Model vs model
DeepSeek V4 Pro vs GLM 5.3
DeepSeek V4 Pro 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 DeepSeek V4 Pro page, OpenRouter's GLM 5.3 page. Updated .
Short answer
They cost the same in llmwise: up to 250 messages a month on Pro on either. Beyond that, they read the same files and neither is easier to try. In our test runs, DeepSeek V4 Pro passed 49 of the 50 prompts both answered and GLM 5.3 48; 3 prompts split them, most on writing (5 to 4).
DeepSeek V4 Pro vs GLM 5.3, prompt by prompt
Every prompt DeepSeek V4 Pro 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 47, only DeepSeek V4 Pro passed 2, only GLM 5.3 passed 1, and neither passed 0. DeepSeek V4 Pro answered sooner on 3 of the 50 and GLM 5.3 on 45; the rest were within 10% of each other. The 50 replies cost $0.1827 on DeepSeek V4 Pro and $0.0364 on GLM 5.3: 5.0× less on GLM 5.3.
The 3 prompts only one of DeepSeek V4 Pro and GLM 5.3 passed
Argue both sides of free buses (writing): DeepSeek V4 Pro passed and GLM 5.3 didn't. DeepSeek V4 Pro: Graded 4.7 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): DeepSeek V4 Pro passed and GLM 5.3 didn't. DeepSeek V4 Pro: Graded 4.3 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 DeepSeek V4 Pro didn't. DeepSeek V4 Pro: Graded 2.3 of 5 on average (lowest 1). GLM 5.3: Graded 4.7 of 5 on average (lowest 4).
Job by job, the widest gaps first
Customer support: DeepSeek V4 Pro passed 4 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 1.8× sooner at the median, 4.1 s against 2.3 s. GLM 5.3 cost 3.3× less, $0.0077 against $0.0023 for the 5 replies. GLM 5.3's replies ran 22% longer, in tokens of reply, thinking not counted.
Writing: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 4 of 5. GLM 5.3 answered 2.2× sooner at the median, 3.9 s against 1.8 s. GLM 5.3 cost 1.9× less, $0.0054 against $0.0029 for the 5 replies. GLM 5.3's replies ran 29% longer, in tokens of reply, thinking not counted.
Summarization: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 4 of 5. GLM 5.3 answered 1.9× sooner at the median, 2.1 s against 1.1 s. GLM 5.3 cost 1.7× less, $0.0046 against $0.0027 for the 5 replies. GLM 5.3's replies ran 11% longer, in tokens of reply, thinking not counted.
Coding: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 113.3× sooner at the median, 119.6 s against 1.1 s. GLM 5.3 cost 16.3× less, $0.1200 against $0.0073 for the 5 replies. Their replies ran to about the same length.
SQL: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 6.9× sooner at the median, 8.0 s against 1.2 s. GLM 5.3 cost 6.4× less, $0.0101 against $0.0016 for the 5 replies. DeepSeek V4 Pro's replies ran 15% longer, in tokens of reply, thinking not counted.
Translation: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 2.7× sooner at the median, 4.3 s against 1.6 s. GLM 5.3 cost 2.8× less, $0.0100 against $0.0035 for the 5 replies. GLM 5.3's replies ran 40% longer, in tokens of reply, thinking not counted.
Math: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 2.9× sooner at the median, 2.7 s against 0.9 s. GLM 5.3 cost 2.5× less, $0.0044 against $0.0018 for the 5 replies. Their replies ran to about the same length.
Data analysis: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 2.1× sooner at the median, 4.1 s against 1.9 s. GLM 5.3 cost 1.5× less, $0.0143 against $0.0096 for the 5 replies. GLM 5.3's replies ran 20% longer, in tokens of reply, thinking not counted.
Agents and tool use: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 1.8× sooner at the median, 1.6 s against 0.9 s. GLM 5.3 cost 1.5× less, $0.0033 against $0.0022 for the 5 replies. DeepSeek V4 Pro's replies ran 11% longer, in tokens of reply, thinking not counted.
