DeepSeek V3.2 vs GPT-5.4: Is the 30x price gap worth it?
DeepSeek V3.2 costs $0.28 per million input tokens. GPT-5.4 costs $2.50. On output, on output the difference is even sharper: $15.00 vs $0.42 per million tokens. We went through the benchmarks, the context window caveats, and the actual math to figure out when you should pay more and when you're just burning money.
Photo by Alexandre Debiève on Unsplash
The price gap, in actual numbers
DeepSeek released V3.2 on December 1, 2025. OpenAI launched GPT-5.4 on March 5, 2026. The pricing difference between them is, to put it plainly, enormous.
| Model | Input / 1M | Cached input / 1M | Output / 1M | Context |
|---|---|---|---|---|
| DeepSeek V3.2 | $0.28 | $0.028 | $0.42 | 128K |
| GPT-5.4 | $2.50 | $0.25 | $15.00 | 1.05M* |
| GPT-5.4 Pro | $30.00 | - | $180.00 | 1.05M* |
| DeepSeek V3.2 (Reasoner) | $0.28 | $0.028 | $0.42 | 128K |
*GPT-5.4's 1.05M context has a catch: sessions exceeding 272K input tokens are billed at 2x input and 1.5x output for the entire session, not just the excess. Sources: DeepSeek pricing, OpenAI GPT-5.4 announcement.
On a blended workload (say, 1 input token for every 3 output tokens), DeepSeek V3.2 runs about $0.37 per million tokens effective. GPT-5.4 runs about $11.25. That's roughly 30x. On output tokens alone, it's 36x.
Here's what that means in practice. If you're processing 10 billion tokens a month - a real number for production summarization or RAG pipelines - that's roughly $3,700 with DeepSeek vs $112,500 with GPT-5.4. The question is whether GPT-5.4 is good enough to justify that difference, or whether DeepSeek is good enough to not need it.
DeepSeek V3.2 also has a hybrid thinking/non-thinking mode in a single model, introduced in V3.1. You pay the same rate either way, so the thinking mode is essentially free. GPT-5.4 has reasoning effort levels (none, low, medium, high, xhigh) that adjust compute but not price - thinking tokens count as regular output.
What you're actually getting
DeepSeek V3.2 is a 685B parameter MoE model. In practice only 8 of its 256 experts activate per token (plus one shared expert), which is how it stays cheap to serve. The V3.2 release added DeepSeek Sparse Attention (DSA), reducing attention complexity from O(L²) to O(Lk) for long sequences. The context window is 128K.
GPT-5.4 has a 1.05M token context window, native computer use (mouse and keyboard control via screenshots), tool search that claims to cut token usage by 47% in large MCP server setups, and a knowledge cutoff of August 31, 2025. DeepSeek V3.2's knowledge cutoff is earlier, and the 128K context is a meaningful constraint if you're doing whole-repository work or processing long documents.
One thing worth mentioning: DeepSeek's API has had availability issues during high-traffic periods, which matters for production use. OpenAI's reliability track record is better, though that gap has narrowed over the past year.
Benchmarks: read the fine print
Comparing these two on benchmarks is trickier than it looks. DeepSeek V3.2 was released in December 2025 and benchmarked against "GPT-5 (High)" in its tech report - not against GPT-5.4, which came out three months later. OpenAI's GPT-5.4 announcement benchmarks it against its own GPT-5.2 and GPT-5.3-Codex predecessors, not against DeepSeek. So we're comparing two models that each tested themselves against different baselines.
That said, here's what we can piece together from the official numbers:
| Benchmark | DeepSeek V3.2 | GPT-5.4 | Notes |
|---|---|---|---|
GPQA Diamond Science reasoning | 82.4% | 92.8% | GPT-5.4 wins by 10.4 pts |
MMLU-Pro Knowledge breadth | 85.0% | n/a | GPT-5.4 not reported |
HLE (text-only) Hard reasoning | 25.1% | 39.8% | GPT-5.4 wins clearly |
AIME 2025 Math olympiad | 93.1% | n/a | GPT-5.4 not reported |
LiveCodeBench Coding contests | 83.3% | n/a | GPT-5.4 not reported |
SWE-Bench Pro Hard coding tasks | n/a | 57.7% | DeepSeek used different variant |
SWE-Bench Verified Production coding | 73.1% | n/a | Easier variant; GPT-5.4 not reported |
BrowseComp Web research | 51.4% | 82.7% | GPT-5.4 wins by 31 pts |
DeepSeek V3.2 scores from the DeepSeek V3.2 tech report (December 2025); GPT-5.4 scores from OpenAI's announcement (March 2026). Different evaluation setups - treat direct comparisons as directional, not exact.
