Model comparison
DeepSeek V4 Flash vs GPT-5.5
Head-to-head evidence from 10 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Flash #61 (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 and GPT-5.5 share 10 comparable benchmark results. 4 of 8 categories are comparable. 12 results are unique to DeepSeek V4 Flash; 47 to GPT-5.5.
Updated July 23, 2026- Shared results
- 10
- DeepSeek V4 Flash only
- 12
- GPT-5.5 only
- 47
- Comparable categories
- 4 / 8
Pick GPT-5.5 if you want the stronger benchmark profile. DeepSeek V4 Flash only becomes the better choice if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 10 shared benchmark results across 4 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 58.88. 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 49.1. The single biggest benchmark swing on the page is HLE, 8.1% to 52.2%. DeepSeek V4 Flash does hit back in coding, 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. That is roughly 107.1x on output cost alone. GPT-5.5 is the reasoning model in the pair, while DeepSeek V4 Flash 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.
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 | Δ | GPT-5.5 |
|---|---|---|---|
| Agentic | DeepSeek V4 Flash49.1 | Margin→ 32.5 | GPT-5.581.6 |
| Knowledge | DeepSeek V4 Flash38.8 | Margin→ 19.0 | GPT-5.557.8 |
| Math | DeepSeek V4 Flash40.8 | Margin→ 6.8 | GPT-5.547.6 |
| Coding | DeepSeek V4 Flash64.2 | Margin← 5.6 | GPT-5.558.6 |
| Reasoning | DeepSeek V4 FlashNot measured | MarginNo overlap | GPT-5.585.0 |
| Multimodal | DeepSeek V4 FlashNot measured | MarginNo overlap | GPT-5.570.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 8.1%B 52.2%Winner: GPT-5.5Δ 44.1HLE: DeepSeek V4 Flash scored 8.1%; GPT-5.5 scored 52.2%. GPT-5.5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 49.1%B 82%Winner: GPT-5.5Δ 32.9Terminal-Bench 2.0: DeepSeek V4 Flash scored 49.1%; GPT-5.5 scored 82%. GPT-5.5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 71.2%B 93.6%Winner: GPT-5.5Δ 22.4GPQA: DeepSeek V4 Flash scored 71.2%; GPT-5.5 scored 93.6%. GPT-5.5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 49.1%B 58.6%Winner: GPT-5.5Δ 9.5SWE-bench Pro: DeepSeek V4 Flash scored 49.1%; GPT-5.5 scored 58.6%. 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 | GPT-5.5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash$0.14 input / $0.28 output | GPT-5.5$5 input / $30 output | DeepSeek V4 Flash has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 FlashNot available | GPT-5.5Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 FlashNot available | GPT-5.5Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash1M | GPT-5.51M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGPT-5.5 wins25 benchmarks
| Benchmark | DeepSeek V4 Flash | GPT-5.5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 49.1% | 82% | GPT-5.5 leads |
| MCP AtlasSource | 64% | 75.3% | GPT-5.5 leads |
| ToolathlonSource | 40.7% | 55.6% | GPT-5.5 leads |
| Claw-EvalSource | 57.8% | — | Not comparable |
| Gert LabsSource | 54.35% | 72.93% | GPT-5.5 leads |
| CyberGymSource | — | 81.8% | Not comparable |
| BrowseCompSource | — | 84.4% | Not comparable |
| OSWorld-VerifiedSource | — | 78.7% | Not comparable |
| τ²-bench resultsSource | — | 93.9% | Not comparable |
| AA Agentic IndexSource | — | 44.9% | Not comparable |
| APEX-Agents-AASource | — | 37.7% | Not comparable |
| GDPval-AASource | — | 49.5% | Not comparable |
| GDPval-AASource | — | 1490 | 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 wins11 benchmarks
| Benchmark | DeepSeek V4 Flash | GPT-5.5 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.7% | — | Not comparable |
| SWE-bench ProSource | 49.1% | 58.6% | GPT-5.5 leads |
| SWE MultilingualSource | 69.7% | — | Not comparable |
| Terminal-Bench 2.0Source | 49.1% | 82.0% | 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 |
| AA Coding IndexSource | — | 74.9% | Not comparable |
| AA-SciCodeSource | — | 56.1% | Not comparable |
| FrontierCode 1.1 MainSource | — | 43.0% | Not comparable |
Reasoning7 benchmarks
KnowledgeGPT-5.5 wins13 benchmarks
| Benchmark | DeepSeek V4 Flash | GPT-5.5 | Result |
|---|---|---|---|
| MMLU-ProSource | 83% | — | Not comparable |
| SimpleQASource | 23.1% | — | Not comparable |
| Chinese-SimpleQASource | 71.5% | — | Not comparable |
| GPQASource | 71.2% | 93.6% | GPT-5.5 leads |
| GPQA-DSource | 71.2% | 93.6% | GPT-5.5 leads |
| HLESource | 8.1% | 52.2% | GPT-5.5 leads |
| HLE w/o toolsSource | — | 41.4% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 54.8% | Not comparable |
| AA-GPQA DiamondSource | — | 93.5% | Not comparable |
| AA-HLESource | — | 44.3% | Not comparable |
| AA-Omniscience IndexSource | — | 20.1% | Not comparable |
| AA-Omniscience AccuracySource | — | 56.9% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 85.5% | Not comparable |
MathGPT-5.5 wins7 benchmarks
| Benchmark | DeepSeek V4 Flash | GPT-5.5 | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 40.8% | — | Not comparable |
| IMOAnswerBenchSource | 41.9% | — | Not comparable |
| ApexSource | 1.0% | — | Not comparable |
| Apex ShortlistSource | 9.3% | — | 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 | GPT-5.5 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 75.9% | Not comparable |
Frequently Asked Questions (5)
Which is better, DeepSeek V4 Flash or GPT-5.5?
GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 58.88. The biggest single separator in this matchup is HLE, where the scores are 8.1% and 52.2%.
Which is better for knowledge tasks, DeepSeek V4 Flash or GPT-5.5?
GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 57.8 versus 38.8. Inside this category, HLE is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Flash or GPT-5.5?
DeepSeek V4 Flash has the edge for coding in this comparison, averaging 64.2 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 or GPT-5.5?
GPT-5.5 has the edge for math in this comparison, averaging 47.6 versus 40.8. DeepSeek V4 Flash stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, DeepSeek V4 Flash or GPT-5.5?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 49.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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