RAG and answering from documents: DeepSeek V4 Pro passed 5 of 5 and GLM 5.3 5 of 5. GLM 5.3 answered 3.2× sooner at the median, 2.1 s against 0.6 s. GLM 5.3 cost 1.2× less, $0.0030 against $0.0025 for the 5 replies. GLM 5.3's replies ran 12% 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, 9.0×took 3.9 s and 0.4 s | GLM 5.3, 1.4×cost $0.0015 and $0.0010 |
| Parse a duration like “1h 30m” | Both passed | GLM 5.3, 113.3×took 119.6 s and 1.1 s | GLM 5.3, 25.9×cost $0.0366 and $0.0014 |
| Merge overlapping intervals | Both passed | GLM 5.3, 8.1×took 6.8 s and 0.8 s | GLM 5.3, 2.6×cost $0.0021 and $0.0008 |
| Evaluate an arithmetic expression, no eval | Both passed | GLM 5.3, 11.7×took 145.7 s and 12.5 s | GLM 5.3, 25.9×cost $0.0440 and $0.0017 |
| Parse CSV with quoted fields | Both passed | GLM 5.3, 46.3×took 122.3 s and 2.6 s | GLM 5.3, 14.9×cost $0.0357 and $0.0024 |
| Announce a second bakery shop on LinkedIn | Both passed | GLM 5.3, 1.4×took 3.9 s and 2.9 s | GLM 5.3, 2.5×cost $0.0010 and $0.0004 |
| Rewrite corporate jargon in plain words | Both passed | GLM 5.3, 3.3×took 5.8 s and 1.8 s | GLM 5.3, 12.0×cost $0.0024 and $0.0002 |
| Decline a meeting and offer two times | Both passed | GLM 5.3, 2.3×took 3.3 s and 1.4 s | GLM 5.3, 2.2×cost $0.0006 and $0.0003 |
| A product announcement with five rules | Both passed | GLM 5.3, 2.1×took 1.8 s and 0.8 s | DeepSeek V4 Pro, 1.5×cost $0.0005 and $0.0008 |
| Argue both sides of free buses | Only DeepSeek V4 Pro | GLM 5.3, 2.3×took 4.1 s and 1.8 s | DeepSeek V4 Pro, 1.4×cost $0.0009 and $0.0013 |
| A discount, then sales tax | Both passed | GLM 5.3, 1.9×took 1.8 s and 0.9 s | GLM 5.3, 2.5×cost $0.0005 and $0.0002 |
| Pens at 3 for $4 | Both passed | GLM 5.3, 1.8×took 2.7 s and 1.5 s | GLM 5.3, 5.9×cost $0.0010 and $0.0002 |
| Compound interest over three years | Both passed | GLM 5.3, 5.9×took 3.4 s and 0.6 s | GLM 5.3, 1.4×cost $0.0008 and $0.0005 |
| Four-digit numbers whose digits sum to 9 | Both passed | GLM 5.3, 2.1×took 4.0 s and 1.9 s | GLM 5.3, 4.7×cost $0.0012 and $0.0003 |
| The highest of three dice is a 5 | Both passed | GLM 5.3, 3.0×took 2.5 s and 0.9 s | GLM 5.3, 1.5×cost $0.0009 and $0.0006 |
| An article in three bullets | Only DeepSeek V4 Pro | DeepSeek V4 Pro, 1.5×took 1.8 s and 2.8 s | GLM 5.3, 2.2×cost $0.0010 and $0.0004 |
| An email thread in one sentence | Both passed | GLM 5.3, 1.2×took 1.2 s and 1.0 s | GLM 5.3, 2.2×cost $0.0005 and $0.0002 |
| Decisions and action items from a meeting | Both passed | GLM 5.3, 3.5×took 5.4 s and 1.5 s | GLM 5.3, 4.6×cost $0.0011 and $0.0002 |
| A quarterly memo for the CEO | Both passed | GLM 5.3, 2.1×took 2.3 s and 1.1 s | GLM 5.3, 1.4×cost $0.0014 and $0.0010 |
| A study with a negative result | Both passed | GLM 5.3, 2.5×took 2.1 s and 0.8 s | DeepSeek V4 Pro, 1.5×cost $0.0005 and $0.0008 |
| The region with the most revenue | Both passed | GLM 5.3, 1.9×took 4.1 s and 2.1 s | GLM 5.3, 5.0×cost $0.0020 and $0.0004 |
| Average order value in August | Both passed | GLM 5.3, 1.1×took 2.2 s and 1.9 s | GLM 5.3, 3.3×cost $0.0012 and $0.0004 |
| Revenue change from July to August | Both passed | GLM 5.3, 3.8×took 5.2 s and 1.4 s | GLM 5.3, 1.3×cost $0.0020 and $0.0016 |
| A median, filtered two ways | Both passed | GLM 5.3, 1.2×took 1.2 s and 1.0 s | Closecost $0.0011 and $0.0011 |
| Correlation between ad spend and sign-ups | Both passed | GLM 5.3, 1.4×took 8.1 s and 5.6 s | GLM 5.3, 1.3×cost $0.0081 and $0.0062 |
