DeepSeek V4 and GLM-5.3 are both cheap, open-weight, Chinese-lab reasoning and coding models that most small businesses would only ever reach through an API rather than by self-hosting. Both labs have also changed their pricing and licensing terms since launch in ways that make any single comparison number a snapshot rather than a stable fact — DeepSeek’s “permanent” price cut from May 2026 was followed months later by a new peak-hour pricing structure, and GLM-5.3’s license is open for virtually everyone except very large resellers, a restriction covered in more detail in our GLM-5.3 review.
“Which is cheaper” assumes a stable number — neither model has one
DeepSeek’s own pricing history this year is the clearest illustration of why a headline price comparison goes stale fast. One tracker documented that DeepSeek converted a 75% promotional discount on V4-Pro into permanent pricing on May 22, 2026 — $0.435 per million input tokens at cache-miss rates, $0.87 output, described at the time as having “no expiry date.” By September, DeepSeek’s own blog describes rolling out a new peak-hour pricing structure — what the company itself calls “industry-first surge pricing” — meaning the rate you actually pay now depends on the time of day you send the request. A separate tracker’s snapshot verified as of September 7, 2026 lists DeepSeek’s off-peak input pricing at $0.66 for V4-Pro and $0.22 for V4-Flash, numbers that don’t map cleanly onto the May “permanent” figure. None of these sources are wrong exactly — they’re each accurate for the date they were checked, which is precisely the point: don’t budget against any single number in this article, including ours, without confirming it on DeepSeek’s own live pricing page first.
License terms: genuinely different, but check whether the difference applies to you
| Model | License | What it actually restricts |
|---|---|---|
| DeepSeek V4 (Pro and Flash) | MIT | Nothing meaningful — MIT is about as unrestricted as an open-weight license gets |
| GLM-5.3 | Bespoke, non-MIT | Only restricts model-as-a-service operators with over $10 billion in annual revenue — covered in our GLM-5.3 review, which called this a commercial lever aimed at large competitors, not a genuine safety measure |
For a solo freelancer or small team, this distinction is almost entirely theoretical — neither of you is a $10 billion MaaS operator, so GLM-5.3’s restriction doesn’t actually change what you can do with it. Don’t let “DeepSeek is MIT, therefore more open” become the deciding factor in your choice unless your business plan genuinely involves reselling model access at enterprise scale, which it almost certainly doesn’t.
If you’re actually self-hosting, the hardware story is where they really diverge
Most readers of this site use models through an API rather than running the weights themselves, but for the smaller group who do self-host, the difference is real. DeepSeek V4 has been adapted to run on Huawei’s Ascend 910C and newer Ascend 950 chips at performance parity with Nvidia GPU deployments — a genuine option for anyone concerned about Nvidia GPU supply constraints or export restrictions. GLM-5.3 doesn’t offer that flexibility: even its “Flash,” meant to be the lighter variant, requires 4x H200 GPUs to run, a serious hardware commitment for something marketed as the small option — a detail also covered in our GLM-5.3 review.
Neither lab’s launch benchmarks should be taken at face value yet
DeepSeek’s own reported scores for V4-Pro-Max — 93.5 on LiveCodeBench Pass@1, a 3206 Codeforces rating — come with an explicit caveat from the tracker reporting them: these are vendor-run numbers from the initial release coverage, not independent leaderboard entries, and vendor-run scaffolds routinely score higher than what an independent harness reproduces. GLM-5.3 has its own unresolved benchmark honesty gap, covered in our review: three separate sources cite conflicting vulnerability-count figures for the exploit-chain capability that held up its release, none reconciled. This is the same pattern our Muse Spark 1.3 review found in a completely different lab’s launch numbers this month — a vendor’s own scorecard isn’t the same thing as an independent one. Treat any specific head-to-head coding benchmark you see comparing these two models as provisional until an independent evaluator — not either vendor — runs both under matched conditions, the same caution our coding-agent harness guide and our Qwen vs Claude Opus 5 comparison apply to vendor-reported numbers generally.
