Model comparison
DeepSeek V4 Flash (High) vs GPT-5.2
Head-to-head evidence from 16 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-5.2 #64 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash (High) and GPT-5.2 share 16 comparable benchmark results. 4 of 8 categories are comparable. 22 results are unique to DeepSeek V4 Flash (High); 12 to GPT-5.2.
Updated July 23, 2026- Shared results
- 16
- DeepSeek V4 Flash (High) only
- 22
- GPT-5.2 only
- 12
- Comparable categories
- 4 / 8
Pick GPT-5.2 if you want the stronger benchmark profile. DeepSeek V4 Flash (High) only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 6 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.2 is clearly ahead on the BenchAlign aggregate, 58.43 to 53.95. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in knowledge, where it averages 92.4 against 52.1. The single biggest benchmark swing on the page is BrowseComp, 53.5% to 65.8%. DeepSeek V4 Flash (High) does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GPT-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (High). That is roughly 50.0x on output cost alone. DeepSeek V4 Flash (High) gives you the larger context window at 1M, compared with 400K for GPT-5.2.
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-5.2 |
|---|---|---|---|
| Math | DeepSeek V4 Flash (High)91.9 | Margin← 56.7 | GPT-5.235.2 |
| Knowledge | DeepSeek V4 Flash (High)52.1 | Margin→ 40.3 | GPT-5.292.4 |
| Coding | DeepSeek V4 Flash (High)68.5 | Margin→ 2.1 | GPT-5.270.6 |
| Agentic | DeepSeek V4 Flash (High)55.3 | Margin→ 0.4 | GPT-5.255.7 |
| Reasoning | DeepSeek V4 Flash (High)Not measured | MarginNo overlap | GPT-5.252.9 |
| Multimodal | DeepSeek V4 Flash (High)Not measured | MarginNo overlap | GPT-5.280.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
BrowseComp
AgenticA 53.5%B 65.8%Winner: GPT-5.2Δ 12.3BrowseComp: DeepSeek V4 Flash (High) scored 53.5%; GPT-5.2 scored 65.8%. GPT-5.2 wins this benchmark. - Source ↗
GPQA
KnowledgeA 87.4%B 92.4%Winner: GPT-5.2Δ 5GPQA: DeepSeek V4 Flash (High) scored 87.4%; GPT-5.2 scored 92.4%. GPT-5.2 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 52.3%B 55.6%Winner: GPT-5.2Δ 3.3SWE-bench Pro: DeepSeek V4 Flash (High) scored 52.3%; GPT-5.2 scored 55.6%. GPT-5.2 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 78.6%B 80%Winner: GPT-5.2Δ 1.4SWE-bench Verified: DeepSeek V4 Flash (High) scored 78.6%; GPT-5.2 scored 80%. GPT-5.2 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Flash (High) | GPT-5.2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash (High)$0.14 input / $0.28 output | GPT-5.2$1.75 input / $14 output | DeepSeek V4 Flash (High) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Flash (High)Not available | GPT-5.273 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash (High)Not available | GPT-5.2130.34 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash (High)1M | GPT-5.2400K | DeepSeek V4 Flash (High) lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.2 wins12 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.6% | — | Not comparable |
| BrowseCompSource | 53.5% | 65.8% | GPT-5.2 leads |
| HLE w/ toolsSource | 40.3% | — | Not comparable |
| MCP AtlasSource | 67.4% | — | Not comparable |
| ToolathlonSource | 43.5% | — | Not comparable |
| τ²-bench resultsSource | 95.6% | 84.8% | DeepSeek V4 Flash (High) leads |
| AA Agentic IndexSource | 28.2% | — | Not comparable |
| GDPval-AASource | 32.4% | — | Not comparable |
| GDPval-AASource | 1147 | — | Not comparable |
| OSWorld-VerifiedSource | — | 47.3% | Not comparable |
| Gert LabsSource | — | 46.54% | Not comparable |
| JobBenchSource | — | 34.3% | Not comparable |
CodingGPT-5.2 wins8 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.2 | Result |
|---|---|---|---|
| CodeforcesSource | 2816.0 | — | Not comparable |
| SWE-bench VerifiedSource | 78.6% | 80% | GPT-5.2 leads |
| SWE-bench ProSource | 52.3% | 55.6% | GPT-5.2 leads |
| SWE MultilingualSource | 70.2% | — | Not comparable |
| Terminal-Bench 2.0Source | 56.6% | — | Not comparable |
| AA-SciCodeSource | 42.0% | 52.1% | GPT-5.2 leads |
| AA Coding IndexSource | 52.0% | — | Not comparable |
| Vibe Code BenchSource | — | 53.50% | Not comparable |
Reasoning5 benchmarks
KnowledgeGPT-5.2 wins12 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.2 | Result |
|---|---|---|---|
| MMLU-ProSource | 86.4% | — | Not comparable |
| SimpleQASource | 28.9% | — | Not comparable |
| Chinese-SimpleQASource | 73.2% | — | Not comparable |
| GPQASource | 87.4% | 92.4% | GPT-5.2 leads |
| GPQA-DSource | 87.4% | — | Not comparable |
| HLESource | 29.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 37.5% | 42.2% | GPT-5.2 leads |
| AA-GPQA DiamondSource | 86.7% | 90.3% | GPT-5.2 leads |
| AA-HLESource | 27.8% | 35.4% | GPT-5.2 leads |
| AA-Omniscience IndexSource | -22.3% | -1.0% | GPT-5.2 leads |
| AA-Omniscience AccuracySource | 35.5% | 43.8% | GPT-5.2 leads |
| AA-Omniscience Hallucination RateSource | 89.7% | 79.7% | GPT-5.2 leads |
MathDeepSeek V4 Flash (High) wins7 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.2 | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 91.9% | — | Not comparable |
| IMOAnswerBenchSource | 85.1% | — | Not comparable |
| ApexSource | 19.1% | — | Not comparable |
| Apex ShortlistSource | 72.1% | — | Not comparable |
| AA AIME 2025Source | — | 99.0% | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 40.700% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 18.800% | Not comparable |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.2 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.5% | 75.4% | GPT-5.2 leads |
Frequently Asked Questions (5)
Which is better, DeepSeek V4 Flash (High) or GPT-5.2?
GPT-5.2 is ahead on BenchLM's BenchAlign leaderboard, 58.43 to 53.95. The biggest single separator in this matchup is BrowseComp, where the scores are 53.5% and 65.8%.
Which is better for knowledge tasks, DeepSeek V4 Flash (High) or GPT-5.2?
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 92.4 versus 52.1. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Flash (High) or GPT-5.2?
GPT-5.2 has the edge for coding in this comparison, averaging 70.6 versus 68.5. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V4 Flash (High) or GPT-5.2?
DeepSeek V4 Flash (High) has the edge for math in this comparison, averaging 91.9 versus 35.2. GPT-5.2 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, DeepSeek V4 Flash (High) or GPT-5.2?
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 55.7 versus 55.3. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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