| A late order | Both passed | GLM 5.3, 1.6×took 4.1 s and 2.6 s | GLM 5.3, 5.8×cost $0.0023 and $0.0004 |
| A return inside the window | Both passed | DeepSeek V4 Pro, 1.2×took 1.3 s and 1.6 s | GLM 5.3, 2.9×cost $0.0007 and $0.0002 |
| A frustrated customer | Only GLM 5.3 | GLM 5.3, 4.1×took 9.5 s and 2.3 s | GLM 5.3, 5.7×cost $0.0018 and $0.0003 |
| A refund request outside the window | Both passed | GLM 5.3, 2.2×took 2.5 s and 1.1 s | GLM 5.3, 1.3×cost $0.0012 and $0.0009 |
| A message with a planted instruction | Both passed | GLM 5.3, 1.3×took 6.7 s and 5.3 s | GLM 5.3, 3.6×cost $0.0017 and $0.0005 |
| A delivery message into Spanish | Both passed | GLM 5.3, 5.9×took 8.8 s and 1.5 s | GLM 5.3, 19.1×cost $0.0044 and $0.0002 |
| A product description into French | Both passed | Closetook 1.3 s and 1.5 s | GLM 5.3, 2.1×cost $0.0006 and $0.0003 |
| A meeting note into German | Both passed | GLM 5.3, 2.7×took 4.3 s and 1.6 s | GLM 5.3, 7.9×cost $0.0015 and $0.0002 |
| Idioms into natural Japanese | Both passed | GLM 5.3, 1.6×took 2.7 s and 1.7 s | Closecost $0.0011 and $0.0012 |
| A lease clause into Brazilian Portuguese | Both passed | GLM 5.3, 9.6×took 16.4 s and 1.7 s | GLM 5.3, 1.5×cost $0.0024 and $0.0016 |
| Customers in one country | Both passed | GLM 5.3, 1.5×took 1.2 s and 0.8 s | GLM 5.3, 3.0×cost $0.0004 and $0.0001 |
| Count orders by status | Both passed | Closetook 1.1 s and 1.2 s | GLM 5.3, 3.1×cost $0.0004 and $0.0001 |
| Revenue by category | Both passed | GLM 5.3, 6.5×took 8.0 s and 1.2 s | GLM 5.3, 12.3×cost $0.0025 and $0.0002 |
| Every customer, even those without orders | Both passed | GLM 5.3, 10.7×took 10.4 s and 1.0 s | GLM 5.3, 4.5×cost $0.0032 and $0.0007 |
| Monthly revenue with a running total | Both passed | GLM 5.3, 5.2×took 16.6 s and 3.2 s | GLM 5.3, 9.3×cost $0.0036 and $0.0004 |
| A fact from one section | Both passed | GLM 5.3, 1.2×took 1.1 s and 0.9 s | GLM 5.3, 1.9×cost $0.0005 and $0.0003 |
| Core hours and start times | Both passed | GLM 5.3, 1.7×took 0.8 s and 0.5 s | DeepSeek V4 Pro, 2.0×cost $0.0004 and $0.0007 |
| Two sections in one answer | Both passed | GLM 5.3, 4.1×took 2.1 s and 0.5 s | GLM 5.3, 1.7×cost $0.0008 and $0.0005 |
| A later amendment changes the answer | Both passed | GLM 5.3, 2.0×took 2.5 s and 1.3 s | GLM 5.3, 1.5×cost $0.0009 and $0.0006 |
| A question the handbook doesn't answer | Both passed | GLM 5.3, 13.7×took 8.8 s and 0.6 s | GLM 5.3, 1.1×cost $0.0005 and $0.0005 |
| Pick the tool and work out the date | Both passed | GLM 5.3, 1.8×took 1.6 s and 0.9 s | GLM 5.3, 2.4×cost $0.0005 and $0.0002 |
| Convert a currency | Both passed | DeepSeek V4 Pro, 1.3×took 0.9 s and 1.1 s | GLM 5.3, 3.0×cost $0.0004 and $0.0001 |
| Book a meeting from a sentence | Both passed | GLM 5.3, 1.4×took 2.3 s and 1.7 s | GLM 5.3, 1.5×cost $0.0008 and $0.0006 |
| Search, but don't book | Both passed | GLM 5.3, 2.6×took 1.4 s and 0.5 s | GLM 5.3, 1.3×cost $0.0007 and $0.0005 |
| Two calls with a unit conversion | Both passed | GLM 5.3, 5.2×took 3.7 s and 0.7 s | GLM 5.3, 1.1×cost $0.0008 and $0.0008 |
DeepSeek V4 Pro vs GLM 5.3 in our test runs
DeepSeek V4 Pro 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 | DeepSeek V4 Pro | GLM 5.3 |
|---|---|---|
| Coding | 5 of 5 | 5 of 5 |
| Writing | 5 of 5 | 4 of 5 |
| Math | 5 of 5 | 5 of 5 |
| Summarization | 5 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 | 49 of 50 · 1 message each · $0.0037 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.