What this actually means if you’re choosing between them
Before comparing price or benchmarks at all, check both providers’ current, live pricing pages directly — the lesson from DeepSeek’s own pricing history this year is the same one our guide to reasoning-token pricing makes about promotional rates generally: “permanent” and “no expiry date” language on an AI vendor’s pricing page has a shorter shelf life than it sounds. If your workload runs at unpredictable hours, DeepSeek’s new peak-hour structure means your actual monthly bill will vary by when your usage happens to land, not just by volume — worth modeling explicitly rather than assuming a single blended rate. If license terms matter to your specific business model, check the actual restriction language rather than treating “MIT” as automatically better than a named-restriction license that doesn’t apply to your scale — the practical difference for most readers here is close to zero. And regardless of which model you pick, weigh it against the general framework in our guide to free vs. paid AI tools and our guide to cutting AI costs for small teams rather than in isolation — a cheap open-weight model is still one line item among several in a real AI budget.
Who should actually pick which one
If you want a genuinely unrestricted license, a very large context window, and you’re comfortable modeling variable peak-hour pricing into your workflow, DeepSeek V4 — particularly the lighter Flash variant — is the more flexible starting point. If you’re specifically evaluating GLM-5.3 for a reason already covered in our dedicated review — its post-training improvements over 5.2, or its specific coding behavior — the license restriction shouldn’t be the deciding factor either way, since it doesn’t apply to a small business regardless of which model you choose. Neither model’s launch-week benchmark claims should be the deciding factor on their own; test both against a representative sample of your actual workload, the same recommendation that applies to every model comparison on this site this month.
Is DeepSeek V4 actually cheaper than GLM-5.3?
It depends on when you check and what hour your usage lands, given DeepSeek’s new peak-hour pricing structure. Both models are positioned at the budget end of the frontier market, but neither has a single stable price you can quote with confidence more than a few weeks out — check each provider’s live pricing page before budgeting.
Why does DeepSeek’s pricing keep changing?
DeepSeek made a promotional discount permanent in May 2026, then introduced a new time-of-day “surge pricing” structure by September, and separately adjusted prices again alongside a newer model variant release. Each change was reported accurately by trackers at the time, which is exactly why comparison articles — including this one — go stale faster than they used to for this specific model family.
Is DeepSeek V4’s MIT license actually different from GLM-5.3’s license?
Yes, technically — MIT is unrestricted, while GLM-5.3’s bespoke license specifically restricts model-as-a-service resellers with over $10 billion in annual revenue. For a solo freelancer or small team, that restriction doesn’t apply either way, so the license difference is unlikely to be the deciding factor in practice.
Can I self-host either of these models affordably?
Not easily for most small teams. GLM-5.3’s smaller “Flash” variant still requires 4x H200 GPUs. DeepSeek V4 offers more hardware flexibility, including performance parity on Huawei’s Ascend chips as an alternative to Nvidia hardware, but self-hosting either model at meaningful scale is a serious infrastructure commitment rather than a weekend project.
Which one should I use for coding tasks?
There’s no independently verified head-to-head answer yet. Both labs’ own launch benchmarks come with explicit caveats about vendor-run scaffolding inflating scores, so test both directly on a representative sample of your own coding tasks rather than choosing from either company’s launch materials.
Should I trust the benchmark numbers each lab published at launch?
Treat them as a starting point, not a verdict. DeepSeek’s own tracker coverage explicitly flags its launch numbers as vendor-run rather than independently verified, and GLM-5.3 has its own unresolved conflicting vulnerability-count figures from its release. Independent, matched-condition testing hasn’t caught up with either launch yet.
Shurah is the founder of AI Tools Daily, tracking pricing, licensing and policy changes across AI tools so readers can make decisions without wading through marketing claims themselves.