The honest read: GPT-5.4 is genuinely better at reasoning tasks that require integrating information from multiple sources (GPQA Diamond, HLE, BrowseComp). DeepSeek V3.2 in thinking mode holds its own on math and coding, though it wasn't tested against GPT-5.4 directly. The 10-point GPQA gap is probably real and not just a methodology artifact.
What's harder to assess: GPT-5.4 has native computer use (75% OSWorld score) and better web research (82.7% BrowseComp vs DeepSeek's 51.4%). If your workload involves agents that need to navigate UIs or do complex web research, those gaps matter. For most text processing, extraction, or generation tasks, they don't.
What different workloads actually cost
Abstract price ratios are less useful than concrete numbers. Here are four common workload patterns with exact costs. All figures assume uncached input tokens.
| Workload (per month) | DeepSeek V3.2 | GPT-5.4 | GPT-5.4 costs |
|---|---|---|---|
100M input + 100M output Early-stage API prototype | $70 | $1,750 | GPT-5.4 costs $1,680 more |
500M input + 250M output Document summarization pipeline | $245 | $5,000 | GPT-5.4 costs $4,755 more |
1B input + 500M output Mid-size production app | $490 | $10,000 | GPT-5.4 costs $9,510 more |
5B input + 5B output High-volume data pipeline | $3,500 | $87,500 | GPT-5.4 costs $84,000 more |
If you have heavy caching - say, a system prompt that repeats across calls - DeepSeek's cached input at $0.028/1M can bring those input costs down by 90%. GPT-5.4's cached input is $0.25/1M, also a 90% discount from standard. The ratio stays similar, but the absolute savings are proportionally larger on DeepSeek because the base price is already low.
One thing the table doesn't show: GPT-5.4's long-context billing quirk. If your sessions regularly exceed 272K input tokens, the entire session prices at $5.00/$22.50 (2x input, 1.5x output). That can significantly change your math for RAG or document-heavy applications. Run the numbers with our token cost calculator before committing to either model at scale.
When to use which
Pick DeepSeek V3.2 if...
You're doing text processing, summarization, classification, code generation, or extraction at volume. The benchmark gap on reasoning doesn't translate to a meaningful quality difference for these tasks. At a fraction of what GPT-5.4 charges, you're getting a model that, on coding benchmarks, is competitive or better depending on the task.
You need thinking mode without paying extra. DeepSeek V3.2 includes a hybrid thinking/non-thinking mode in a single API endpoint at the same price. For math, complex code debugging, or multi-step reasoning, that's genuinely useful.
Pick GPT-5.4 if...
Computer use and desktop automation are GPT-5.4's clearest advantage. It scores 75% on OSWorld - above the human baseline of 72.4% - and has the most mature computer use tooling in the OpenAI ecosystem. DeepSeek doesn't have native computer use.
Context window matters for your use case. 1.05M tokens is significantly more than DeepSeek's 128K, and if you're processing full codebases or large document sets within 272K tokens (to avoid the surcharge), GPT-5.4 works. Just watch the billing threshold carefully.
SLA requirements drive some teams toward OpenAI regardless of price. DeepSeek's API has had availability issues during high-traffic periods. If your application can't handle degraded performance, that reliability premium is worth paying.
Other models worth considering
The DeepSeek vs GPT-5.4 framing misses a few models that sit interestingly between them. Gemini 3.1 Pro at $2/$12 scores 94.3% on GPQA Diamond - nearly matching GPT-5.4 Pro ($30 input) on the hardest reasoning benchmark we have. If you want reasoning quality at a reasonable price, that three-way comparison is worth reading.
GPT-5.4 Mini ($0.75/$4.50) is worth considering if you like the OpenAI ecosystem but the $2.50 input price is a problem. It won't match DeepSeek V3.2's price, but it's substantially cheaper than the full GPT-5.4 and covers most general-purpose use cases. See our full pricing table for the complete picture.
Where this lands
For most production workloads, the 30x price difference is hard to ignore. GPT-5.4 is better at multi-hop reasoning and web research, and it has computer use. But DeepSeek V3.2 is competitive or ahead on coding benchmarks (where they're measurable) and costs a fraction of the price. The benchmarks where GPT-5.4 wins most clearly - GPQA Diamond, HLE, BrowseComp - tend to be specialized rather than general purpose.
If you're building something where reasoning quality genuinely matters - medical, legal, complex analysis - pay for GPT-5.4 or check whether Gemini 3.1 Pro gives you similar reasoning quality at a better price. For everything else, DeepSeek V3.2 is worth testing before committing to a more expensive option.
Sources
- DeepSeek API pricing - DeepSeek (official)
- DeepSeek-V3.2 release announcement - DeepSeek (December 1, 2025)
- DeepSeek-V3.2 technical report - arxiv (December 2025)
- Introducing GPT-5.4 - OpenAI (March 5, 2026)