro); the least expensive GLM model is GLM 5.3 Flash (60 messages a day on Pro).
Context window
GPT goes up to 1.05M tokens (GPT-6 Astra); GLM up to 1.05M tokens (GLM 5.3).
Images and PDFs
GLM 5.3 doesn't read images. GLM 5.3 and GLM 5.3 Flash get a PDF's text rather than the file itself.
On the Free plan
Free's one-time trial of 5 messages covers GPT-6 Sol, GPT-6 Luna, GLM 5.3, and GLM 5.3 Flash. Paid plans have every model, with messages every month.
Model by model
Two named models side by side, prompt by prompt, each with its messages on every plan.
More head-to-heads
Each of GPT and GLM against the other families, every job from the same test runs.
Where your messages go
In llmwise, a message to GPT goes to its maker, OpenAI, or through OpenRouter when llmwise can't reach the maker directly. GLM models are served only through OpenRouter, by endpoints that don't store or train on prompts. The Privacy Policy has the details.
Questions
Which is better, ChatGPT or GLM?
In our test runs on September 27, 2026, the same 50 prompts across 10 jobs: GPT's 3 models passed 140 of 150 replies and GLM's 2 models passed 88 of 100 replies. Job by job, both families' best models shared the top result on 7 of the 10 jobs; GPT's alone had it on writing and summarization; GLM's alone had it on customer support.
Which is cheaper, GPT or GLM?
In llmwise, the least expensive GPT model is GPT-6 Luna (60 messages a day on Pro), and the least expensive GLM model is GLM 5.3 Flash (60 messages a day on Pro). At API list prices (September 2026), a typical message of 4,000 tokens in and 700 out costs $0.0008 on GPT-6 Luna and $0.0010 on GLM 5.3 Flash.
Can I use GPT and GLM in the same chat?
Yes. Pick a model for each message; when you switch, the next model sees the whole conversation, including the other one's answers.
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.