Model comparison
DeepSeek V4 Flash Base vs o1-pro
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Flash Base unranked (Not scored); o1-pro #141 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash Base and o1-pro share 0 comparable benchmark results. 1 of 8 categories are comparable. 24 results are unique to DeepSeek V4 Flash Base; 2 to o1-pro.
Updated July 23, 2026- Shared results
- 0
- DeepSeek V4 Flash Base only
- 24
- o1-pro only
- 2
- Comparable categories
- 1 / 8
Treat this as a split decision. DeepSeek V4 Flash Base makes more sense if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model; o1-pro is the better fit if knowledge is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V4 Flash Base and o1-pro finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
o1-pro is the reasoning model in the pair, while DeepSeek V4 Flash Base is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. DeepSeek V4 Flash Base gives you the larger context window at 1M, compared with 200K for o1-pro.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Flash Base | o1-pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash BaseNot available | o1-pro$150 input / $600 output | A complete price comparison is not available. |
| Generation speedtokens per second | DeepSeek V4 Flash BaseNot available | o1-proNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash BaseNot available | o1-proNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash Base1M | o1-pro200K | DeepSeek V4 Flash Base lists the larger context window. |
Benchmark Deep Dive
Coding2 benchmarks
Reasoning6 benchmarks
Knowledgeo1-pro wins14 benchmarks
| Benchmark | DeepSeek V4 Flash Base | o1-pro | Result |
|---|---|---|---|
| AGIEvalSource | 82.6% | — | Not comparable |
| MMLUSource | 88.7% | — | Not comparable |
| MMLU-ReduxSource | 89.4% | — | Not comparable |
| MMLU-ProSource | 68.3% | — | Not comparable |
| MMMLUSource | 88.8% | — | Not comparable |
| C-EvalSource | 92.1% | — | Not comparable |
| CMMLUSource | 90.4% | — | Not comparable |
| MultiLoKoSource | 42.2% | — | Not comparable |
| SimpleQASource | 30.1% | — | Not comparable |
| SuperGPQASource | 46.5% | — | Not comparable |
| FACTS ParametricSource | 33.9% | — | Not comparable |
| TriviaQASource | 82.8% | — | Not comparable |
| GPQASource | — | 79% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 18.9% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | DeepSeek V4 Flash Base | o1-pro | Result |
|---|---|---|---|
| MGSMSource | 85.7% | — | Not comparable |
Frequently Asked Questions (2)
Which is better, DeepSeek V4 Flash Base or o1-pro?
DeepSeek V4 Flash Base and o1-pro are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, DeepSeek V4 Flash Base or o1-pro?
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 56.4. DeepSeek V4 Flash Base stays close enough that the answer can still flip depending on your workload.
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