Model comparison
DeepSeek V4 Flash (High) vs GPT-4o
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Flash (High) #92 (Estimated); GPT-4o #165 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash (High) and GPT-4o share 12 comparable benchmark results. 1 of 8 categories are comparable. 26 results are unique to DeepSeek V4 Flash (High); 1 to GPT-4o.
Updated July 23, 2026- Shared results
- 12
- DeepSeek V4 Flash (High) only
- 26
- GPT-4o only
- 1
- Comparable categories
- 1 / 8
Pick DeepSeek V4 Flash (High) if you want the stronger benchmark profile. GPT-4o only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 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 (High) is clearly ahead on the BenchAlign aggregate, 53.95 to 41.49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V4 Flash (High)'s sharpest advantage is in mathematics, where it averages 91.9 against 0.3.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (High). That is roughly 35.7x on output cost alone. DeepSeek V4 Flash (High) is the reasoning model in the pair, while GPT-4o 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 (High) gives you the larger context window at 1M, compared with 128K for GPT-4o.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | DeepSeek V4 Flash (High) | Δ | GPT-4o |
|---|---|---|---|
| Math | DeepSeek V4 Flash (High)91.9 | Margin← 91.6 | GPT-4o0.3 |
| Agentic | DeepSeek V4 Flash (High)55.3 | MarginNo overlap | GPT-4oNot measured |
| Coding | DeepSeek V4 Flash (High)68.5 | MarginNo overlap | GPT-4oNot measured |
| Knowledge | DeepSeek V4 Flash (High)52.1 | MarginNo overlap | GPT-4oNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Flash (High) | GPT-4o | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash (High)$0.14 input / $0.28 output | GPT-4o$2.5 input / $10 output | DeepSeek V4 Flash (High) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Flash (High)Not available | GPT-4o131 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash (High)Not available | GPT-4o0.81 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash (High)1M | GPT-4o128K | DeepSeek V4 Flash (High) lists the larger context window. |
Benchmark Deep Dive
Agentic9 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-4o | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.6% | — | Not comparable |
| BrowseCompSource | 53.5% | — | Not comparable |
| HLE w/ toolsSource | 40.3% | — | Not comparable |
| MCP AtlasSource | 67.4% | — | Not comparable |
| ToolathlonSource | 43.5% | — | Not comparable |
| τ²-bench resultsSource | 95.6% | 25.1% | DeepSeek V4 Flash (High) leads |
| AA Agentic IndexSource | 28.2% | — | Not comparable |
| GDPval-AASource | 32.4% | — | Not comparable |
| GDPval-AASource | 1147 | — | Not comparable |
Coding7 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-4o | Result |
|---|---|---|---|
| CodeforcesSource | 2816.0 | — | Not comparable |
| SWE-bench VerifiedSource | 78.6% | — | Not comparable |
| SWE-bench ProSource | 52.3% | — | Not comparable |
| SWE MultilingualSource | 70.2% | — | Not comparable |
| Terminal-Bench 2.0Source | 56.6% | — | Not comparable |
| AA-SciCodeSource | 42.0% | 33.3% | DeepSeek V4 Flash (High) leads |
| AA Coding IndexSource | 52.0% | — | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-4o | Result |
|---|---|---|---|
| MMLU-ProSource | 86.4% | — | Not comparable |
| SimpleQASource | 28.9% | — | Not comparable |
| Chinese-SimpleQASource | 73.2% | — | Not comparable |
| GPQASource | 87.4% | — | Not comparable |
| GPQA-DSource | 87.4% | — | Not comparable |
| HLESource | 29.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 37.5% | 11.2% | DeepSeek V4 Flash (High) leads |
| AA-GPQA DiamondSource | 86.7% | 54.3% | DeepSeek V4 Flash (High) leads |
| AA-HLESource | 27.8% | 3.3% | DeepSeek V4 Flash (High) leads |
| AA-Omniscience IndexSource | -22.3% | -10.7% | GPT-4o leads |
| AA-Omniscience AccuracySource | 35.5% | 19.7% | DeepSeek V4 Flash (High) leads |
| AA-Omniscience Hallucination RateSource | 89.7% | 37.9% | GPT-4o leads |
MathDeepSeek V4 Flash (High) wins5 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-4o | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1238 | 861 | DeepSeek V4 Flash (High) leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-4o | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.5% | 34.3% | DeepSeek V4 Flash (High) leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V4 Flash (High) or GPT-4o?
DeepSeek V4 Flash (High) is ahead on BenchLM's BenchAlign leaderboard, 53.95 to 41.49.
Which is better for math, DeepSeek V4 Flash (High) or GPT-4o?
DeepSeek V4 Flash (High) has the edge for math in this comparison, averaging 91.9 versus 0.3. GPT-4o stays close enough that the answer can still flip depending on your workload.
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