Model comparison
DeepSeek V4 Flash (High) vs GPT-5.5
Head-to-head evidence from 25 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-5.5 #9 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash (High) and GPT-5.5 share 25 comparable benchmark results. 4 of 8 categories are comparable. 13 results are unique to DeepSeek V4 Flash (High); 32 to GPT-5.5.
Updated July 23, 2026- Shared results
- 25
- DeepSeek V4 Flash (High) only
- 13
- GPT-5.5 only
- 32
- Comparable categories
- 4 / 8
Pick GPT-5.5 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 25 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-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 53.95. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 55.3. The single biggest benchmark swing on the page is BrowseComp, 53.5% to 84.4%. 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-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (High). That is roughly 107.1x on output cost alone.
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-5.5 |
|---|---|---|---|
| Math | DeepSeek V4 Flash (High)91.9 | Margin← 44.3 | GPT-5.547.6 |
| Agentic | DeepSeek V4 Flash (High)55.3 | Margin→ 26.3 | GPT-5.581.6 |
| Coding | DeepSeek V4 Flash (High)68.5 | Margin← 9.9 | GPT-5.558.6 |
| Knowledge | DeepSeek V4 Flash (High)52.1 | Margin→ 5.7 | GPT-5.557.8 |
| Reasoning | DeepSeek V4 Flash (High)Not measured | MarginNo overlap | GPT-5.585.0 |
| Multimodal | DeepSeek V4 Flash (High)Not measured | MarginNo overlap | GPT-5.570.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
BrowseComp
AgenticA 53.5%B 84.4%Winner: GPT-5.5Δ 30.9BrowseComp: DeepSeek V4 Flash (High) scored 53.5%; GPT-5.5 scored 84.4%. GPT-5.5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 56.6%B 82%Winner: GPT-5.5Δ 25.4Terminal-Bench 2.0: DeepSeek V4 Flash (High) scored 56.6%; GPT-5.5 scored 82%. GPT-5.5 wins this benchmark. - Source ↗
HLE
KnowledgeA 29.4%B 52.2%Winner: GPT-5.5Δ 22.8HLE: DeepSeek V4 Flash (High) scored 29.4%; GPT-5.5 scored 52.2%. GPT-5.5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 52.3%B 58.6%Winner: GPT-5.5Δ 6.3SWE-bench Pro: DeepSeek V4 Flash (High) scored 52.3%; GPT-5.5 scored 58.6%. GPT-5.5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 87.4%B 93.6%Winner: GPT-5.5Δ 6.2GPQA: DeepSeek V4 Flash (High) scored 87.4%; GPT-5.5 scored 93.6%. GPT-5.5 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-5.5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash (High)$0.14 input / $0.28 output | GPT-5.5$5 input / $30 output | DeepSeek V4 Flash (High) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Flash (High)Not available | GPT-5.5Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash (High)Not available | GPT-5.5Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash (High)1M | GPT-5.51M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGPT-5.5 wins25 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.6% | 82% | GPT-5.5 leads |
| BrowseCompSource | 53.5% | 84.4% | GPT-5.5 leads |
| HLE w/ toolsSource | 40.3% | — | Not comparable |
| MCP AtlasSource | 67.4% | 75.3% | GPT-5.5 leads |
| ToolathlonSource | 43.5% | 55.6% | GPT-5.5 leads |
| τ²-bench resultsSource | 95.6% | 93.9% | DeepSeek V4 Flash (High) leads |
| AA Agentic IndexSource | 28.2% | 44.9% | GPT-5.5 leads |
| GDPval-AASource | 32.4% | 49.5% | GPT-5.5 leads |
| GDPval-AASource | 1147 | 1490 | GPT-5.5 leads |
| CyberGymSource | — | 81.8% | Not comparable |
| OSWorld-VerifiedSource | — | 78.7% | Not comparable |
| APEX-Agents-AASource | — | 37.7% | Not comparable |
| Gert LabsSource | — | 72.93% | Not comparable |
| ResearchClawBenchSource | — | 17.0% | Not comparable |
| OSWorld 2.0Source | — | 13.0% | Not comparable |
| JobBenchSource | — | 42.7% | Not comparable |
| ExploitGymSource | — | 13.4% | Not comparable |
| AA BriefcaseSource | — | 1154 | Not comparable |
| AA AutomationBenchSource | — | 42.1% | Not comparable |
| AA EnterpriseOps-GymSource | — | 46.6% | Not comparable |
