o4-mini vs DeepSeek V4 Flash
Head-to-head specifications
| Metric | o4-mini | DeepSeek V4 Flash | Difference |
|---|---|---|---|
| Intelligence Index | 29.0 | 40.0 | -27.5% |
| Context window | 256K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.64 | $0.06 | +966.7% |
| Output speed (tokens/s) | 167 | 102 | +63.7% |
| Access | Proprietary API | Open weights | — |
- DeepSeek V4 Flash leads overall capability (Intelligence Index 40.0 vs 29.0).
- DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 10.7× cheaper.
- DeepSeek V4 Flash offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: o4-mini or DeepSeek V4 Flash?
o4-mini advantages
- Output speed (+39%)
DeepSeek V4 Flash advantages
- General intelligence (+28%)
- Context window (+74%)
- Affordability (+91%)
Which should you choose?
- Choose the o4-mini if low latency and fast generation matter for your application.
- Choose the DeepSeek V4 Flash if you need the strongest overall reasoning and accuracy.
Value for money
DeepSeek V4 Flash offers more intelligence per dollar (14.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
o4-mini vs DeepSeek V4 Flash: which should you choose?
o4-mini — OpenAI multimodal model with an Intelligence Index of 29, a 256K-token context window and a blended price of $0.64/1M tokens.
DeepSeek V4 Flash — DeepSeek text model with an Intelligence Index of 40, a 1M-token context window and a blended price of $0.06/1M tokens (open weights).
o4-mini vs DeepSeek V4 Flash: DeepSeek V4 Flash scores higher on the Intelligence Index. DeepSeek V4 Flash leads overall capability (Intelligence Index 40.0 vs 29.0). DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 10.7× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the DeepSeek V4 Flash scores 40.0 versus 29.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The DeepSeek V4 Flash accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, o4-mini generates faster (167 vs 102 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, DeepSeek V4 Flash is the cheaper model to run ($0.06 vs $0.64 per 1M tokens). o4-mini is proprietary api and DeepSeek V4 Flash is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the o4-mini better than the DeepSeek V4 Flash?
DeepSeek V4 Flash is the clearly stronger overall choice, winning most of the dimensions that matter. DeepSeek V4 Flash leads overall capability (Intelligence Index 40.0 vs 29.0).
What is the main difference between the o4-mini and the DeepSeek V4 Flash?
DeepSeek V4 Flash leads overall capability (Intelligence Index 40.0 vs 29.0). DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 10.7× cheaper.
Which is better value?
DeepSeek V4 Flash offers more intelligence per dollar (14.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the o4-mini if low latency and fast generation matter for your application. Choose the DeepSeek V4 Flash if you need the strongest overall reasoning and accuracy.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.