DeepSeek V4 Pro
Passed: Graded 4.7 of 5 on average (lowest 4).
Making buses free turns public transit into a true public good, like sidewalks or libraries. It removes fares as a barrier for low-income riders, boosts ridership, cuts traffic congestion and emissions, and speeds boarding by eliminating payment delays. Cities already subsidize most transit costs; dropping the small fare share is a modest investment with outsized returns in mobility, equity, and cleaner air. The strongest case against free buses is that fares, however small, are the only direct signal of demand. Removing them invites overcrowding on popular routes while doing nothing to expand service where it is sparse. Scarce transit dollars are better spent on frequency, reliability, and coverage—improvements that actually attract riders—than on subsidizing trips for people who can already afford to pay.
395 tokens in, 193 out (40 of them reasoning) · 4.1 s · $0.0009 · 1 message on Pro · answered by deepseek/deepseek-v4-pro-0813 via Wafer ·
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 ·
DeepSeek V4 Pro 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 | DeepSeek V4 Pro | GLM 5.3 |
|---|---|---|---|
| Free | $0 | Not in the trial | Not in the trial |
| Plus | $10 a month | Up to 125 a month | Up to 125 a month |
| Pro | $20 a month | Up to 250 a month | Up to 250 a month |
| Max | $50 a month | Up to 800 a month | Up to 800 a month |
| Ultra | $100 a month | Up to 1,800 a month | Up to 1,800 a month |
| Studio | $200 a month | Up to 4,000 a month | 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: Plus $3.75, 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
They cost the same in llmwise: up to 250 messages a month on Pro on either.
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
DeepSeek V4 Pro and GLM 5.3 don't read images. DeepSeek V4 Pro and GLM 5.3 get a PDF's text rather than the file itself.
On Free
Neither is in the free trial: both need a paid plan, from Plus.
Where messages go
DeepSeek V4 Pro: Served through OpenRouter, only by hosts that don't store or train on prompts. The maker's own endpoint is never asked. 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 | DeepSeek V4 Pro | GLM 5.3 |
|---|---|---|
| Context window | 1.05M tokens | 1.05M tokens |
| Reads images | No | No |
| PDFs | Text only | Text only |
| Reasoning | Yes | Yes |
| API price (October 2026) | $0.40 in / $4.00 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) | $0.0044 | $0.0087 |
| A $10 top-up adds | 200 messages | 200 messages |
| Where a message goes | Served through OpenRouter, only by hosts that don't store or train on prompts. The maker's own endpoint is never asked. | 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 | When one host is down, OpenRouter moves the request to another host that meets the same rules. | 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 |
DeepSeek V4 Pro 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 DeepSeek V4 Pro: it costs its maker less to run ($0.0044 a typical message at API prices), though in llmwise the count is the same.
Each model's page, the families, and other pairs
DeepSeek V4 Pro vs GLM 5.3 is one pair of models. The page below covers the whole families.
- DeepSeek V4 Pro: price, limits and messages on every plan
- GLM 5.3: price, limits and messages on every plan
- DeepSeek vs GLM
- Claude Opus 5.5 vs DeepSeek V4 Pro
- DeepSeek V4 Pro vs Kimi K3
- Claude Sonnet 5 vs DeepSeek V4 Pro
- GLM 5.3 vs GLM 5.3 Flash
- Kimi K3 vs GLM 5.3
- Claude Sonnet 5 vs GLM 5.3
- Every model-vs-model page
DeepSeek V4 Pro is a DeepSeek model; GLM 5.3 is a GLM model.
Questions
Is DeepSeek V4 Pro or GLM 5.3 cheaper in llmwise?
They cost the same in llmwise: up to 250 messages a month on Pro on either. Every paid plan's monthly allowance is shared by the models on it, so each count is the most you get if it all goes to that model.
Can I try DeepSeek V4 Pro and GLM 5.3 for free?
Neither is in the free trial of 5 messages: both need a paid plan, from Plus.
Which has the bigger context window, DeepSeek V4 Pro 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 DeepSeek V4 Pro and GLM 5.3 in the same chat?
Yes. Pick DeepSeek V4 Pro 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, DeepSeek V4 Pro or GLM 5.3?
On the same 50 prompts, run on October 8, 2026, DeepSeek V4 Pro passed 49 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, GLM, and Mistral, 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.