| AA Harvey LABSource | — | 86.3% | Not comparable |
| AA ITBenchSource | — | 45.8% | Not comparable |
| AA Tau3 BankingSource | — | 31.3% | Not comparable |
| terminalBenchHardSource | — | 60.6% | Not comparable |
| aaTerminalBench21Source | — | 84.3% | Not comparable |
CodingDeepSeek V4 Flash (High) wins12 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.5 | Result |
|---|---|---|---|
| CodeforcesSource | 2816.0 | — | Not comparable |
| SWE-bench VerifiedSource | 78.6% | — | Not comparable |
| SWE-bench ProSource | 52.3% | 58.6% | GPT-5.5 leads |
| SWE MultilingualSource | 70.2% | — | Not comparable |
| Terminal-Bench 2.0Source | 56.6% | 82.0% | GPT-5.5 leads |
| AA-SciCodeSource | 42.0% | 56.1% | GPT-5.5 leads |
| AA Coding IndexSource | 52.0% | 74.9% | GPT-5.5 leads |
| Vibe Code BenchSource | — | 69.85% | Not comparable |
| React Native EvalsSource | — | 84.7% | Not comparable |
| cursorBench31Source | — | 59.2% | Not comparable |
| cursorBench32Source | — | 58.4% | Not comparable |
| FrontierCode 1.1 MainSource | — | 43.0% | Not comparable |
Reasoning7 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.5 | Result |
|---|---|---|---|
| MRCR 1MSource | 76.9% | — | Not comparable |
| CorpusQA 1MSource | 59.3% | — | Not comparable |
| AA-LCRSource | 62.7% | 74.3% | GPT-5.5 leads |
| CritPtSource | 3.4% | 27.1% | GPT-5.5 leads |
| MRCR v2 64K-128KSource | — | 83.1% | Not comparable |
| MRCR v2 128K-256KSource | — | 87.5% | Not comparable |
| ARC-AGI-2Source | — | 85% | Not comparable |
KnowledgeGPT-5.5 wins13 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.5 | Result |
|---|---|---|---|
| MMLU-ProSource | 86.4% | — | Not comparable |
| SimpleQASource | 28.9% | — | Not comparable |
| Chinese-SimpleQASource | 73.2% | — | Not comparable |
| GPQASource | 87.4% | 93.6% | GPT-5.5 leads |
| GPQA-DSource | 87.4% | 93.6% | GPT-5.5 leads |
| HLESource | 29.4% | 52.2% | GPT-5.5 leads |
| Artificial Analysis Intelligence IndexSource | 37.5% | 54.8% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 86.7% | 93.5% | GPT-5.5 leads |
| AA-HLESource | 27.8% | 44.3% | GPT-5.5 leads |
| AA-Omniscience IndexSource | -22.3% | 20.1% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 35.5% | 56.9% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 89.7% | 85.5% | GPT-5.5 leads |
| HLE w/o toolsSource | — | 41.4% | Not comparable |
MathDeepSeek V4 Flash (High) wins7 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.5 | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 91.9% | — | Not comparable |
| IMOAnswerBenchSource | 85.1% | — | Not comparable |
| ApexSource | 19.1% | — | Not comparable |
| Apex ShortlistSource | 72.1% | — | Not comparable |
| FrontierMath (legacy)Source | — | 51.7% | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 51.700% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 35.400% | Not comparable |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GPT-5.5 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.5% | 75.9% | GPT-5.5 leads |
Frequently Asked Questions (5)
Which is better, DeepSeek V4 Flash (High) or GPT-5.5?
GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 53.95. The biggest single separator in this matchup is BrowseComp, where the scores are 53.5% and 84.4%.
Which is better for knowledge tasks, DeepSeek V4 Flash (High) or GPT-5.5?
GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 57.8 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-5.5?
DeepSeek V4 Flash (High) has the edge for coding in this comparison, averaging 68.5 versus 58.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V4 Flash (High) or GPT-5.5?
DeepSeek V4 Flash (High) has the edge for math in this comparison, averaging 91.9 versus 47.6. GPT-5.5 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-5.5?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 55.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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